2016 ELECTRIC RESOURCE PLAN - Black Hills Energy ELECTRIC RESOURCE PLAN Colorado PUC E-Filings System. Attachment LS-1 2016 ERP i Table of Contents ... Table 4-9 Peak Demand and Energy

Attachment LS-1 2016 ERP

June 3, 2016

2016 ELECTRIC RESOURCE PLAN

Colo

rado

PUC

E-Fil

ings

Syst

em


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Table of Contents Executive Summary 1. Introduction ....................................................................................................................................... 1

1.1 Background ..............................................................................................................2 1.2 Overview of 2013 ERP ............................................................................................................ 3 2. Planning Environment .................................................................................................................. 5

2.1 Colorado Renewable Energy Standard ............................................................................ 5 2.2 Demand-Side Management .................................................................................................. 5 2.3 Economic Conditions .............................................................................................................. 6 2.4 Natural Gas Supply Trends over the Long-Term ......................................................... 6 2.5 Environmental Regulation .................................................................................................... 8 2.5.1 Greenhouse Gas Regulations........................................................................................ 9 2.5.2 Environmental Protection Agency’s Clean Power Plan ......................................... 9

2.6 Federal Tax Incentives for Wind and Solar Projects ............................................... 10 2.7 The Power Supply Market ................................................................................................. 11

3. Assumptions ...................................................................................................................................... 3.1 Planning Period ...................................................................................................................... 12 3.2 Resource Acquisition Period ............................................................................................. 12 3.3 Planning Reserve Margin ................................................................................................... 12 3.4 Fuel and Market Gas Prices ............................................................................................... 13 3.4.1 Natural Gas Prices .......................................................................................................... 13 3.4.2 Oil Prices ............................................................................................................................ 15 3.4.3 Economy Energy Prices ............................................................................................... 15 3.4.4 Seasonal Firm Market Purchase Prices .................................................................. 16 3.5 Financial Parameters ............................................................................................................ 16 3.6 Emissions Costs ....................................................................................................................... 17 3.7 Demand-Side Management ................................................................................................ 17 4. Load Forecast ................................................................................................................................. 21 4.1 Econometric Model Overview ........................................................................................... 21 4.2 Load, Economic, and Weather Data ................................................................................ 22 4.2.1 Historical Load Data ....................................................................................................... 22 4.2.2 Economic Data .................................................................................................................. 23 4.2.3 Weather Data .................................................................................................................... 23 4.2.4 Normal Weather Conditions ....................................................................................... 24 4.3 Forecast Methodology ....................................................................................................... 24 4.3.1 Peak Demand Forecast Methodology ................................................................... 24 4.3.2 Energy Forecast Methodology ................................................................................. 24 4.3.3 Large Customer Growth Assumptions ................................................................. 25 4.4 Base Peak Demand and Annual Energy Forecasts .................................................. 26 4.5 Low and High Forecasts...................................................................................................... 28 4.6 Historical Peak Demand and Annual Energy .............................................................. 31 4.7 Load Forecast Comparison-2013 ERP vs. Actual vs. 2016 ERP .......................... 32


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4.8 Energy Capacity Sales to other Utilities & Intra-Utility Energy and Capacity Sales and Losses .................................................................................................. 36 4.9 Load Profiles .................................................................................................................... 37

5. Supply-Side Resources......................................................................................................... 38 5.1 Existing Resources ......................................................................................................... 38 5.1.1 Diesels .......................................................................................................................... 40 5.1.2 Pueblo Airport Generating Station ................................................................... 40 5.1.3 Existing Purchases .................................................................................................. 40 5.1.4 Coordination Letters .............................................................................................. 41 5.1.5 Existing Renewables .............................................................................................. 41 5.1.6 Busch Ranch Wind Project ................................................................................... 41 5.1.7 Solar Resources ........................................................................................................ 41 5.1.8 1.8MW Distributed Generation Wind Facility .............................................. 42 5.1.9 Peak View Wind Project ........................................................................................ 42 5.2 Resources Options ......................................................................................................... 42 5.2.1 Conventional ............................................................................................................. 42 5.2.2 Combustion Turbine............................................................................................... 43 5.2.3 Combined Cycle ........................................................................................................ 43 5.2.4 Reciprocating Engines ........................................................................................... 44 5.2.5 Seasonal Firm Market Purchased Power ....................................................... 45 5.3 Renewables ....................................................................................................................... 45 5.3.1 PV Solar ....................................................................................................................... 45 5.3.2 Wind ............................................................................................................................. 46 5.4 Section 123 Resources ................................................................................................. 46 5.4.1 Sodium Sulfur Battery ........................................................................................... 47 5.4.2 Waste-to-Energy Facility ...................................................................................... 47

6.0 Costs and Benefits of Integration for Intermittent Renewable Energy Resources .................................................................................................................................. 48

6.1 Integration Capacity Needs and Costs .................................................................... 48 6.2 Accreditable Capacity of Wind and Solar .............................................................. 51 7.0 Transmission Resources ..................................................................................................... 53 7.1 Local Transmission Planning Process .................................................................... 54 7.2 Regional Transmission Planning Process ............................................................. 55 7.3 Transmission Constraints ........................................................................................... 56 7.4 Transmission Projects .................................................................................................. 59 7.5 Senate Bill 07-100 Transmission Projects ............................................................ 62 8.0 Future Resource Analysis and Selection....................................................................... 65 8.1 Resource Need ................................................................................................................. 65 8.2 Analysis .............................................................................................................................. 67 8.3 Base Plan Analysis and Alternative Plans ............................................................. 68 8.4 Base-with-RES Plan ....................................................................................................... 69 8.5 Alternative Plan 1 ........................................................................................................... 70 8.6 Alternative Plan 2 ........................................................................................................... 70 8.7 Retail Rate Impact Analysis ........................................................................................ 73 8.8 Scenario Analysis............................................................................................................ 75 8.9 Risk Analysis .................................................................................................................... 78


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8.9.1 Stochastic Analysis ................................................................................................... 78 8.9.2 Risk Profiles ................................................................................................................ 82 8.10 Preferred Plan ............................................................................................................... 85 8.11 2018-2021 RES Compliance Plan .......................................................................... 86 9.0 Contingency Plan ................................................................................................................... 87 10.0 RFPs and Model Contracts ................................................................................................. 87 11.0 Confidential and Highly Confidential Information ................................................... 88 11.1 Public Information 11.1.1 Company Information .......................................................................................... 88 11.1.2 Purchased Generation Resource Information ............................................ 88 11.1.3 Model Input Data .................................................................................................... 88 11.1.4 Modeling Output Data .......................................................................................... 89 12.0 Confidential Information .................................................................................................... 90 12.1 Modeling Input Data ................................................................................................... 90 12.2 Modeling Output Data ................................................................................................ 91 13.0 Highly Confidential Information ...................................................................................... 92 14.0 Information that the Company will Provide Bidders .............................................. 92 15.0 Implementation of Separation Policy ............................................................................ 92 16.0 Protection of Bid Information, Modeling Inputs and Assumptions, and Bid

Evaluation Results ................................................................................................................. 92 17.0 Water Usage ............................................................................................................................. 94


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List of Appendices (*Found Outside the Body of this Document)

A Estimated Net Incremental Cost Tables: *Schedule A-1: 2016-2040 Estimated Avoided Costs and Net Incremental

Cost of 2019 60 MW Wind Resource *Schedule A-2: 2016-2040 Estimated Avoided Costs and Net Incremental Cost of 2026 30 MW Wind Resource *Schedule A-3 - 2016-2040 Estimated Avoided Costs and Net Incremental Cost of 2038 60 MW Wind Resource *Schedule A-4: Source and Use of Funds Available for Eligible Energy Acquisition

B *WECC Standard BAL-002-WECC-2 Contingency Reserves C Econometric Load Forecast Methodology ................................................................... 98 *Schedule C-1 - Hourly Historic Demand (MW) *Schedule C-2 - Monthly Historical Class Energy Sales Data (MWh) *Confidential Schedule C-3 - Historical and Forecasted Economic Data *Schedule C-4 - Historical and Forecasted Weather Data

*Schedule C-5 - Historical and Forecasted Weather Data Used for Sales Models *Schedule C-6 - Historical and Forecasted Variable Values for the Demand

Model *Schedule C-7 - Variable Statistical Values for the Demand Model

*Schedule C-8 - Base Monthly Customer Class Sales Forecast & Demand Forecast

*Schedule C-9 - Base Annual Customer Class Sales Forecast & Demand Forecast

*Schedule C-10 - Historical and Forecasted Variable Values for Residential use per Customer Model

*Schedule C-11 - Variable Statistical Values for Residential use per Customer Model

*Schedule C-12 - Historical and Forecasted Variable Values for Residential Customer Model

*Schedule C-13 - Variable Statistical Values for Residential Customer Model *Schedule C-14 - Historical and Forecasted Variable Values for Commercial

use per Customer Model *Schedule C-15 - Variable Statistical Values for Commercial use per Customer

Model *Schedule C-16 - Historical and Forecasted Variable Values for Commercial

Customer Model *Schedule C-17 - Variable Statistical Values for Commercial Customer Model

*Schedule C-18 - Historical and Forecasted Variable Values for Industrial Sales Model

*Schedule C-19 - Variable Statistical Values for Industrial Sales Model D Load Profiles (Tables and Figures D-1 through D-36) .......................................... 103 E *Technology Characterization and Busbar Cost Analysis


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F *Black Hills VER Integration Study G *2016 – 2040 Load and Resource Balance H General Planning Assumptions ...................................................................................... 143 I Computer Models Used for Electric Resource Plan ................................................ 144 J Emissions Projections (Tables J-1 through J-10) .................................................... 146 K *Confidential Price Forecasts:

*Confidential Schedule K-1 -Natural Gas Price Forecast *Confidential Schedule K-2 -Fuel Oil Price Forecast *Confidential Schedule K-3 - Emission Costs - CO2 Scenario *Confidential Schedule K-4 - Seasonal Firm Market Price Forecasts-AZ PV Market-Base Plan *Confidential Schedule K-5 - Seasonal Firm Market Price Forecasts-AZ PV Market-High Gas Scenario *Confidential Schedule K-6 - Seasonal Firm Market Price Forecasts-AZ PV Market-Low Gas Scenario *Confidential Schedule K-7 - Seasonal Firm Market Price-AZ PV Market-CO2 Tax Scenario *Confidential Schedule K-8 - Market Clearing Price Forecasts- CO East Market- Base Plan *Confidential Schedule K-9 – Market Clearing Price Forecasts- AZ PV Market-Base Case *Confidential Schedule K-10 – Market Clearing Price Forecasts- CO East Market-CO2 Tax Scenario *Confidential Schedule K-11 – Market Clearing Price Forecasts- AZ PV Market- CO2 Tax Scenario *Confidential Schedule K-12 – Market Clearing Price Forecasts- CO East Market-High Gas Scenario *Confidential Schedule K-13 – Market Clearing Price Forecasts- AZ PV Market-High Gas Scenario *Confidential Schedule K-14 – Market Clearing Price Forecasts- CO East Market-Low Gas Scenario *Confidential Schedule K-15 – Market Clearing Price Forecasts – AZ PV Market-Low Gas Scenario *Confidential Schedule K-16 - Projected Emissions Rates for Existing Resources

L Confidential Exhibit Cost Parameters for Waste-to-Energy PPA Price .......... 158 M *Model Request for Proposal for Intermittent Resources N *Model Form Contract for Intermittent Resources O *Model Request for Proposal for Stand Alone Renewable Energy Credits Abbreviations ........................................................................................................................ 162


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List of Tables Page Table ES-1 Optimal Expansion Plans……………………………………………………………ES-5 Table 3-1 Average Annual Henry Hub Gas Price ............................................................. 14 Table 3-2 Economy Energy Purchase Capacity Allowed by Market Value ............ 15 Table 3-3 Financial Parameters ............................................................................................. 17 Table 3-4 2016-2018 Demand Side Management - ........................................................ 19 Table 3-5 Adjustments to Load Forecast for DSM .......................................................... 20 Table 4-1 Large Customer Load Additions and Reductions 2016- 2022 ............... 25 Table 4-2 2013 ERP Large Customer Load Assumptions Compared to Actual

Load Additions ......................................................................................................... 26 Table 4-3 Combined Transmission and Distribution Losses ...................................... 27 Table 4-4 Base Load Forecast ................................................................................................. 28 Table 4-5 Low, Base and High Load Forecasts ................................................................. 30 Table 4-6 Seasonal Peak Demand Load Forecast Comparison – Base, Low and

High (including impacts of DSM Plans) .......................................................... 31 Table 4-7 Historical Peak Demand and Annual Energy ................................................ 32 Table 4-8 Historical Peak Demand ....................................................................................... 33 Table 4-9 Peak Demand and Energy Forecast Comparison ........................................ 34 Table 4-10 Peak Demand Forecast Comparison ................................................................ 36 Table 5-1 Existing Generating Facilities ............................................................................. 39 Table 5-2 Combustion Turbine Parameters ...................................................................... 43 Table 5-3 Combined Cycle Parameters ............................................................................... 44 Table 5-4 Wartsila Gas Engine Parameters ....................................................................... 44 Table 5-5 PV Solar Performance Parameters ................................................................... 45 Table 5-6 Wind Performance Parameters ......................................................................... 46 Table 5-7 Sodium Sulfur Battery Performance Parameters ....................................... 47 Table 6-1 Future Eligible Energy Resources Modeled .................................................. 49 Table 6-2 Wind Integration Cost Assumptions ................................................................ 50 Table 6-3 Net Integration Costs ............................................................................................. 51 Table 6-4 Incremental Wind and Solar ELCC ................................................................... 53 Table 6-5 Cost for Integration and ELCC Summary ....................................................... 53 Table 7-1 Transfer Path Capacity .......................................................................................... 59 Table 7-2 Black Hills 2016 Rule 3206 Planned Transmission Projects ................. 60 Table 7-3 Black Hills 2015 SB-100 Transmission Projects ......................................... 64 Table 8-1 Load and Resource Balance (2016-2022) ..................................................... 66 Table 8-2 Expansion Plans ....................................................................................................... 71


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Table 8-3 Base-with-RES Plan Annual Capacity Factors during the Resource Acquisition Period ................................................................................................... 72

Table 8-4 Incremental Cost/Saving of Eligible Energy Resources ........................... 75 Table 8-5 Optimal Expansion Plans-Scenario Analysis ................................................ 77 Table 8-6 Short-Term Volatilities .......................................................................................... 79 Table 8-7 Mean Reversion Rates ........................................................................................... 80 Table 8-8 Load Correlations .................................................................................................... 80 Table 8-9 AZ-PV Market Price Correlations ...................................................................... 81 Table 8-10 CO-East Market Price Correlations ................................................................... 81 Table 8-11 Uncertainty Variable Range Multipliers ......................................................... 82 Table 16-1 Water Resources-Existing Generating Facilities ......................................... 94 Table 16-2 Water Resources-Potential Generating Facilities ....................................... 95 Table B-1 General Planning Assumptions .......................................................................... 96 Table D-1 Residential Daily Load Profiles (kW) – January ........................................ 104 Table D-2 Residential Daily Load Profiles (kW) – February ..................................... 105 Table D-3 Residential Daily Load Profiles (kW) – March ........................................... 106 Table D-4 Residential Daily Load Profiles (kW) – April .............................................. 107 Table D-5 Residential Daily Load Profiles (kW) – May ............................................... 187 Table D-6 Residential Daily Load Profiles (kW) – June ............................................... 109 Table D-7 Residential Daily Load Profiles (kW) – July ................................................ 110 Table D-8 Residential Daily Load Profiles (kW) – August .......................................... 111 Table D-9 Residential Daily Load Profiles (kW) – September .................................. 112 Table D-10 Residential Daily Load Profiles (kW) – October ........................................ 113 Table D-11 Residential Daily Load Profiles (kW) – November ................................... 114 Table D-12 Residential Daily Load Profiles (kW) – December ................................... 115 Table D-13 Commercial Daily Load Profiles (kW) – January ....................................... 116 Table D-14 Commercial Daily Load Profiles (kW) – February .................................... 117 Table D-15 Commercial Daily Load Profiles (kW) – March.......................................... 118 Table D-16 Commercial Daily Load Profiles (kW) – April ............................................ 119 Table D-17 Commercial Daily Load Profiles (kW) – May .............................................. 120 Table D-18 Commercial Daily Load Profiles (kW) – June ............................................. 121 Table D-19 Commercial Daily Load Profiles (kW) – July ............................................... 122 Table D-20 Commercial Daily Load Profiles (kW) – August ........................................ 123 Table D-21 Commercial Daily Load Profiles (kW) – September ................................ 124 Table D-22 Commercial Daily Load Profiles (kW) – October ...................................... 125 Table D-23 Commercial Daily Load Profiles (kW) – November ................................. 126 Table D-24 Commercial Daily Load Profiles (kW) – December .................................. 127 Table D-25 Industrial Daily Load Profiles (kW) – January ............................................ 128 Table D-26 Industrial Daily Load Profiles (kW) – February ......................................... 129 Table D-27 Industrial Daily Load Profiles (kW) – March ............................................... 130 Table D-28 Industrial Daily Load Profiles (kW) – April ................................................. 131 Table D-29 Industrial Daily Load Profiles (kW) – May ................................................... 132 Table D-30 Industrial Daily Load Profiles (kW) – June .................................................. 133


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Table D-31 Industrial Daily Load Profiles (kW) – July .................................................... 134 Table D-32 Industrial Daily Load Profiles (kW) – August ............................................. 135 Table D-33 Industrial Daily Load Profiles (kW) – September ..................................... 136 Table D-34 Industrial Daily Load Profiles (kW) – October ........................................... 137 Table D-35 Industrial Daily Load Profiles (kW) – November ...................................... 138 Table D-36 Industrial Daily Load Profiles (kW) – December ....................................... 139 Table J-1 Annual Projected SO2 Emissions from Existing Resources (Tons) .... 147 Table J-2 Annual Projected SO2 Emissions from Generic Resources (Tons) ..... 148 Table J-3 Annual Projected CO2 Emissions from Existing Resources (Tons) .... 149 Table J-4 Annual Projected CO2 Emissions from Generic Resources (Tons) ..... 150 Table J-5 Annual Projected NOx Emissions from Existing Resources (Tons) ... 151 Table J-6 Annual Projected NOx Emissions from Generic Resources (Tons) .... 152 Table J-7 Annual Projected PM Emissions from Existing Resources (Tons) ..... 153 Table J-8 Annual Projected PM Emissions from Generic Resources (Tons)...... 154 Table J-9 Annual Projected Mercury Emissions from Existing Resources (Tons)......................................................................................................................... 155 Table J-10 Annual Projected Mercury Emissions from Generic Resources (Tons)......................................................................................................................... 156


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List of Figures Page Figure ES-1 ERP Process ........................................................................................................... ES-2 Figure ES-2 2013 ERP and 2016 ERP Peak and Energy Comparison ...................... ES-3 Figure ES-3 Load and Resources Summary ....................................................................... ES-4 Figure ES-4 Base-with-RES Plan and Alternative Plans - Deterministic 25 Year

PVRR (2016-2040) ............................................................................................. ES-6 Figure ES-5 Scenarios – Deterministic PVRRs (2016-2040) ...................................... ES-9 Figure 1-1 Black Hills Service Territory .................................................................................. 2 Figure 2-1 U.S. Natural Gas Production, 1990-2040 (trillion cubic feet per year) . 7 Figure 2-2 EIA’s Different Scenarios for Generation by Fuel .......................................... 8 Figure 3-1 2009-2015 Energy Efficiency Savings ............................................................. 18 Figure 6-1 Monthly Regulating Reserve Requirement ................................................... 49 Figure 6-2 Calculated Wind and Solar ELCC ....................................................................... 52 Figure 7-1 Existing Black Hills Transmission System ..................................................... 54 Figure 7-2 Existing Black Hills Transfer Path Map ........................................................... 58 Figure 7-3 Planned and Conceptual Black Hills Transmission System .................... 62 Figure 8-1 Base-with-RES and Alternative Plans-Deterministic 25 PVRR (2016-

2040) .......................................................................................................................... 72 Figure 8-2 Base-with-RES Plan and Scenarios-PVRRs (2016-2040) ........................ 78 Figure 8-3 Risk Profiles-All Plans and Scenarios .............................................................. 84 Figure D-1 Residential Daily Load Profiles – January .................................................... 104 Figure D-2 Residential Daily Load Profiles – February ................................................. 105 Figure D-3 Residential Daily Load Profiles – March ....................................................... 106 Figure D-4 Residential Daily Load Profiles – April ......................................................... 107 Figure D-5 Residential Daily Load Profiles – May ........................................................... 108 Figure D-6 Residential Daily Load Profiles – June........................................................... 109 Figure D-7 Residential Daily Load Profiles – July ............................................................ 110 Figure D-8 Residential Daily Load Profiles – August ..................................................... 111 Figure D-9 Residential Daily Load Profiles – September ............................................. 112 Figure D-10 Residential Daily Load Profiles – October ................................................... 113 Figure D-11 Residential Daily Load Profiles – November .............................................. 114 Figure D-12 Residential Daily Load Profiles – December ............................................... 115 Figure D-13 Commercial Daily Load Profiles – January .................................................. 116 Figure D-14 Commercial Daily Load Profiles – February ............................................... 117 Figure D-15 Commercial Daily Load Profiles – March ..................................................... 118 Figure D-16 Commercial Daily Load Profiles – April ........................................................ 119


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Figure D-17 Commercial Daily Load Profiles – May ......................................................... 120 Figure D-18 Commercial Daily Load Profiles – June ......................................................... 121 Figure D-19 Commercial Daily Load Profiles – July .......................................................... 122 Figure D-20 Commercial Daily Load Profiles – August .................................................... 123 Figure D-21 Commercial Daily Load Profiles – September ............................................ 124 Figure D-22 Commercial Daily Load Profiles – October .................................................. 125 Figure D-23 Commercial Daily Load Profiles – November ............................................. 126 Figure D-24 Commercial Daily Load Profiles – December ............................................. 127 Figure D-25 Industrial Daily Load Profiles – January ....................................................... 128 Figure D-26 Industrial Daily Load Profiles – February .................................................... 129 Figure D-27 Industrial Daily Load Profiles – March.......................................................... 130 Figure D-28 Industrial Daily Load Profiles – April ............................................................ 131 Figure D-29 Industrial Daily Load Profiles – May .............................................................. 132 Figure D-30 Industrial Daily Load Profiles – June ............................................................. 133 Figure D-31 Industrial Daily Load Profiles – July ............................................................... 134 Figure D-32 Industrial Daily Load Profiles – August ........................................................ 135 Figure D-33 Industrial Daily Load Profiles – September ................................................ 136 Figure D-34 Industrial Daily Load Profiles – October ...................................................... 137 Figure D-35 Industrial Daily Load Profiles – November ................................................. 138 Figure D-36 Industrial Daily Load Profiles – December .................................................. 139


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Summary of Compliance with the Electric Resource Plan Reporting Requirements

(4 CODE OF COLORADO REGULATIONS (CCR) 723-3 - PART 3 RULES REGULATING ELECTRIC UTILITIES 4 CSR 240-22.050 (11))

3600 Electric Resource Planning

Rule Description Location in Report

3603 Resource Plan Filing Requirements

3603 (a) Plan required by October 31, 2011 This plan is being filed by June 3, 2016 in accordance with Decision No. C15-1071.

3603 (b) Motions for highly confidential information to be filed at the same time

Motions filed. Section 11.0.

3604 Contents of the Resource Plan

3604 (a) Identification of planning period and resource acquisition period

Sections 3.1 and 3.2

3604 (b) Forecast developed pursuant to rule 3606

Section 4.0

3604 (c) Evaluation of existing resources pursuant to rule 3607

Section 5.1

3604 (d) Evaluation of transmission resources pursuant to rule 3608

Section 7.0

3604 (e) Reserve margins and contingency plans pursuant to rule 3609

Section 3.3 – Reserve Margins. Section 9.0 – Contingency Plan

3604 (f) Assessment for need for additional resources pursuant to rule 3610

Section 8.0

3604 (g) Plan for acquiring needed resources pursuant to rule 3611 and emissions

Section 8.10

3604 (h) Water consumption Section 16.0

3604 (i) Proposed RFPs and model contracts Appendices M, N, O

3604 (j) Identification of confidential and highly confidential information

See motions filed. Section 11.0

3604 (k) Three alternate plans Section 8.0

3604 (l) Integration of intermittent renewable energy resources

Section 6.0

3606 Electric Energy and Demand Forecasts

3606 (a) (I) Coincident summer and winter peak demand and energy by jurisdiction

Section 4.5

3606 (a) (II) Coincident summer and winter peak demand and energy by customer class

Waiver requested for peak demand

3606 (a) (III) Sales to other utilities Section 4.8

3606 (a) (IV) Intra-utility capacity and energy use Section 4.8


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3606 (a) (V) System losses Waiver requested. Section 4.4

3606 (a) (VI) Typical day load patterns Appendix D

3606 (b) Base, low and high forecasts Section 4.0

3606 (c) (I) End-use, econometric or other method

Section 4.0, Appendix C

3606 (c) (II) Data by individual customers Not applicable

3606 (d) Data comparison – five years of historical data; last ERP

Section 4.7

3606 (e) Description and justification Section 4.0

3606 (f) Graphs; data available electronically Section 4.0, Appendix D

3607 Evaluation of Existing Resources

3607 (a) (I) Name and location Section 5.1

3607 (a) (II) Capacity Section 5.1

3607 (a) (III) Operational data Section 5.1

3607 (a) (IV) In-service dates Sections 5.1

3607 (a) (V) Remaining useful lives Section 5.1

3607 (a) (VI) Purchases Section 5.1

3607 (a) (VII) Wheeling and coordination agreements

Section 5.1

3607 (a) (VIII) Emissions data Appendix J and Confidential Schedule K-6, Appendix K

3607 (a) (IX) DSM Section 3.7

3607 (b) Coordination with other Colorado jurisdictional utilities

Section 5.1.4

3608 Transmission Resources

3608 (a) Report capabilities – 115 kV and above

Section 7.3

3608 (b) All transmission to be built during RAP

Section 7.4

3608 (c) (I) Length and location Section 7.4

3608 (c) (II) Estimated in-service date Section 7.4

3608 (c) (III) Injection capacity Section 7.5

3608 (c) (IV) Estimated costs Section 7.4

3608 (c) (V) Terminal points Section 7.4

3608 (c) (VI) Voltage and MW rating Section 7.4

3608 (d) Transmission costs and benefits included in bid evaluation criteria

Appendix (RFP)

3608 (e) Costs for transmission required for facilities not acquired through a bid process

Not Applicable

3609 Planning Reserve Margins and Contingency Plan

3609 (a) Means of assessing reliability Section 3.3


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3609 (b) Reserve margins for base, load and high forecast and associated risks

Section 3.3, Appendix B

3609 (c) Contingency plans Section 9.0

3610 Assessment of Need for Additional Resources

3610 (a) Determine need for additional resources

Section 8.0

3610 (b) (I) Additional renewable resources required to comply with RES

Section 8.0

3610 (b) (II) Additional DSM Section 8.0

3610 (c) Consideration of carbon dioxide with new resources

Not applicable Waiver requested.

3611 Utility Plan for Meeting Need

3611 (a) – (h) Competitive acquisition for new resources unless waiver or other arrangements

Section 8.10

3612 Independent Evaluator

3612 (a) – (f) Selection and approval of independent evaluator

Waiver Requested. IE approval has been requested in the Application.

3616 Request(s) for Proposals

3616 (a) – (f) RFP and model contracts included with ERP filing

Appendix M, N, O


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Executive Summary

Introduction

This Electric Resource Plan (“2016 ERP”) is being filed by Black Hills Colorado Electric (“Black Hills” or “the Company”) in compliance with the Rules of the Colorado Public Utilities Commission (“Commission”) and its ruling in Proceeding No.(“Planning Period”) 15V-0622E that Black Hills file this ERP on or before June 3, 2016. It covers a Planning Period of twenty-five years from January 2016 through December 2040. The Resource Acquisition Period (“RAP”) covers the seven-year period January 2016 through December 2022. The analysis conducted for this 2016 ERP used industry-accepted methods to determine the capacity necessary for expected future load growth and to determine the cost to add Eligible energy resources sufficient to comply with the Renewable Energy Standard (“RES”) established by C.R.S. § 40-2-124 et seq. (“the RES Statute”) and implemented by Commission Rules 4 CCR 723-3-3650 et seq. (“the RES Rules”). Black Hills’ 2016 ERP was developed by progressing through five fundamental planning steps. The first element of the planning process is to prepare a load forecast and load and resource balance to quantify resource need over time. In the second planning step, the Company developed different resource portfolios that meet the projected resource needs based on varying key planning assumptions. In the third step, Black Hills performed comparative cost and risk analysis among the different resource portfolio alternatives. This cost and risk analysis informs the selection of a preferred portfolio which is the fourth step. Fifth and finally, the Company selects the associated resource action plan. Throughout this process, the Company assessed the current planning environment to develop key planning assumptions and identify planning uncertainties. A supplemental study was also completed to support the derivation of specific modeling assumptions.

Figure ES-1 ERP Process

Resource Needs Assessment

Resource Portfolio Development

Cost and Risk Analysis

Preferred Portfolio Selection

Action Plan

Key Planning Assumptions Supplemental Study


3

Load Forecast

The load forecast completed for the 2016 ERP indicates that growth in the Black Hills’ service territory over the Planning Period will be slow. In addition, the effects of the Company’s 2016-2018 Demand Side Management Plan (“2016-2018 DSM Plan”) will reduce future retail system peak demand (MW) and retail energy sales (MWh). The Company is aware of some known load additions and load losses, based on discussions with existing large-demand customers and these load additions and losses were incorporated into the load forecast. The Company used an econometric forecasting methodology to forecast peak demand and energy for the 2016 ERP. Black Hills gathered and refined a variety of different types of datasets including historical load, revenue, economic, and weather data. This data was used to develop regression models for the monthly system-level peak demand forecast and the major customer class energy forecasts. The final system-level monthly peak demand was computed by adding large customer loads, including their anticipated future load growth, and accounting for the effects of demand side management programs (“DSM”) to the load forecast produced from the econometric methods. The final system-level major customer class energy forecasts were computed by adding large customer loads, including their anticipated future load growth, losses, and accounting for the effects of the 2016-2018 DSM Plan to the energy forecasts calculated through the regression analysis. In addition, high and low load forecasts were developed to allow the Company to assess risk associated with uncertainty related to load growth. The forecast completed by the Company for the 2016 ERP indicates that peak demand growth will be lower than was forecasted in the 2013 ERP but that energy consumption will be similar to what was indicated in the 2013 ERP load forecast. Detailed information related to the load forecast is found in Section 4 of this report.

Figure ES-2 2013 ERP and 2016 ERP Peak and Energy Forecast Comparison

200

400

600

Forecasted Annual System Peak Demand (MW)

2013 ERP 2016 ERP

1,750.0

2,250.0

Forecasted Annual System Energy Usage (GWh)

2013 ERP 2016 ERP


4

Using the results of the load forecast analysis the Company prepared a load and resource balance that shows that Black Hills will have sufficient capacity resources to serve customer electricity demand including a 15 percent reserve margin beyond the 2016-2022 RAP. Figure ES-3 shows a summary of the Company’s existing resources and the resources that are currently under construction compared to the Company’s peak load forecast.

Figure ES-3 Load and Resources Summary

Resource Portfolio Evaluation

Capacity expansion and production cost modeling were conducted using an array of conventional and renewable resources to develop resource portfolios to meet the resource needs of the Company during the RAP and throughout the Planning Period. These methods also allow the Company to assess the costs and benefits of various types of resources. Three alternative plans (Base-with-RES Plan, Alternative Plan1 and Alternative Plan 2) were constructed to describe the costs and benefits of the new utility resources required to meet the utility’s needs during the Planning Period and to meet the requirements of the Commission’s Rules. The Base-with-RES Plan model incorporates into the future resource portfolio all of the additional renewable energy resources that would be required to achieve the RES requirements during the RAP and the Planning Period. The Alternative Plans represent the costs and benefits associated with increasing amounts of renewable energy resources or Section 123 resources. The Base-with-RES Plan adds 60 MW of wind resources in 2019, 30 MW of wind resources in 2026, and 60 MW of wind resources in 2038. The optimal expansion plans for the Base-with-RES Plan, Alternative Plan 1, and the Alternative Plan 2 plans are shown in Table ES-1.

0

100

200

300

400

500

600

MW

Peak Demand Plus 15%

Existing Resources


5

Table ES-1 Optimal Expansion Plans

(Table only shows years where resource additions are identified, does not show seasonal firm market purchases)

Year Base-with-RES Alternative Plan 1

Alternative Plan 2

2019 60 MW Wind 60 MW Wind 60 MW Wind


2032 (2) LMS100 (2) LMS100 (2) LMS100

2035 (2) 10 MW Solar (2) 10 MW Solar


2039 Sodium Sulfur Battery 10 MW

2040 (2) Sodium Sulfur Battery 10 MW

The deterministic present values of revenue requirements (“PVRR”) for the Base-with-RES Plan, Alternative Plan 1 and the Alternative Plan 2 plans are shown in Figure ES-4.


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Figure ES-4

Base-with-RES and Alternative Plans Deterministic 25 Year PVRR (2016-2040)

When recommending the addition of Eligible energy resources, the Company must consider the net retail rate impact of this plan under the RES Rules. Rule 3661(a) provides that “the net retail rate impact of actions taken by an investor owned qualifying retail utility (“QRU”) to comply with the renewable energy standard shall not exceed two percent of the total electric bill annually for each customer of that QRU.”1 To determine the net incremental cost or savings of Eligible energy resources, the Company compared two scenarios to estimate the cost and benefits of each system over the ten-year RES planning period (“RES Planning Period”). The first scenario is a “RES plan” that reflects the utility’s plans and actions to acquire new Eligible energy resources necessary to meet the RES. The second scenario is a “No-RES plan” which reflects the utility’s resource plan that replaces the new Eligible energy resources in the RES plan with new non-renewable resources reasonably available.

1Rule 3661(a) require the Company to evaluate RES Compliance over defined period of time which for Black Hills will be 10 years.

$2,059.8 $2,060.2 $2,065.0

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Net incremental cost is determined over a ten-year RES Planning Period and is the calculated difference between the RES and No-RES over that period. See Rule 3661(f). The portfolios were compared through computer modeling so that the benefits associated with the addition of the wind resources selected in the Base-with-RES Plan could be captured. Those benefits are the avoided costs of fossil fuel expense, purchased power expense, and variable O&M production expense.2 The RES/No-RES Comparison showed that the avoided costs of the 60 MW wind resource in 2019, 30 MW wind resource in 2026, and 60 MW wind resource in 2038 exceeded the resource costs of the same renewable resources over the ERP Planning Period and therefore complied with the two percent retail rate impact. Over the ten-year RES compliance period (2018-2027) identified in the Company’s 2018-2021 RES Plan the modeling shows that the 60 MW wind resource would provide approximately $74 million of avoided cost savings from 2019 through 2027. The resource cost of the 60 MW of wind over the same time period, 2019 through 2027, is projected to be $76 million. Thus, the addition of 60 MW of wind energy in 2019 would result in a net incremental cost of approximately $2 million over the ten-year RES Planning Period. Over the Planning Period (2016-2040), the modeling shows that the 60 MW wind resource in 2019 would provide approximately $305 million of avoided cost savings from 2019 through 2040. The resource cost of the 60 MW of wind resource over the same time period, 2019 through 2040, is projected to be $235.6 million. Thus, the addition of 60 MW of wind energy in 2019 would result in a net incremental savings of approximately $69.3 million over the 25-year 2016 ERP Planning Period. The Base-with-RES Plan also included a 30 MW wind resource in 2026 and a 60 MW wind resource in 2038 for compliance with the RES. Over the Planning Period, the modeling shows that the 30 MW wind resource in 2026 would provide approximately $128 million of avoided cost savings from 2026 through 2040. The resource cost of the 30 MW of wind resource in 2026 over the same time period, 2026 through 2040, is projected to be $157 million. Thus, the addition of 30 MW of wind energy in 2026 would result in a net incremental cost of approximately $29 million over the time period 2026 through 2040. For the 60 MW wind resource in 2038, the modeling forecasts avoided cost savings of approximately $70 million from 2038 through 2040. The resource cost of the 60 MW of wind resource in 2038 over the same time period, 2038 through 2040, is projected to be $63 million. Thus, the addition of 60 MW of wind energy in 2038 would result in a net incremental savings of approximately $7 million over the time period 2038 through 2040. Schedules A-1, A-2, and A-3 in Appendix A shows the avoided cost calculations associated with the proposed Eligible energy resources in 2019, 2026, and 2038

2 When the wind turbines are generating, the Company does not have to run a conventional unit or purchase capacity and these are the avoided costs of the wind.


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Over the 25-year 2016 ERP Planning Period, the retail rate impact of the addition of the 60 MW wind resource in 2019, 30 MW wind resource in 2026, and the 60 MW wind resource in 2038 is shown in Schedule A-4 in Appendix A. The Company forecasts that the RESA will have a positive balance by 2020 if the Company meets all of its existing and authorized REC obligations and the Commission approves the Company’s proposed 2018-2021 solar programs and the acquisition of up to 60 MW of Eligible energy resources in 2019 through a solicitation process. Schedule A-4 also shows that over the course of the Planning Period the Company will have sufficient RESA funds to acquire Eligible energy resources in 2026 and 2038. These wind resource additions would not increase the RESA negative balance. The Base-with-RES Plan that adds 60 MW of new Eligible energy resources in 2019, 30 MW new Eligible energy resources in 2026, and 60 MW of new Eligible energy resources in 2038 complies with the RES.

Scenario Analysis

Scenario analysis was conducted using the Capacity Expansion module to derive optimal resource expansion plans under potential future conditions. The Base-with-RES plan represents expected future conditions while scenario analysis is useful for considering uncertainties that may impact decision-making in today’s world based on a variety of potential future conditions. The scenarios include variations in inputs representing the significant sources of portfolio cost variability and risk. The scenarios evaluated by the Company include an Environmental, High Load, Low Load, High Gas Price, Low Gas Price, and NYMEX Gas Price scenario. The deterministic PVRRs associated with these scenarios are shown in Figure ES-5.


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Figure ES-5

Scenarios – Deterministic PVRRs (2016-2040)

Utilities must plan for future customer needs for electricity in an environment of significant uncertainty. Thus, the analysis conducted for this 2016 ERP examined resource needs under a variety of possible future conditions. Stochastic analysis and risk profile compilation were among the risk techniques examined. A wide range of uncertainties in demand, electric prices, fuel prices, supply and costs was examined.

Preferred Plan

As a result of its evaluations, Black Hills’ Preferred Plan does not include the addition of any new capacity resources during the RAP. However, based on the bids that were received in the Company’s 2014 All-Source Solicitation, the Preferred Plan does include the addition of 60 MW of wind resources as energy resources in 2019. The Company used bid data submitted in the 2014 All-Source Solicitation as inputs in its modeling and based on the bid prices, the forecasted cost of natural gas, and the forecasted electric prices the model identified a 60 MW wind resource in 2019 as an economical option for energy.

2,059.8 2,059.8 2,062.9 2,053.2

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Without additional Eligible energy resources, the Company will not be able to meet the 30 percent RES requirement in 2020. Black Hills will be able to provide all of the retail and wholesale distributed generation (“DG”) required by the RES Statute and RES Rules through 2025. Retail DG renewable energy credits (“REC”s) will be provided by the resources in the Company’s solar programs and wholesale DG RECs will be provided by the 29 MW Busch Ranch Wind Project located in Huerfano County. The Peak View Wind Project and carry-forward RECs will provide sufficient RECs for compliance with the RES through 2019. However, in 2020, when the RES requirement increases to 30 percent of retail sales Black Hills’ existing Eligible energy resources will not be able to generate enough RECs to meet this standard. Beyond the RAP, Black Hills’ load and resource balance shows a small capacity deficit in 2029, growing by a few megawatts each year until the beginning of 2032 when the Company’s contract for 200 MW of generation expires. The modeling for the 2016 ERP identified an optimal portfolio to replace this expiring contract, however, based on current Colorado Commission rules, the Company will be required to complete at least two electric resource plans prior to considering the appropriate replacement capacity for this contract. Black Hills’ Action Plan to implement the Company’s Preferred Plan recommends that the Company engage in a Phase II competitive solicitation to acquire up to 60 MW of Eligible energy resources by 2019. This solicitation will allow the Company to determine if Eligible energy resources can be acquired at a cost that will provide savings for customers and generate sufficient RECs such that Black Hills will comply with the RES through 2025.

RFPs and Model Contracts

The Intermittent model Request for Proposal (“RFP”) and model contracts required as part of this 2016 ERP are provided in Appendix M, N and O. (Waiver for other contracts and RFPs)


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1.0 Introduction The Company is filing its 2016 ERP pursuant to the Electric Resource Planning Rules, 4 CCR 723-3-3600 et seq. (“ERP Rules”). The resource planning process in Colorado typically involves two phases. The first step, otherwise known as Phase I, includes development of a load forecast, evaluation of the utility’s current resources (including transmission), determination of need for additional resources, and the utility’s proposed plan for acquiring the resources to meet the identified need. The Company’s 2016 ERP covers a seven-year RAP and a 25-year Planning Period, conforming to the ERP Rules. Prudent utility practices were employed in the preparation of the 2016 ERP. The Company completed a load forecast and load and resource balance to determine resource need, capacity expansion modeling to determine the optimum resource portfolio, production cost modeling to evaluate cost, and stochastic analysis to evaluate the risk of each portfolio under a range of varying conditions. The ERP Rules require that the Company include the descriptions of at least three alternative plans that can be used to represent the cost and benefits of the resources necessary to meet the resource need in its ERP. One of the three plans must represent a baseline case that complies with the RES, as well as with the demand-side resource requirements under C.R.S. § 40-3.2-104. The other two plans must include alternative combinations of resources that meet the same resource needs as the baseline case but that include proportionately more renewable energy resources, demand-side resources, or Section 123 resources. Following the Commission’s Phase I decision, the utility proceeds to implement that decision.

Background 1.0 Black Hills provides electric service to several communities in Colorado, serving more than 95,000 customers in 21 communities. In 2015, the Company sold more than 1,959 GWh. In June 2012, the system peak was set at 400 MW, the highest load level recorded by the Company to date. The largest communities served include Pueblo, Cañon City, and Rocky Ford. The Company’s generating stations are located in Pueblo and Rocky Ford. The Company’s service territory, which encompasses parts of Crowley, Custer, El Paso, Fremont, Otero, Pueblo, and Teller counties, is shown in Figure 1-1.


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Figure 1-1 Black Hills Service Territory

The Company currently meets electric demand and energy needs using its own generation resources, through purchased power contracts, and via purchases from the open market. The Company’s portfolio includes:

• Pueblo Airport Generating Station (“PAGS”) natural gas-fired combustion turbines with a total capacity of 180 MW;

• Three diesel stations with a total net capacity of 30 MW; o Rocky Ford diesels units are located in Rocky Ford, CO providing 10

MW of net capacity; o Pueblo diesels, with a total of 10 MW of net capacity located in Pueblo, o Airport diesels, with a total of 10 MW of net capacity located in

Pueblo;

• Black Hills Colorado IPP PPA expiring at the end of 2031, which provides 200 MW of power from Black Hills Colorado IPP’s combined-cycle turbines;

• An agreement with the Western Area Power Administration (“WAPA”) between the western and eastern transmission grids to facilitate the delivery of 5 MW of capacity and energy, extending through September 30, 2024;


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• The Busch Ranch Wind Project3 in Huerfano County, which entered commercial operation in 2012, consisting of sixteen 1.8 MW wind turbines;

• A short-term PPA expiring December 31, 2016, whereby Black Hills purchases 50 MW of firm energy.

The Company currently has two generating facilities under-construction:

• A 40 MW natural gas-fired combustion turbine at PAGS that was approved in Proceeding No. 13A-0445E; and

• Peak View Wind Project, a 60 MW wind resource that will be located in Huerfano County and Las Animas County, Colorado. The project was approved in Proceeding No. 15A-0502E.

The Company’s power delivery system consists of approximately 585 miles of transmission lines and 3,100 miles of distribution lines.

Overview of 2013 ERP 1.1 The Company last filed an ERP with the Colorado Public Utilities Commission on April 30, 2013. The Company’s Preferred Plan in the 2013 ERP relied on a two-year power purchase agreement (“PPA”) beginning in 2015, a 40 MW LM6000 as replacement capacity for the Clark Station, a 74 MW GE 7EA in 2017, and seasonal firm market power in several years. Based on the Preferred Plan recommendations Black Hills filed concurrently with the 2013 ERP:

• A request for a Certificate of Public Convenience and Necessity (“CPCN”) to construct the 40 MW LM6000;

• A CPCN to retire Pueblo 5 and 6 at the end of 2013; and

• An agreement with Cargill for a two year (2015–2016) 50 MW purchase power agreement.

On December 17, 2013, in Decision C14-0007, the Commission approved the Company’s 2013 ERP and the 2013-2014 RES Compliance Plan with modifications. In the same decision, the Commission granted the Company a CPCN for the LM6000 and a CPCN for the retirement of the Pueblo Units 5 and 6. In addition, the Commission authorized Black Hills to conduct a Phase II All-Source Solicitation for utility resources to meet the 42 MW resource4 need in 2017 and for up to 60 MW of Eligible energy resources in 2017 or 2018.

3 The Company owns half of the 29 MW and purchases the energy produced by the remaining turbines under a PPA that has a 25-year term. 4 One of the stipulations of the 2013 ERP Settlement Agreement included revising the Company’s load forecast to reflect the anticipated demand and energy savings included in the Commission-approved 2012 DSM Settlement Agreement in Proceeding No. 12A-100E. In addition, the Company agreed to recognize two interruptible customer contracts as resources. These revisions to the Company’s load forecast and resources resulted in a lower resource need of 42 MW in 2017, 29 MW in 2018, and 33 MW in 2019 effectively eliminating the need for the 74 MW GE 7EA in 2017.


4

As a result of Decision C14-0007, the following significant resource changes have occurred or have been agreed to:

• Pueblo Units 5 and 6 were retired on December 31, 2013;

• The Company issued an All-Source Solicitation for up to 60 MW of Eligible energy resources and a seasonal capacity need in 2017, 2018 and 2019 (approximately 42 MW, 29 MW and 33 MW respectively) on May 1, 2014; and

• The 40 MW LM6000 at PAGS is under construction and is expected to begin commercial operation on December 31, 2016.

In Commission Decision C15-1182 the Commission approved a CPCN for the 60 MW Peak View Wind Resource.


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2 Planning Environment The environment in which utilities must plan their future resources continues to evolve. Significant issues and concerns facing the Company during this 2016 ERP planning cycle include:

• Colorado Renewable Energy Standard;

• Plans and programs for demand-side management;

• Economic conditions globally and in Colorado;

• Natural gas supply and pricing over the long-term;

• Environmental regulation;

• Federal production tax incentives for wind and solar project;

• Power supply markets in the state and regionally. Each of these topics is described in this section of the 2016 ERP.

Colorado Renewable Energy Standard 2.0 The RES Rules require the Company to provide specific percentages of renewable energy and/or recycled energy according to the following schedule:

• 20% of its retail electricity sales in Colorado for the years 2015-2019; and

• 30% of its retail electricity sales in Colorado for the year 2020 and for each following year.

Black Hills must have a certain percentage of its retail sales produced by either wholesale DG or retail DG, regardless of technology type, according to the following schedule:

• 1.75% of its retail electricity sales in 2015 and 2016;

• 2% of its retail electricity sales in 2017-2019, and

• 3% of its retail electricity sales in 2020 and each following year. At least one-half of the DG requirement must be generated by retail DG systems located on-site at customers’ facilities or premises.

Demand-Side Management 2.1 The Company will comply with the mandate of C.R.S. § 40-3.2-104 to reduce its retail system peak demand and retail energy sales through implementation of DSM plans. In that statute, DSM is defined as one of, or any combination of, the following measures: energy efficiency, conservation, load management, and demand response programs. The required goals to be achieved by 2018 are a reduction in retail system peak demand (MW) equal to 5 percent of the 2006 level (304 MW without


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losses) and a reduction of retail energy sales (MWh) equal to 5 percent of the 2006 level (1,839 GWh without losses).

Economic Conditions 2.2 Since the Company filed its 2013 ERP, economies around the globe and the customers in the Company’s service territory, have continued to experience a weak recovery from the recession that began in late 2007. According to the “Economic and Revenue Forecast” published by the Colorado Legislative Council Staff Economics Section, activity in Colorado and the nation is expected to continue to expand in 2016 and 2017, though at a slower pace than in the prior two years. The weak global economy constrained domestic growth in the second half of 2015. Low commodity prices and a strong dollar have hurt the agriculture, natural resources, and manufacturing sectors, dampening momentum in other sectors of the economy. 5 The national unemployment rate was 4.9 percent in February 2016, the lowest rate since February 2008 and Colorado businesses also continued to add jobs in 2015. However, economic activity in the southern mountains region of Colorado, which consists of Pueblo, Fremont, Custer, Huerfano, and Las Animas counties, was tepid in 2015. Overall, regional employment was flat in 2015 over the prior year. The area unemployment rate fell due in large part to a contracting labor force. Retail sales rose slightly, and construction activity remained at low levels. The recession and slow recovery have dampened the current and projected demand for electricity.

Natural Gas Supply Trends Over the Long-Term 2.3 Shale gas technology increased the amount of recoverable North American gas supply, enabling producers to access vast supplies of shale gas. New technology that provided lower cost access to shale gas caused total U.S. production to grow substantially from 2005 to 2015. Figure 2-1 below shows the historical and future production forecasts for shale gas relative to other production methods.

5 “Focus Colorado: Economic and Revenue Forecast Colorado Legislative Council Staff Economics Section,” Colorado General Assembly Legislative Council Staff, https://www.colorado.gov/pacific/sites/default/files/MarchForecast.pdf.

https://www.colorado.gov/pacific/sites/default/files/MarchForecast.pdf


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Figure 2-1

U.S. Natural Gas Production, 1990-2040 (trillion cubic feet per year)

As a result of EPA regulation to limit emissions from stationary sources, many generation owners will close old, inefficient coal plants as opposed to investing new capital to comply with environmental regulation. Natural gas is the lowest-cost resource that will comply with these environmental regulations, while offering operational flexibility, large scale capacity, and grid stability. Therefore, most electric generation forecasts reflect continued closure of coal plants and new capacity additions in gas generation and some in renewable energy. Over the next few years, natural gas generation is expected to decrease as a result of generation owners taking advantage of the production tax credit and investment tax credit available to renewable generation investments. However, that trend is expected to reverse and natural gas should overtake coal as the leading generation fuel by the mid- to late-2020s. The figure below illustrates the growth in natural gas electric generation forecasted by the U.S. Department of Energy’s Energy Information Administration (“EIA”), published in its 2016 Annual Energy Outlook – Early Release (“AEO2016 – Early Release”) for its reference case including implementation of the Clean Power Plan and a case without implementation.


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Figure 2-2 – EIA's Different Scenarios for Generation by Fuel6

The AEO2016 Early Release compares electric generation capacity both with and without implementation of the Clean Power Plan. Implementation of the Clean Power Plan is likely to result in natural gas further replacing coal generation as a means of compliance.

Environmental Regulation 2.4

The Environmental Protection Agency (“EPA”) has been very active since 2009 with regard to regulations for utility-scale power plants, particularly coal-fired power plants. Because the Company no longer has any coal-fired generation as of January 1, 2014, it will not be impacted by the Regional Haze Rule. Similarly, since the Company does not own any oil-fired generating units and its coal-fired generation was retired at the end of 2013, it is not affected by the EPA’s Mercury and Air Toxics Standard (“MACT”), which was finalized on February 16, 2012. Therefore, the focus of this section on environmental regulation is greenhouse gases.

6 Energy Information Administration, Annual Energy Outlook 2015 With Projections to 2040, DOE/EIA-0383(2015), April 2015, 28.


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Greenhouse Gas Regulation 2.4.1

Since January 2, 2011, the EPA has been regulating greenhouse gas (“GHG”) emissions from the largest stationary sources through the Prevention of Significant Deterioration (“PSD”) and Title V Operating Permit Programs. GHGs are comprised of six gases: carbon dioxide, nitrous oxide, methane, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride.7 Under the Greenhouse Gas Tailoring Rule, these gases are regulated as pollutants under the major source permitting requirements. New or modified electric generation resources with GHG emissions above certain levels must go through a technology-based, source-by-source review process to demonstrate that they will use the Best Available Control Technology (“BACT”) to control GHG emissions. This process is required before the resource can receive a Clean Air Act permit. On April 13, 2012, the EPA proposed GHG New Source Performance Standards for new steam electric power plants. EPA expects that new combined cycle natural gas-fired generation will meet the new standard for GHG and that new coal-fired plants will not. As of the end of 2011, the Company installed two utility-owned natural gas-fired units at PAGS and contracted for power purchased from two 2x1 combined cycle natural gas-fired units also located at PAGS.

Environmental Protection Agency’s Clean Power Plan (“CPP”) 2.4.2 The EPA proposed the Clean Power Plan (“CPP”) on June 2, 2014. The final CPP rule was issued on August 3, 2015, published in the Federal Register on October 23, 2015 and became effective on December 22, 2015. The final CPP set specific CO2 emission reduction goals for State’s and a national CO2 emission reduction goal that equates to a 32 percent reduction by 2030, using 2005 emission levels as a baseline. The Clean Power Plan provides specific rate and mass goals for each State. The rule contains guidelines for the development, submittal, and implementation of State Implementation Plans (“SIP”). In addition, the EPA is drafting Federal Implementation Plans (“FIP”) using rate and mass compliance approaches. States must submit their SIP or request a two-year extension by September 6, 2016. EPA has specified detailed actions which must be undertaken in order for a State to qualify for an extension. If an extension is granted, States will have until September

7 Clean Air Act Permitting for Greenhouse Gases, U.S. Environmental Protection Agency, http://www.epa.gov/nsr/ghgpermitting.html.

http://www.epa.gov/nsr/ghgpermitting.html


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6, 2018 to complete their SIP. If a state chooses to not complete a SIP it will be subject to the requirements of EPA’S FIP. The compliance period for the rule begins in 2022 with partial compliance requirements during the 2022-2029 interim period and full compliance by 2030. The rule establishes three interim “steps” or time periods (2022-2024, 2025-2027, 2028-2029), which forms a “glide path” in which emissions performance increases as the final compliance date approaches. States may elect to set their own goals for the three interim periods as long as they meet their “interim period” goals. States can also choose to participate in the Clean Energy Incentive Program (“CEIP”): a program which seeks to reward early investments in renewable energy and energy efficiency measures that generate carbon-free electricity or reduce end-use energy demand during 2020 and/or 2021. However, projects must meet certain criteria and construction timing. On February 9, 2016, the U.S. Supreme Court issued an emergency order “staying” the CPP while litigation proceeds on the legal merits of the rule. No deadlines or other compliance obligations may be enforced while the stay is in effect. The US D.C. Circuit Court of Appeals has been tasked to review the case and is scheduled to begin hearing oral arguments on June 2, 2016. The D.C. court is expected to make a final ruling on the case at the end of 2016 or the beginning of 2017. Depending on the circuit court’s decision, either the CPP opponents or EPA could petition the Supreme Court to hear the case which may affect State Implementation Plan submittal dates and the initial compliance date of 2022.

Federal Tax Incentives for Wind and Solar Projects 2.5 The Energy Policy Act of 1992 originally enacted the Production Tax Credit which has been the primary production based incentive for wind energy and has been essential to the industry’s research and development. The original PTC legislation expired in July 1999 but has been expanded and extended several times through many different laws. In late 2015, Congress provided a 5-year PTC with provisions to phase out the PTC by 2020 at a rate of 20 percent per year. The Consolidated Appropriations Act, 2016 extended the expiration date for this tax credit to December 31, 2019. The value of the PTC is based on the year that construction begins rather than when the facility begins production. The phase-down begins for wind projects commencing construction after December 31, 2016. The Act extended the tax credit for other eligible renewable energy technologies commencing construction through December 31, 2016. The Act applies retroactively to January 1, 2015. Legislation also extended the Solar Investment Tax Credit (“ITC”). This legislation, which was signed into law on December 18, 2015, extends the 30 percent ITC for both residential and commercial projects through the end of 2019. Previous to the 2015 extension, the 30 percent ITC was available to such facilities placed in service on or before December 31, 2016. Changing from a deadline based solely on placed


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in-service to one focused on commencement of construction was intended to provide facility developers with greater certainty. Like the PTC legislation, provisions to lower the credits over time were included in the bill. The new legislation decreases the credit to 26 percent for projects that begin construction in 2020, and 22 percent for projects that begin construction in 2021 before dropping permanently to ten percent for commercial projects and zero percent for residential projects. Projects must be placed in service before the end of 2023 to qualify for applicable credit based on the year construction began. Projects placed into service after 2023 will only qualify for the ten percent ITC.

The Power Supply Market 2.6 The availability and price of power through the economy and spot markets is an important factor in how the Company operates and plans its generation. These transactions yield economic efficiency by assuring that resources with the lowest operating cost are serving demand in a region and by providing reliability benefits that arise from a larger portfolio of resources. Electric prices in the WECC region have been low for the past several months and low natural gas prices, adequate capacity supplies and moderate load growth will likely keep prices at a low level going forward. Some tightening of these markets is assumed through the Planning Period and in the later years of the RAP. Electricity markets are also faced with demand uncertainties driven by weather, overall economic conditions, and resource supply availability. WECC publishes an annual assessment of power supply that tracks the status of new resource additions in the WECC region. The latest assessment, published in September 2014, indicates that even when including only existing and under-construction units, WECC as a whole, has ample resources through 2024, and the WECC subregion in which Black Hills operates, the Rocky Mountain Reserve Group, has a surplus of capacity through 2021.


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3 Assumptions Many data assumptions were required to complete the load forecast and modeling for the 2016 ERP. Key assumptions described in the following paragraphs were used in the Base-with-RES Plan, Alternative Plan 1 and Alternative Plan 2 (plans and scenarios are described in Section 8 Future Resource Analysis and Selection). Selected assumptions were varied in the Environmental, Low Load, High Load, High Gas, Low Gas, and NYMEX Gas Price scenarios, such as the price of natural gas or load growth, to test the risk associated with each specific assumption. Assumptions must be made for the Planning Period, RAP, planning reserves, financial parameters and DSM impact. In addition, fuel price forecasts, market price forecasts, and emissions cost assumptions must be input into the model for all types of generic resources as well as for existing resources.

Planning Period 2.1 The ERP Rules allow utilities to select a planning period of between twenty years and forty years. The Company selected twenty-five years for this 2016 ERP which covers the period 2016 through 2040. Twenty-five years was selected to allow an adequate horizon to evaluate conventional and renewable alternatives relative to the lives of those alternatives. Black Hills may use a longer-term planning horizon in a future resource plan when it needs to consider a baseload resource.

Resource Acquisition Period 2.2 A seven-year RAP is proposed for this 2016 ERP. This covers the period 2016-2022. The Company chose a seven-year RAP because that period complies with the rule and includes the years that Black Hills has identified a need for additional Eligible energy resources to comply with the RES. In addition, Commission Rule 3603(a) requires that utilities file resource plans every four years. The Company will file its next ERP on or before October 31, 2019. The RAP provides adequate time for the acquisition of the necessary resources to meet our customers’ resource needs and the RES through the RES Planning Period.

Planning Reserve Margin 2.3 Planning reserve is the amount of capacity that each electric utility must hold in reserve above its annual peak load requirements. A planning reserve margin is a percentage applied to the expected peak load to determine the minimum additional capacity that an electric utility should plan for to ensure that it will meet its peak load obligations in the event of an unforeseen loss of generating resources, extreme weather, or other unexpected conditions. For purposes of the 2016 ERP, the Company has continued to use a planning reserve margin of 15 percent that was approved by the Commission in the Company’s 2013


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ERP proceeding (Proceeding No. 13A-0445E).8 The Company believes a 15 percent reserve margin is appropriate. The Company’s peak requirement is expected to be approximately 400 MW in 2016 and its largest single hazard is 100 MW. As a member of the Rocky Mountain Reserve Group (“RMRG”), the Company has the ability to rely on the RMRG for up to two hours after a forced outage of any of its generating units. Thereafter, the Company will be required to replace the lost capacity. According to the WECC 2014 Power Supply Assessment the Rocky Mountain area is projected to have a surplus of capacity available in the market through 2021. The RMRG has adopted the WECC’s Standard BAL-002-WECC-2 for Contingency Reserves and the group, as a whole, meets this criteria. This standard includes reliability performance criteria such as minimum contingency reserves, types and levels of reserves (i.e. spinning reserves), methods for measurement and documentation of contingency reserves and compliance criteria. The entire WECC standard is included in Appendix B of this report. The 15 percent reserve margin was assumed for each of the base, high, and low load forecasts.

Fuel and Market Prices 2.4 The fuel price assumptions used in the 2016 ERP were based on the ABB WECC 2015 Fall Reference Case. This is a confidential, proprietary product which can be purchased from ABB. In order to protect ABB from public disclosure of its proprietary product, the details of the ABB fuel price forecasts are set forth in the Confidential Schedule K’s.

Natural Gas Prices 2.4.1 The Company used the natural gas price forecast for Henry Hub from ABB’s WECC 2015 Fall Reference Case for both existing and future natural gas-fired resources. Basis differential and transportation costs were added to ABB’s Henry Hub forecast to reflect the delivered price of natural gas. Table 3-1 below shows the price ranges of Henry Hub natural gas prices from 2016 through 2040 for the Base Plan and scenarios completed for the 2016 ERP. The natural gas price forecasts used in the modeling are included in Confidential Schedule K-1, Appendix K.

8 In accordance with the stipulation in Proceeding No. 08A-346E, the Company undertook a study to determine a planning reserve margin to use in its analysis in its 2013 ERP. The study was provided in Volume II of the 2013 ERP filing. The key finding of that report was that the appropriate reserve margin to use for planning purposes was between 20 and 30 percent. This level was determined on a probabilistic basis with a target loss-of-load hours of no higher than one day in ten years or 2.4 hours per year.


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Table 3-1 Average Annual Henry Hub Gas Price ($/MMBtu)

Scenario 2016 2040

Base $2.94 $13.43

High Gas $3.47 $19.90

Low Gas $2.38 $ 7.52

NYMEX Price $2.55 $13.43

CPP $2.90 $13.77

The High Gas price forecast, Low Gas price forecast and CO2 gas price forecast were all developed by ABB. ABB describes its High Gas Price forecast as a subjective view of the 90th percentile of the natural gas price probability distribution. This subjective view includes more stringent and costly regulation, significant bands or moratoria on hydraulic fracturing, small or economically recoverable resources then current median estimates, and higher cost of production then current estimates for either geological or technical factors. ABB’s Low Gas Price forecast is described as a subjective view of the 10th percentile of the natural gas price probability distribution. This subjective view includes less stringent, more streamlined and less costly regulation, larger economically recoverable resources, and lower cost of production than current estimates. The CO2 scenario used ABB’s best estimation of EPA’s building blocks to determine the contributions from retirements, re-dispatch of coal to gas, renewable capacity additions, and energy efficiency improvements. ABB found that these factors combined to lower gas demand from the power sector beginning in 2024. Small increases in power sector gas demand were seen from 2016-2023 as a result of targeted coal retirements. Beginning in 2025 lower gas consumption from the power sector compared with the Base Reference Case is offset by approximately 75 percent by increased consumption from industry and pipeline exports, and to a lesser degree LNG exports. The NYMEX Price forecast was developed by Black Hills and is comprised of the NYMEX natural gas price forecast and CIG Basis forecast for January 1, 2016 through December 31, 2021 that were published as of the close of trading on December 29, 2015. The Company used ABB’s Base price forecast for the remainder of the forecast, values for 2022 through 2040. As the Company has moved away from coal-fired generation, its fleet is fueled almost entirely by natural gas. On July 1, 2011, the Company filed a gas mitigation plan (Proceeding No. 11A-580E) that includes a hedging plan to protect customers against natural gas price spikes. The Commission approved the plan in Decision No. C11-1132. The plan was reviewed, changes (if any) were filed, and the plan was executed each year from 2012-2015. Early in 2016, as a result of the Company’s Cost of Service Gas initiative, the plan was suspended until further notice. The


15

Company is renewing its hedging plan in light of the Commission’s rejection of the Cost of Service Gas initiative.

Oil Prices 2.4.2 The oil price forecast from ABB’s WECC 2015 Fall Reference Case for diesel was used for the oil price forecast and is shown in Confidential Schedule K-2, Appendix K.

Economy Energy Prices 2.4.3 Economy energy is energy (sold without capacity) that may be available in the market from time-to-time and which is available for prices that are lower than the incremental cost of a utility’s own resources. Economy energy is not firm energy and, therefore, it is only available if a utility has adequate capacity to support its load requirements. The selling party may recall an economy energy transaction at any time. Thus, the buying party must maintain sufficient contingency reserve to replace recalled supply. The model was allowed to purchase up to 150 MW of economy energy every hour. However, the pricing and the capacity allowed to be purchased varied depending on the month of the year. Table 3-2 shows the market area used and the amount of economy energy allowed to be purchased each month from each market area. The market area price forecasts, included in Confidential Schedule K-8 through K-15, Appendix K, are based on the ABB Fall 2015 Reference Case.

Table 3-2 Economy Energy Purchase

Capacity Allowed by Market Area (MW)

Month CO-East Palo Verde Total

January 100 50 150

February 100 50 150

March 100 50 150

April 100 50 150

May 100 50 150

June 50 100 150

July 50 100 150

August 50 100 150

September 100 50 150

October 100 50 150

November 100 50 150

December 100 50 150


16

Seasonal Firm Market Purchase Prices 2.4.4 The Company used the ABB Fall 2015 Reference Case energy price forecast for the Palo Verde, Arizona (“PV”) market area plus a 20 percent premium and transmission adder, as a proxy for seasonal firm market purchases. Seasonal firm market purchases were assumed to be 25 MW blocks up to 75 MW through 2021 then adjusted to 25 MW blocks up to 50 MW through the remainder of the Planning Period9. In establishing these assumptions, the Company considered both the excess capacity in the region and capacity import limitations specific to the Company’s system. Seasonal firm market purchases were assumed for 16 hours per day six days a week for the summer months. The base Palo Verde, Arizona price forecast used is included in Confidential Schedule K-4 through K-7, Appendix K.

Financial Parameters 2.5 Financial assumptions were used to develop incremental financial statements for the Company. Cost of debt and equity, return on rate base, and interest rate assumptions are necessary for the model to calculate the total system PVRR. Table 3-3 presents the financial parameters used for the 2016 ERP evaluation, including cost of debt, cost of equity, weighted-average cost of capital, income tax rate, rate of escalation (same as inflation rate), capital structure, property tax rate, and fixed charge rates. Tax lives of 20 years were used for combined cycle and peaking technology. A 5-year tax life was used for solar, wind, and batteries. The Company used a 5.29 percent cost of debt to reflect anticipated interest rates when resources may be added in future years and a 9.83 percent return on equity value, both of these values were approved by the Commission in the Company’s most recent approved rate case (Proceeding No. 14AL-0393E).

9 The Company reduced the assumption for seasonal firm capacity purchases in 2022 based on data from the WECC 2014 Power Supply Assessment. This study shows a reserve margin deficit in the RMRG and SRSG beginning in 2022 and excess capacity in the NWPP beyond 2022. Therefore the Company assumed that the availability of seasonal firm capacity from 2022 through the end of the Planning Period will be reduced.


17

Table 3-3 Financial Parameters

Component Annual Rate (percent)

Cost of Debt 5.29

Equity 9.83

Weighted-Average Cost of Capital (WACC After-tax) 6.54

Weighted-Average Cost of Capital (WACC Before-tax) 7.55

Income Tax Rate 38.01

Rate of Escalation 2.8

Capital Structure

Equity 49.8 Debt 50.2

Property Tax Rate 1.20

20-year fixed charge rate (Solar and Wind) 12.8839

30-year fixed charge rate (Combined Cycle, Peaking) 11.0833

Emissions Costs 2.6

In February 2008, the United States Court of Appeals for the District of Columbia Circuit vacated The Clean Air Mercury Rule (“CAMR”), so no mercury costs were included in the modeling for the 2016 ERP. In addition, because no national greenhouse gas regulation bill has passed the U.S. Congress and the Supreme Court issued a stay of EPA’s CPP, no CO2 emission costs were considered in any of the plans or scenarios, with exception to the Environmental scenario, of the 2016 ERP or in the development of the stochastic prices. For the Environmental scenario, the ABB WECC Fall 2015 Reference Case CO2 Tax scenario forecast for CO2 emission costs was used. This forecast is included in Confidential Schedule K-3, Appendix K.

Demand-Side Management 2.7 DSM plans for the Company are filed for and approved outside of the ERP process. Black Hills has administered DSM plans since 2009 helping customers to conserve energy and save money. The graph below shows the energy efficiency savings that were achieved from 2009 through 2015.


18

Figure 3-1 2009 – 2015 Energy Efficiency Savings (kWh)

Black Hills filed for approval of its 2016-2018 Electric DSM Plan for calendar years 2016, 2017 and 2018 on May 29, 2015 in Proceeding No. 15A-0424E. A Settlement Agreement resolving the application was filed with the Commission on September 18, 2015 and a hearing on the settlement was held on September 25, 2015. Approval of the 2016-2018 DSM Plan, as modified by the Settlement Agreement and the recommended decision (Decision No. R15-1292), was issued on December 8, 2015. The 2016-2018 DSM Plan is composed of three broad categories: residential programs, non-residential programs (e.g., commercial and industrial), and special programs. Each program has been designed to provide a variety of energy efficiency opportunities for various customer types.

• Residential programs include high efficiency lighting, high efficiency cooling, appliance recycling, home energy comparison reports, and on-site energy evaluations.

• Commercial and industrial programs include rebates for prescriptive and small business direct install lighting; prescriptive rebates for the purchase and installation of pre-qualified measures, including HVAC, motors and refrigeration; custom rebates for cost effective non-prescriptive measures and equipment; incentives for the design and construction of new energy efficient buildings and a self-direct program that provides rebates for cost-effective non-prescriptive measures or equipment for customers with an

4,553,767

17,295,547 18,561,256

31,740,049

17,829,736

25,826,523

115,806,878

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

2009/2010 2010/2011 2011/2012 2012/2013 2014 2015 5-Year Total


19

aggregated peak demand higher than 1 MW in any single month and annual energy usage of 5,000 MWh.

• Special Programs include a low-income assistance program and a school education program.

The approved 2016-2018 DSM Plan will result in the demand reductions and energy savings shown in Table 3-4. The 2016 ERP load forecast was adjusted – reductions in both the peak demand (MW) and energy (MWh) forecasts – to incorporate the DSM impacts of the Company’s 2016-2018 DSM Plan.

Table 3-4 2016-2018 DSM Plan

Projected Peak

Savings (MW)

Projected Energy Savings (MWh)

Plan Year 1 – 2016 5.23 18,014

Plan Year 2 – 2017 5.60 19,769

Plan Year 3 - 2018 5.82 20,633

Plan Total 16.65 58,416


20

Annual load forecast adjustments to peak demand and energy are shown in Table 3-5.

Table 3-5 Adjustments to Load Forecast for DSM

Year

Projected Peak

Savings (MW)*

Projected Energy Savings

(MWh)**

2016 (3.1) (18,014)

2017 (8.5) (37,783)

2018 (14.2) (58,416)

2019 (16.7) (58,416)

2020 (16.7) (58,416)

2021 (16.7) (58,416)

2022 (16.7) (58,416)

2023 (16.7) (58,416)

2024 (16.7) (58,416)

2025 (16.7) (58,416)

2026 (16.7) (58,416)

2027 (16.7) (58,416)

2028 (16.7) (58,416)

2029 (16.7) (58,416)

2030 (16.7) (58,416)

2031 (16.7) (58,416)

2032 (16.7) (58,416)

2033 (16.7) (58,416)

2034 (16.7) (58,416)

2035 (16.7) (58,416)

2036 (16.7) (58,416)

2037 (16.7) (58,416)

2038 (16.7) (58,416)

2039 (16.7) (58,416)

2040 (16.7) (58,416) *Demand adjustment reflects the expected saving achieved as of July 1 of each year. **Energy adjustment reflects the expected savings achieved over the entire year.


21

3 Load Forecast The Company used an econometric forecasting methodology to forecast peak demand and energy for the 2016 ERP. Black Hills gathered and refined a variety of different types of datasets including historical load, revenue, economic, and weather data. This data was used to develop regression models for the monthly system-level peak demand forecast and the major customer class energy forecasts. The final system-level monthly peak demand was then computed by adding large customer loads, including their anticipated future load growth and accounting for the effects of DSM, to the load forecast produced from the econometric methods. The final system-level major customer class energy forecasts were computed by adding large customer loads, including their anticipated future load growth, losses and accounting for the effects of DSM to the energy forecasts calculated through the regression analysis. Base, high, and low load forecasts were developed and are described in detail in the following sections. There are no annual sales of energy or capacity to other utilities or intra-utility energy and capacity usage, thus the load forecast has not been adjusted for these factors. The Company prepared a system-level peak demand forecast rather than a major customer class level demand forecast because historical demand data for all customer classes has only been maintained by the Company since 2013 when the full implementation of the automated metering infrastructure (“AMI”) system in the Company’s service territory was completed. With only three years of historical customer-level demand data available from the AMI dataset, the Company determined that a system-level peak demand forecast using system-level hourly load data from the Company’s Open Access Technology International, Inc. (“OATI”) database and Aquila legacy systems would provide a better base for the peak demand forecast. The OATI system included hourly load data from June 25, 2008 through December 31, 2015, and the data for January 1, 2006 through June 24, 2008 was acquired from Aquila’s data system. Black Hills did complete major customer class energy forecasts using historical customer class data that is maintained in the Company’s customer information system (“CIS+”). The AMI interval consumption data available for 2014 and 2015 has been validated for overall accuracy by comparing the total kilowatt hours of measured consumption by rate code to the total retail kilowatt hours billed in the CIS+ billing system during 2014 and 2015, respectively. The variance between the two datasets for 2015 is 0.09 percent.

Econometric Model Overview 3.0 Econometric modeling was used as the foundation for system level demand and major customer class energy forecasts. The econometric models were developed using the statistical software package Stata. Black Hills used this software to develop


22

statistical models that estimate the effect of various factors (e.g., weather) on customer sales, the number of customers served, and system peak demand. The explanatory factors used in these equations consist of weather, electricity prices (calculated as average revenue per-kWh), demographic variables, and economic variables. The advantages of econometric forecasting models include:

• The ability to estimate effects of specific drivers on sales and demand, controlling for the effect of all other included variables. For example, the models estimate the effect of economic conditions on sales controlling for variations in weather conditions.

• The ability to refine and adapt the models to reflect changing circumstances over time.

• The use of third-party weather, economic, and demographic data in the forecast, removing potential concerns about biased inputs.

• Providing measures of the statistical precision of the estimates, such as the statistical significance of particular driver variables or the overall explanatory power of the forecast model.

Econometric forecasting models reveal relationships between sales (or demand or the number of customers served) and economic or demographic variables to forecast future developments. The process begins by estimating the historical relationship between sales (or demand or the number of customers served) and the relevant drivers, which may include weather, economic conditions, demographic trends, seasonal patterns, or retail electricity prices. The resulting estimates of the relationship between each driver and the associated outcome (e.g., sales) are then applied to forecasts of the drivers to develop the forecast sales, demand, or number of customers served. The statistical models are reviewed and refined to ensure that the estimated relationships are reasonable (i.e., correctly signed and of reasonable magnitude).

Load, Economic, and Weather Data 3.1

Historical Load Data 3.1.1 The Company used historical system-level hourly load data that has been maintained since 2006. The data set was reviewed to ensure accuracy and any data anomalies were replaced by averaging data from the hour before and the hour after to create a new value for missing or erroneous data. One large customer’s load was removed from the Company’s historical load data prior to modeling. The Company excluded this customer’s load from the historical data because it is a significant percentage of the Company’s total load and is not expected to increase. Therefore the Company did not want the growth rates calculated through the regression analysis applied to this large load. Black Hills subtracted this large customer’s


23

hourly peak data from the system historical data, creating a new “base” historical dataset. This “base” historical dataset was used in the regression analysis. The excluded data for the one large customer was added back into the demand forecast after the models runs were complete. The major customer class energy forecasts were developed using historic sales and customer count data that has been maintained by the Company in its (“CIS+”). Sales data by rate identification from 2006 through 2015 was gathered, reviewed, and aggregated into three major customer classes: residential, commercial, and industrial. Similar to the hourly load data, the Company created a “base” historical sales dataset. The Company removed the historical data of three large customers as well as lighting service and Company-use data prior to conducting the sales forecast modeling. The lighting and Company-use sectors were removed to ensure that the customer class sales growth rates were not skewed by the historical growth patterns of these sectors and the Company did not want the growth rates calculated through the regression analysis applied to the three large customer loads. The excluded data for the three large customers, lighting, and Company-use was added into the aggregated sales forecast after the major class forecast models were complete. The historical load and sales data used in the peak demand and sales models is included in Schedule C-1 and Schedule C-2, Appendix C, respectively.

Economic Data 3.1.2 Economic and demographic historical and forecast data were obtained from Woods & Poole Economics, Inc. (“W&P”) for Pueblo and Fremont Counties for the years 1969 through 2050. Though this dataset includes several economic variables, Black Hills determined that the relevant variables for the Company’s load forecasts were Gross Regional Product (“GRP”), Number of Households, Nonfarm Employment, Manufacturing Employment, Total Employment, Mean Real Household Total Personal Income, Net Earnings, Total Earnings, Total Retail Sales Including Eating and Drinking Places Sales, and Total Personal Income. Each of these variables was tested in the regression analysis. In addition, historical electric price data was gathered from the Company’s FERC Form 1, page 304 filings reflecting the average annual price of electricity, on a dollars per-kWh basis, for each of Black Hills’ customer classes. The historical and forecasted economic data and historical price data used in the peak demand and sales models are included in Confidential Schedule C-3, Appendix C.

Weather Data 3.1.3 Historical weather data was collected from the NOAA National Climatic Data Center’s (“NCDC”) Pueblo Airport weather station. The historical hourly


24

temperature data was used to calculate heating degree days (“HDD”) and cooling degree days (“CDD”) using a 60 degree Fahrenheit threshold. The heating degree hours (“HDH”) and cooling degree hours (“CDH”) were calculated using 50 degree and 70 degree Fahrenheit thresholds, respectively. The HDD, CDD, HDH, and CDH data were used for both historical and normal weather forecasting purposes. The monthly CDD daily average was based upon the monthly average of total CDD. The historical weather data used in the peak demand and sales models is included in Schedule C-4 and Schedule C-5, Appendix C respectively.

Normal Weather Conditions 3.1.4 The weather variables in the energy and demand forecasts are set to reflect “normal” conditions, which is interpreted as the average weather conditions over 20 years. In the energy model, the average of the sum of the cooling degree days over the available time period was used to calculate normal weather for each month. In the peak demand model, each month is determined to be either a predominantly cooling- or heating-peak month, and then only the relevant peak-hours for each month and year are averaged. Those averages are averaged again for each month and used as normalized peak weather conditions.

Forecast Methodology 3.2 Multiple combinations of the variables described above were tested in the development of the energy and demand forecasts. The models were refined to ensure that the estimates were logically reasonable (e.g., sales increase with CDDs) and statistically significant (or approaching statistical significance). Normal weather conditions are used to forecast energy and demand.

Peak Demand Forecast Methodology 3.2.1 The Company’s system demand forecast is a system-level forecast inclusive of residential, commercial, industrial, and lighting sectors. Each month’s peak hours from 2006 to 2015 were used to model the monthly peak demand forecast. The peak demand model was estimated using Ordinary Least Squares (“OLS”). The resulting estimates were used in combination with normal weather and forecasted economic conditions to forecast peak demands.

Energy Forecast Methodology 3.2.2 To complete the energy forecast, the Black Hills system was disaggregated into three major customer classes: residential, commercial and industrial. The residential customer class is an aggregation of all of Black Hills’ residential rate IDs. The commercial class is an aggregation of Black Hills’ small and large general


25

service rate IDs, and the Company’s large power service rate IDs constitute the industrial class. Summaries of the final energy and demand equations are described in Appendix C. Also included in Schedules C-1, C-2, Confidential C-3, Appendix C are the historical and forecasted values for variables that were used in the models. The resulting forecasts, monthly and annual, for the residential and customer use per customer (“UPC”), number of customer and sales models and the industrial sales model are in Schedules C-8 and C-9, Appendix C, respectively..

Large Customer Growth Assumptions 3.2.3 The Company periodically reviews the growth plans of the largest customers in its service territory. These expected load increases can be uncertain and depend to a great extent on economic conditions. Table 4-1 shows anticipated large customer load additions and reductions (with confidence factor applied) for the period 2016 through 2022. This information was compiled based on information gathered by the Company’s economic development personnel and adjusted by a confidence factor depending on the level of certainty expressed by the customer that the growth will actually occur. These annual changes in large customer loads are reflected in the peak demand and energy load forecasts.

Table 4-1 Large Customer Load Additions and Reductions 2016 - 2022

Customer Load Factor

2016 (MW)

2017 (MW)

2018 (MW)

2019 (MW)

2020 (MW)

2021 (MW)

2022 (MW)

Large Customer A 85% 1.5 1.6 2.5 1.6 1.3

Large Customer B 68% 6.4 -6.3 -5

Large Customer C 55% 1.7

The economic downturn and the uncertainty of the PTC legislation have had a significant impact on several of the larger customers in the Company’s service territory in the past few years. Of significance in this area are the changes in shorter term forecasts associated with several of the Company’s large volume customers. Since the Company’s 2013 ERP filing, the reductions in the short-term load forecasts for these customers has amounted to more than 15 megawatts of reduction or delay in forecasted demand growth. Table 4-2 shows a comparison between the large volume customer load addition assumptions used in the 2013 ERP and the actual load growth experienced by these customers in 2013 through 2015. While the forecast for this small group of customers continues to reflect moderate growth in the near term (11-12 additional megawatts over the period, 2016-2020), the anticipated rate of growth has slowed during that time period. In addition, one large customer expects significant load reduction in 2021 and 2022.


26

Table 4-2 2013 ERP Large Customer Load Additions Assumptions

Compared to Actual Load Additions Projected Peak Growth

2013 2014 2015

2013 ERP Projected Additions (MW)

Actual 2013 Additions (MW)





Customer 1 1.9 0.8 9.5 0.4 1.9 3.5 Customer 2 5.4 1.0 0.0 1.5 9.9 0.6 Customer 3 0 0 0 0 9 0 Customer 4 0 0 0.9 0 0 0 Customer 5 0.8 0 0 0 0 0 Annual Total Additions 8.1 1.8 10.4 1.9 20.8 4.1

Industrial Agriculture is a growing customer segment comprised primarily of cannabis-related farming activities. In contrast to the trends characterizing Black Hills’ aforementioned industrial customer class, growth in both the number and overall energy demand associated with the industrial agriculture loads has been trending upwards since early 2013. While the Company will continue to monitor growth of this new and growing industry and its energy needs, it is the Company’s belief that the lack of sufficient load and growth data combined with the uncertainties surrounding the new industry’s legal and regulatory environment preclude the Company’s ability to accurately forecast future growth patterns at this time.

Base Peak Demand and Annual Energy Forecasts 3.3 The final base system-level monthly peak demand forecast was computed by adding the one large customer and anticipated future load growth of other large customers into the load forecast calculated by the regression analysis. The final system-level major customer class energy forecasts were computed by adding large customer loads, including their anticipated future load growth, lighting service, and Company-use to the energy forecasts calculated through the regression analysis. Combined transmission and distribution losses were also added into the annual energy forecast for each major customer class. Losses were estimated by calculating a weighted loss percentage for each aggregated major class. The class level transmission and distribution losses are shown in Table 4-3. Separate system loss estimates cannot be made for transmission and distribution because the forecast was not developed at the transmission and distribution voltage level. The peak demand and energy forecast values for the Base load forecast are shown in Table 4-4.


27

Table 4-3 Combined Transmission and Distribution Losses

Major Sales

Class Line Loss Class Average

Estimated

Losses

Aggregated

Customer Class

Weighted

Losses by Class

Non-

aggregated

Customer

Class Sales

Losses Residential Residential 5.82% 5.82%

Commercial Large General Service - Primary

4.03% 5.67%

Large General Service - Secondary

5.82%

Small General Service 5.82%

Industrial Large Power Service - Primary

4.03% 4.11%

Large Power Service - Secondary

5.82%

Large Power Service - Transmission

2.21%

Large Customer 1

Large Customer 1 4.03% 4.03%

Large Customer 2


Large Customer 3


Lighting 5.82% 5.82%

Company Use 5.82% 5.82%

Auxiliary* Total System 0.63%

Station Use

* Auxiliary Total System and Station Use MWh consists of 2014 totals


28

Table 4-4 Base Load Forecast

Year Peak Demand*

(MW) Annual Energy*

(MWh) Losses (MWh)

2016 395 2,037,488 109,783

2017 395 2,065,684 111,025

2018 394 2,084,666 112,432

2019 397 2,123,907 113,806

2020 401 2,156,324 115,091

2021 401 2,157,010 116,167

2022 397 2,145,097 117,207

2023 398 2,152,368 118,356

2024 401 2,173,886 119,599

2025 404 2,194,817 120,808

2026 406 2,216,110 122,038

2027 409 2,237,165 123,255

2028 411 2,258,860 124,509

2029 414 2,280,431 125,756

2030 416 2,300,541 126,916

2031 419 2,319,801 128,026

2032 421 2,338,428 129,097

2033 423 2,356,329 130,126

2034 426 2,374,779 131,188

2035 428 2,393,173 132,246

2036 430 2,411,213 133,283

2037 432 2,427,570 134,221

2038 435 2,443,671 135,142

2039 437 2,460,146 136,086

2040 439 2,476,553 137,025 *Peak Demand and Annual Energy Forecast values includes impacts of DSM Plans and losses.

Low and High Forecasts 3.4 The base load forecast is assumed to represent the expected midpoint of possible future outcomes, meaning that a future year’s actual load may deviate from the midpoint projections. To evaluate the impact of these potential deviations, low, and high load forecasts were developed. The Company prepared low and high load forecasts in addition to its base load forecast as required by Rule 3606(b). For the high and low load forecasts, the


29

Company developed an 80 percent confidence interval band around the base demand and sales forecasts. Black Hills had a relatively short historical time series of data over which to examine variations in demand and sales for the service territory. So the Company opted to base the degree of variability in the forecast on GRP and nonfarm employment data rather than historical demand and energy usage. GRP and nonfarm employment historical data was available from the Woods and Poole dataset for the time period 1969 through 2013. The peak demand model provided an estimate of the effect of changes in GRP and nonfarm employment on changes in peak demand, along with a standard error associated with the estimate. These two uncertainties (in GRP and nonfarm employment over time and in the estimated effect of GRP and nonfarm employment on peak demand) are combined to produce the confidence interval around the demand and sales forecasts. The following steps were used to develop the confidence interval.

1. Calculate the average annual 10-year percentage change in GRP and nonfarm employment for each 10-year window between 1969 and 2013, producing 35 separate percentage change values.

2. From the peak demand model, obtain the estimated coefficient associated with the GRP and nonfarm employment variables.

3. Multiply each of the 35 GRP and nonfarm employment 10-year growth rates by the corresponding coefficient from the peak demand model, then add the resulting values. The sum represents the 10-year average growth rate in peak demand during the 10-year window in question.

4. Calculate the mean and standard deviation of the growth rate of demand across these 35 observations.

5. Calculate the coefficient of variation (“CV”) from this mean and standard deviation by dividing the standard deviation by the mean. This represents the historical relationship between mean load growth and the variability of load growth.

6. Calculate the forecast standard error of load growth by multiplying the CV by the forecast percentage load growth (as forecast from the peak demand forecast model).

7. The high and low scenarios are simulated as the 90th and 10th percentile values (respectively) from a normal distribution with a mean equal to the forecast growth rate in peak demand and the standard deviation equal to the value derived in Step 7.

8. These high and low percentages are applied to the demand and sales forecasts in each of the forecast months, beginning in 2017.

The values for the base, low and high load forecasts, including the effects of DSM are shown in Table 4-5.


30

Table 4-5 Low, Base and High Load Forecasts

Peak Demand (MW) Energy (GWh)

Year Low Base High Low Base High

2016 395 395 395 2,037 2,037 2,037

2017 392 395 398 2,041 2,066 2,089

2018 387 394 400 2,032 2,085 2,138

2019 387 397 406 2,046 2,124 2,204

2020 388 401 414 2,054 2,156 2,263

2021 385 401 417 2,030 2,157 2,291

2022 378 397 417 1,993 2,145 2,307

2023 376 398 422 1,976 2,152 2,344

2024 376 401 428 1,973 2,174 2,395

2025 376 404 434 1,969 2,195 2,445

2026 375 406 440 1,966 2,216 2,497

2027 375 409 446 1,963 2,237 2,550

2028 375 411 452 1,959 2,259 2,604

2029 374 414 458 1,956 2,280 2,659

2030 374 416 464 1,953 2,301 2,711

2031 374 419 470 1,949 2,320 2,761

2032 373 421 476 1,946 2,338 2,810

2033 373 423 481 1,945 2,356 2,858

2034 373 426 487 1,942 2,375 2,907

2035 372 428 493 1,941 2,393 2,957

2036 372 430 498 1,939 2,411 3,007

2037 372 432 504 1,938 2,428 3,053

2038 371 435 510 1,936 2,444 3,098

2039 371 437 516 1,934 2,460 3,145

2040 370 439 521 1,933 2,477 3,191

Table 4-6 shows the total system summer and winter peak demand forecast for each year of the Planning Period.


31

Table 4-6 Seasonal Peak Demand Load Forecast Comparison – Base, Low and High

(including impacts of DSM Plans)

Peak Summer Demand

(MW) Peak Winter Demand

(MW)

Year High Base Low High Base Low

2016 395 395 395 320 320 320

2017 398 395 392 321 319 316

2018 400 394 387 323 317 312

2019 406 397 387 329 321 314

2020 414 401 388 335 325 315

2021 417 401 385 334 321 308

2022 417 397 378 334 318 303

2023 422 398 376 339 320 303

2024 428 401 376 344 322 302

2025 434 404 376 349 324 302

2026 440 406 375 354 326 302

2027 446 409 375 359 328 301

2028 452 411 375 363 330 301

2029 458 414 374 368 332 301

2030 464 416 374 373 334 301

2031 470 419 374 377 336 300

2032 476 421 373 382 338 300

2033 481 423 373 387 340 300

2034 487 426 373 391 342 299

2035 493 428 372 396 344 299

2036 498 430 372 400 346 299

2037 504 432 372 405 348 299

2038 510 435 371 410 349 298

2039 516 437 371 414 351 298

2040 521 439 370 419 353 298

Historical Peak Demand and Annual Energy 3.5 The Company has historically experienced annual peaks in the summer. Peak demand and annual energy for the period 2011-2015 are provided on Table 4-7. Since 2011, the summer peak has experienced an average annual growth rate of 0.04 percent and the historical annual energy experienced an average annual growth rate of 1.76 percent.


32

Table 4-7 Historical Peak Demand and Annual Energy

Year

Peak Demand Annual Energy* Load Factor (%) Summer

(MW) % Change GWh % Change

2011

2012

2013 -

2014

2015

Average Annual Growth (%)

0.04

1.76

* Annual energy includes transmission and distribution losses.

Load Forecast Comparison – 2013 ERP versus Actual and versus 3.62016 ERP

A comparison of actual peak demand and energy sales and the forecasts from the 2013 ERP and this 2016 ERP is shown in Table 4-9. In the 2013 ERP, the annual energy growth was projected at 0.92 percent over the 2013-2037 period, compared to the 0.82 percent growth rate projection in the current plan over the 2016-2040 time period. The annual summer and winter peak demand growth over the 2013-2037 period was forecasted at 1.09 percent and 1.16 percent, respectively, in the 2013 ERP, compared to the summer and winter 2016 ERP growth rates projected to be 0.44 percent and 0.41 percent, respectively, as shown in Table 4-9. The economic downturn and the uncertainty of the PTC legislation have had a significant impact on several of the larger customers in the Company’s service territory in the past few years. The changes in short-term anticipated load growth associated with several of the Company’s large volume customers had a significant impact on the Company’s 2016 ERP load forecast as compared to the 2013 ERP load forecast. Since the Company’s 2013 ERP filing, the reductions in the short-term load forecasts for these customers has amounted to more than 15 megawatts of reduction in forecasted demand growth. The load forecast methodology used for the 2013 ERP relied upon historical load data from 2006 through 2012 that showed a steadily increasing peak demand (with exception to one year) over this time period with an all-time system peak occurring in the last year of the historical data. Table 4-8 shows the actual summer peak demand for 2006 through 2015.


33

Table 4-8

Historical Peak Demand

Year

Peak Demand

(MW) % Change

2006 360

2007 375 4.17

2008 376 0.27

2009 365 -2.93

2010 384 5.21

2011 392 2.08

2012 400 2.04

2013 381 -4.75

2014 384 0.79

2015 392 2.08

Average Annual Growth (%)

1.77

In 2013 the Company experienced a peak demand of 381 MW, 19 MW lower than the previous year’s peak demand of 400 MW. Though demand has continued to increase since 2013, demand has not yet reached the peak that was set in 2012. The Company compared the 2016 ERP load forecast to the 2013 ERP load forecast and found that the primary reasons that the 2016 ERP demand forecast is lower than estimated for the 2013 ERP are: (1) the anticipated effects of the Company’s 2016-2018 DSM Plan; (2) the sizable revisions to the large customer load projection since the 2013 ERP; and (3) the fact that the econometric analysis used in this 2016 ERP included three additional years of historical data. This is important because the Company’s historical system peak occurred in 2012, the last year of data used in the 2013 ERP load forecast analysis. The lower annual system peaks in 2013, 2014, and 2015 are accounted for in the 2016 ERP load forecast. Table 4-9 depicts the load forecast comparison between the 2013 ERP and the 2016 ERP.


34

Table 4-9

Peak Demand and Energy Forecast Comparison

Year

Annual Energy including DSM (GWh)

Summer Peak Demand including

DSM (MW) Winter Peak Demand including DSM (MW)

2013 ERP 2016 ERP 2013 ERP 2016 ERP 2013 ERP 2016 ERP

2011 1,915* 1,915* 392* 392* 297* 297*

2012 1,928* 1,928* 400* 400* 284* 284*

2013 1,928* 1,928* 381* 381* 289* 289*

2014 1,960* 1,960* 384* 384* 298* 298*

2015 2,052* 2,052* 392* 392* 303* 303*

2016 2,177 2,037 450 395 339 320

2017 2,192 2,066 457 395 345 319

2018 2,098 2,085 445 394 332 317

2019 2,098 2,124 449 397 335 321

2020 2,116 2,156 454 401 339 325

2021 2,135 2,157 458 401 341 321

2022 2,154 2,145 462 397 344 318

2023 2,173 2,152 466 398 347 320

2024 2,192 2,174 470 401 350 322

2025 2,212 2,195 474 404 353 324

2026 2,231 2,216 479 406 356 326

2027 2,251 2,237 483 409 359 328

2028 2,271 2,259 487 411 362 330

2029 2,291 2,280 491 414 365 332

2030 2,311 2,301 496 416 369 334

2031 2,332 2,320 500 419 372 336

2032 2,352 2,338 504 421 375 338

2033 2,373 2,356 509 423 378 340

2034 2,394 2,375 513 426 382 342

2035 2,416 2,393 518 428 385 344

2036 2,437 2,411 523 430 388 346

2037 2,459 2,428 527 432 392 348

2038 2,444 435 349

2039 2,460 437 351

2040 2,477 439 353

Average Annual Growth

2013-2037 0.92% 1.09% 1.16%

2016 – 2040 0.82% 0.44% 0.41%

*Actual

Table 4-9 reflects the load forecast that was filed with the 2013 ERP on April 30, 2013, however, the load forecast was reduced as a part of the 2013 ERP Settlement Agreement. The levels of DSM modeled for the Company’s 2013 ERP Baseline 1 with RES Plan (the base plan in which the 30 percent of energy sales provided by


35

renewable resources by 2020 requirement is achieved) and which is reflected in Table 4-9, assumed compliance with C.R.S. § 40-3.2-104(2), requiring a 5 percent reduction in both retail system peak demand and retail energy sales from 2006 levels by 2018 due to DSM measures. These savings levels were lower than what was approved by the Commission in Proceeding No. 12A-100E, the Company’s Electric DSM Plan for 2012-2015. As a part of the Settlement Agreement for the 2013 ERP, the Company agreed to reduce the load forecast to reflect the anticipated demand and energy savings included in the Commission-approved 2012 DSM Settlement Agreement in Proceeding No. 12A-100E. The revised 2013 ERP peak demand forecast is compared to the 2016 ERP peak demand forecast in Table 4-10.


36

Table 4-10

Peak Demand Forecast Comparison

Year

Summer Peak Demand including

DSM (MW)

2013 ERP Revised 2016 ERP

2011 392* 392*

2012 400* 400*

2013 381* 381*

2014 384* 384*

2015 392* 392*

2016 437 395

2017 443 395

2018 432 394

2019 436 397

2020 440 401

2021 444 401

2022 448 397

2023 453 398

2024 456 401

2025 461 404

2026 465 406

2027 469 409

2028 474 411

2029 478 414

2030 482 416

2031 487 419

2032 491 421

2033 495 423

2034 500 426

2035 504 428

2036 509 430

2037 514 432

2038 435

2039 437

2040 439

*Actual values

Energy and Capacity Sales to Other Utilities and Intra-Utility Energy 3.7and Capacity Sales and Losses

Pursuant to Rule 3606(a)(III), the Company must provide a forecast of annual energy and capacity sales to other utilities; and capacity sales to other utilities at the time of coincident summer and winter peak demand. The Company does not have any energy or capacity contracts with other utilities.


37

Pursuant to Rule 3606(a)(IV), the Company must provide a forecast of annual intra-utility energy and capacity use at the time of coincident summer and winter peak demand. The Company does not have any intra-utility energy or capacity contracts.

Load Profiles 3.8 Typical day load patterns for each major customer class presented for peak day, average day, and representative average off-peak days for each calendar month are provided in Appendix D. The major customer classes represented by these load profiles are the same as the major customer classes used for the load forecast. These monthly load shapes were developed from data acquired from the Company’s AMI system for the year 2015 and reflect average customer use for each major class.


38

4.0 Supply-Side Resources

4.1 Existing Resources The Company’s generation resources consist of two existing natural gas combustion turbines, three diesel plants, and 50 percent ownership of the Busch Ranch Wind Project. The Peak View Wind Project, and a 40 MW LM6000 that will be located at PAGS are currently under construction. The Rocky Ford diesels are located in Rocky Ford, the Pueblo diesels, Airport diesels, and the three PAGS combustion turbines are located in Pueblo, and the Busch Ranch Wind Project, and Peak View Wind Project are located in Huerfano County and Las Animas County, Colorado. The data used for modeling these units are shown in Table 5-1. This table provides information on unit operating parameters for each of the generating facilities. Nameplate capacity is considered to be equivalent to rated capacity. The summer capacity is what is considered dependable capacity. The annual availability is projected to range between 80 percent and 90 percent for all units and is dependent on the timing of major overhauls as well as actual forced outages.

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40

Diesels 3.8.1 The diesel units commonly known as the Pueblo, Rocky Ford, and the Airport Diesels are used for peaking, to support the transmission system, and to provide system reserves. There are five 2 MW diesel units at each of Pueblo and Rocky Ford. There are four 2.5 MW diesel units at the Airport Station.

Pueblo Airport Generating Station 3.8.2 Two LMS100 natural gas-fired combustion turbines at PAGS began commercial operation on January 1, 2012. The Company is currently constructing a 40 MW LM6000 at PAGS that was approved by the Commission in Decision No. C14-0007. The LM6000 is replacement capacity for the Company’s 42 MW Clark Station that was retired pursuant to the Clean Air-Clean Jobs Act (“CACJA”). The LM6000 is expected to begin commercial operation on December 31, 2016.

Existing Purchases 3.8.3 The Company purchases 200 MW of firm power from Black Hills Colorado IPP, LLC. This power is generated by two natural gas-fired combined cycle units at PAGS that began commercial operation on January 1, 2012. This contract expires at the end of 2031. The Company also has one agreement in place for the purchase of 50 MW of firm energy. This contract expires at the end of 2016. Firm power is also purchased through what is referred to as the MPS Agreement. The Company has a letter agreement in place with the former Missouri Public Service (“MPS”). MPS provides capacity and energy to the Western Area Power Administration (“Western”) on the eastern grid. In exchange, Western provides capacity and energy to the Company on the western grid. This agreement expires on September 30, 2024 and provides 5 MW of firm capacity and energy to the Company.

- -


41

Coordination Letters 3.8.4

Pursuant to Section 3607(b) of the ERP Rules, utilities must coordinate their ERP plan filings such that the amount of electricity purchases and sales between utilities during the Planning Period is reflected uniformly in their respective plans. The Company does not have any contracts of this type. Therefore no coordination letters are provided in this 2016 ERP.

Existing Renewables 3.8.5

The Company’s existing renewable resources include the Busch Ranch Wind Project, modest amounts of on-site solar in the form of photovoltaics (“PV solar”) installed by customers, a 120 kW Community Solar Garden, and the Vestas 1.8 MW wind turbine. The Peak View Wind Facility is currently under-construction with commercial operation expected in late 2016.

Busch Ranch Wind Project 3.8.6 The Busch Ranch Wind Project in eastern Huerfano County, Colorado began commercial operation in October 2012. The project consists of sixteen 1.8 MW wind turbines. The Company owns half of the 29 MW and purchases the energy produced by the remaining turbines under a PPA that has a 25-year term from the date of commercial operation of the facility. The Company proposed the project pursuant to Rule 3660(h) of the RES Rules.

Solar Resources 3.8.7 The Company has offered solar on-site distributed generation programs to residential, commercial, and industrial customers since 2006. The programs offered have changed over the years but generally the Company has offered capacity in small (under 10 kW), medium (10 kW to 100 kW), and large (over 100 kW) increments to customers who connect a PV solar system to the Company’s grid. These customers have received rebates, production based incentives and bill credits and the Company is entitled to the RECs generated by these systems over the first twenty years of the solar installation’s operation. In addition to the RECs acquired through the solar on-site program, Black Hills has a contractual obligation with the winning bidder of a 2007 competitive acquisition process to acquire the RECs from a large class (100 kW to 2 MW) solar on-site installation and a recently installed 120 kW Community Solar Garden (“CSG”).


42

1.8 MW Distributed Generation Wind Facility 3.8.8 Vestas Towers A/S, a wind turbine manufacturer with a facility located in the Company’s service territory in Pueblo, Colorado, has installed on its site a wind turbine (V100 1.8 MW) with a prototype blade system technology in order to test and demonstrate the ability of the system to generate energy with low wind velocity. The generation from this facility is used by Vestas at their manufacturing facility, however, Black Hills has an agreement with Vestas for the RECs from this facility. That agreement expires in June 2030.

Peak View Wind Project 3.8.9 The Peak View Wind Project is currently under construction in Huerfano County and Las Animas County, Colorado. The project, when complete, will consist of 34 GE 1.7-100 class wind turbines. The Peak View Wind Project is expected to produce energy at an estimated net capacity factor of approximately 41 percent. Black Hills filed a CPCN Application for the Peak View Wind Project on June 23, 2015 in Proceeding No. 15A-0502E. The Company has entered into a Build Transfer Agreement with Invenergy Colorado LLC. Upon commercial operation of the plant, targeted for November 2016, ownership rights would transfer from Invenergy to Black Hills. The project was approved by the Commission in Decision No. C15-1182.

4.2 Resources Options Conventional resources, renewable energy resources, and purchased power alternatives were analyzed in the evaluation of the resource options for this 2016 ERP. 4.2.1 Conventional A variety of conventional supply-side resource options were available for selection in the modeling. These include natural gas-fired simple-cycle combustion turbines, natural gas-fired combined-cycle units, and reciprocating engines. The natural gas-fired resources were assumed to be built in the Pueblo area at a “greenfield” or undeveloped site. All capital cost estimates used in the modeling are order-of-magnitude overnight estimates with an accuracy level of ±25 percent. All estimates are based on an engineering, procurement, and construction (“EPC”) method of contracting. EPC capital cost estimates are exclusive of owner’s cost and only consider “inside-the-fence” physical assets. Inside-the-fence physical assets begin with interconnects at the plant boundary (fuel, water, etc.) and end at the high side of the generator step-up transformer. An important consideration for siting any generating facility is accessibility to transmission. For natural gas-fired units, additional consideration needs to be given


43

to accessibility to natural gas pipelines and availability of natural gas from the natural gas pipeline. Performance parameter and cost values for modeling conventional resources came from a 2012 study conducted by Black & Veatch for the Company (see Appendix E). This study was reviewed and updated to reflect changes in capital cost and operations and maintenance cost trends since 2012. All resources examined in the Black & Veatch study, with the exception of the 2x1 LMS 100 conversion to combined-cycle unit with duct firing, 3x1 GE LM6000 PF Sprint, and the 100 MW coal unit, were evaluated as resource options in this 2016 ERP. 5.2.2 Combustion Turbine Combustion turbines (“CT”) typically burn natural gas and/or No. 2 fuel oil and are available in a wide variety of sizes and configurations. CTs are generally used for peaking and reserve purposes because of their relatively low capital costs, higher full-load heat rate, and the higher cost of fuel when compared to conventional baseload capacity. Many CTs have the added benefit of providing quick-start and black-start capability in certain configurations. In this analysis, technology options modeled for CTs included LMS 100 (an aeroderivative turbine), LM6000, and a GE 6FA. Parameters used to model each of these CT options are shown on Table 5-2.

Table 5-2 Combustion Turbine Parameters

Parameter LMS 100 LM6000 GE 6FA

Earliest feasible installation 11/2019 8/2019 6/2019

Size, MW (net) - summer 90.2 39.6 64.5

Full load heat rate, Btu/kWh 8,946 9,445 10,999

SO2 Emission Rate, lb/MWh 0.0104 0.0113 0.0136

NOx Emission Rate, lb/MWh 0.1872 0.2038 0.2457

CO2 Emission Rate, lb/MWh 1164 1237 1505

Fixed O&M, $/kW-year (2016 $) 18.32 40.26 25.30

Variable O&M, $/MWh (2016 $) 6.61 7.14 18.78

Forced Outage Rate, % 2.00 2.00 2.00

Maintenance Outage Rate, % 2.00 2.00 2.00

Capital Cost, $/kW (2016 $) 1,130 1,591 1,366

5.2.3 Combined Cycle In a combustion turbine combined-cycle (“CC”) facility, the hot exhaust gases from the combustion turbine pass through a heat recovery steam generator (“HRSG”). The steam generated by the HRSG is expanded through a steam turbine which, in turn, drives an additional generator. CCs typically burn natural gas and are available in a wide variety of sizes and configurations. Parameters for two different CC configurations were included in the modeling; 1x1 CC with duct firing and 2x1


44

CC. These are shown on Table 5-3. The 1x1 CC consists of one gas turbine generator, one steam turbine generator, and one HRSG. The 2x1 CC configuration has two gas turbine-generators and two HRSGs that supply steam through a common header to a separate single steam turbine-generator.

Table 5-3 Combined Cycle Parameters

Parameter 1x1 with

Duct Firing 2x1

Earliest feasible installation 9/2019 9/2019

Size, MW (net) - summer 55.1 102.2

Full load heat rate, Btu/kWh 7,660 7,316

SO2 Emission Rate, lb/MWh 0.0080 0.0078

NOx Emission Rate, lb/MWh 0.0879 0.0860

CO2 Emission Rate, lb/MWh 775 854

Fixed O&M, $/kW-year (2016 $) 38.94 27.20

Variable O&M, $/MWh (2016 $) 7.44 7.44

Forced Outage Rate, % 2.00 2.00

Maintenance Outage Rate, % 2.00 2.00

Capital Cost, $/kW (2016 $) 2,184 1,845

5.2.4 Reciprocating Engines A Wartsila 34 SG lean-burn natural gas engine was modeled in two configurations – as an individual unit (9.1 MW) and in a group of ten units (total capacity 91.4 MW). This four stroke, gas-ignited spark engine can be used for baseload, intermediate and peaking operation. Parameters used to model an individual and group of ten Wartsila gas engines are shown on Table 5-4.

Table 5-4 Wartsila Gas Engine Parameters

Parameter One Unit Ten Units

Earliest feasible installation 6/2019 6/2019

Size, MW (net) - summer 9.1 91.4

Full load heat rate, Btu/kWh 8,982 8,982

SO2 Emission Rate, lb/MWh 0.0096 0.0096

NOx Emission Rate, lb/MWh 0.1736 0.1736

CO2 Emission Rate, lb/MWh 1127 1127

Fixed O&M, $/kW-year (2016 $) 94.07 21.80

Variable O&M, $/MWh (2016 $) 13.49 11.66

Forced Outage Rate, % 2.00 2.00

Maintenance Outage Rate, % 2.00 2.00

Capital Cost, $/kW (2016 $) 2,184 1,806


45

5.2.5 Seasonal Firm Market Purchased Power The Company assumed that, due to its small size relative to the market, it will be able to purchase seasonal firm market power during the summer months. This measure covers any peak demand shortfall and defers the need to install new resources until the need for capacity extends to multiple months. The product would be seasonal firm market power available 6 x 16 (six days per week, sixteen hours per day, 7 am – 11 pm). The model is able to select the seasonal firm market power in 25 MW blocks, up to a total of 75 MW (three blocks) through 2021 and 50 MW (two blocks) beginning in 2022. This seasonal firm market power is priced at the cost of energy at Palo Verde plus a 20 percent premium and transmission adder. The 25-MW block size was selected based on the minimum size of the blocks of power typically available for this type of product in the market.

4.3 Renewables The renewable energy resource technologies that were modeled in this 2016 ERP include PV and wind. A sodium sulfur battery and a waste-to-energy facility were modeled as Section 123 resources. Data for performance and cost parameters for solar and wind technology were gathered from the Company’s 2014 All-Source Solicitation. 5.3.1 PV Solar The Company included three sizes of PV solar facilities for selection in the modeling: a 10 MW, 30 MW and 60 MW option. A PV solar cell is made of semiconducting material, typically wafer-based crystalline silicon technology, configured such that when sunlight hits the cells, the electrons flow through the material and produce electricity. Using data from the bids that were obtained during the Company’s 2014 All-Source Solicitation, Black Hills developed performance parameter and cost assumptions for future PV solar resources. Parameters used to model PV solar, which assume a PPA for solar energy, are included Table 5-5. Recent legislation related to ITC levels for 2016 through 2022 were included in the development of the PV solar cost assumptions.

Table 5-5 PV Solar Performance Parameters

Parameter 10 MW 30 MW 60 MW

Earliest feasible year of installation 5/2018 5/2018 5/2018

Size, MW (net) - summer 10 10 10


46

Capacity Factor, % 29 29 29

Accreditable Capacity, % 37 37 37

Forced Outage Rate, % 0.00 0.00 0.00

Maintenance Outage Rate, % 0.00 0.00 0.00

PPA Energy Cost (First Year $/MWh) 65.35 76.21 50.92

5.3.2 Wind Wind turbines use their blades to collect the kinetic energy of the wind. The blades are connected to a drive shaft that turns an electric generator to produce electricity. Similar to the PV solar performance parameters and costs, the Company used data from bids that were obtained during the Company’s 2014 All-Source Solicitation to develop future wind resource assumptions. Recent legislation related to PTC levels for 2016 through 2022 were included in the development of the wind resource cost assumptions. Parameters used to model wind in this 2016 ERP, which assume a PPA for wind, are shown in Table 5-6.

Table 5-6 Wind Performance Parameters

Parameter 30 MW 60 MW

Earliest feasible year of installation 5/2018 5/2018

Forced Outage Rate, % 0.00

Maintenance Outage Rate, % 0.00

Capacity Factor, % 36.01 36.01

Accreditable Capacity, % 20 20

PPA Energy Cost (First Year $/MWh) 56.21 40.08

4.4 Section 123 Resources

As defined in the RES Rules, Section 123 resources mean new energy technology or demonstration projects, including new clean energy or energy efficient technologies under C.R.S. § 40-2-123 (1)(a), and C.R.S. § 40-2-123 (1)(c), and Integrated Gasification Combined Cycle projects under C.R.S. § 40-2-123(2). Energy storage systems, including sodium sulfur batteries, have the potential to enhance a utility’s ability to integrate renewable energy technologies and increase electric grid reliability and asset use. Energy storage systems allow a utility to move energy production to the hour during the day when it is most beneficial.10 Energy storage systems have significant locational flexibility and, if sited on the distribution system, could provide voltage support and congestion relief, among other benefits.

10 Electricity Energy Storage Technology Options: A White Paper Primer on Applications, Costs, and

Benefits, 1020676, Electric Power Research Institute, Technical Update, December 2010.


47

Much demonstration work still needs to be done with energy storage systems. These demonstration or pilot projects are expected to provide the needed data and information on the robustness of such systems, including performance and durability, life cycle costs, and risks. In addition, such projects are expected to increase interest in such technologies, resulting in lower initial capital costs and increased deployment and adoption of energy storage systems.11 Because of the need for demonstration projects to validate the operational characteristics, costs, and risks associated with the deployment of energy storage technologies, including sodium sulfur batteries, the Company characterizes sodium sulfur batteries as a Section 123 resource. The Company also characterizes a proposed waste-to-energy facility as Section 123 resources. 5.4.1 Sodium Sulfur Battery

Sodium sulfur (“NAS”) batteries consist of liquid (molten) sulfur at the positive electrode and liquid (molten) sodium at the negative electrode. The electrodes are separated by a solid beta alumina ceramic electrolyte. The battery is hermetically sealed with an environment of approximately 300o C. NAS battery round trip efficiency ranks relatively high amongst electrochemical batteries. Depending on parasitic loads, operating temperatures, and applications, efficiency ranges from 70 to 90 percent. NAS batteries typically are charged at night, when demand and costs are relatively low, and are discharged during peak demand when energy costs are relatively high. This effectively shifts some of the peak demand to the hour during the day when it is most beneficial and allows utilities to reduce costs. NAS battery performance parameters are shown in Table 5-7.

Table 5-7

Sodium Sulfur Battery Performance Parameters

Parameter Value

Size, MW (net) – summer and winter 10

Efficiency (%) 75

Lifetime Cycles 4,500

Capital Cost, $/kW (2012 $) 3,775

5.4.2 Waste-to-Energy Facility A waste-to-energy facility was proposed within the Company’s service territory and the developer submitted a bid for this project in the Company’s 2014 All-Source Solicitation. Using data submitted in the bid the Company derived modeling parameters for a 10 MW facility, with an 80 percent capacity factor that would operational in 2019 with PPA pricing. Because this was the only project of its kind

11 Id.


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bid into the 2014 All-Source Solicitation the cost parameter used in the 2016 ERP modeling is confidential pending its public disclosure pursuant to Rule 3613(j). 5.0 Costs and Benefits of Integration for Intermittent Renewable Energy

Resources To comply with the RES requiring that in 2020 thirty percent of retail electricity sales come from renewable energy, the Company anticipates developing or procuring new wind and solar resources in the coming years. The impact that higher levels of wind and solar penetration will have on Black Hills’ operation is important to understand so that appropriate steps can be taken to assure that grid stability is not compromised. In 2015, the Company contracted with Black & Veatch to perform a renewable integration analysis on the Black Hills system. The objectives of the study were two-fold; first, to determine the incremental costs of providing regulation and ancillary services for the integration of future wind and solar resources (referred to in this analysis as Variable Energy Resources, or “VER”s) at various penetration levels; and second, to determine the accreditable capacity of wind and solar resources for reliability planning purposes. The study is contained in Appendix F. The incremental regulation costs for the Company to integrate future levels of renewable resources were determined by first calculating the additional amounts of regulating reserves required to maintain a certain reliability level. Black Hills and Black & Veatch used a conservative approach that mimics existing reliability standards. Specifically, the analysis proxies the NERC Control Performance Standard 2 (CPS2) approach to calculating incremental regulation requirements. Black & Veatch calculated the accreditable capacity of future levels of wind and solar resources utilizing an Effective Load Carrying Capability (“ELCC”) analysis to determine the percentage of the nameplate capacity that can be counted on for reserve margin planning purposes.

5.1 Integration Capacity Needs and Costs In order to assess the impact of additional wind and solar resources on the Company’s operations, assumptions were made for the amount, type, and location of future renewable resources. Locations were chosen based on areas that have received the greatest commercial interest during recent renewable resource RFPs. The cases assessed are outlined in Table 6-1; each location refers to a specific electrical substation in the Black Hills system.


49

Table 6-1 Future Eligible Energy Resources Modeled

Type Location

Total Incremental

MW (AC)

Wind Rattlesnake Butte (“RB”) 60

Wind RB 60 MW, Lamar 30 MW 90



Solar Burnt Mill 30 MW 30

Solar Burnt Mill 30 MW, Nyberg 30 MW 60

Figure 6-1 depicts the monthly average 10-minute incremental reserve requirements for 98 percent NERC CPS2 compliance for various levels of wind and solar resource additions in 2020. The incremental regulating reserve requirements increase more drastically as the renewable levels approach 30 percent of the peak load. Wind and solar integration calculations were performed separately to distinguish the cost of integration independently for each of these resources.

Figure 6-1 Monthly Regulating Reserve Requirement

The integration cost components of the additional regulation reserve requirements were separated into the underlying energy and capacity components. The cost of integration relative to energy was derived from a NERC CPS2 analysis which calculated the ACE deviation at 10 minutes intervals. The cost of regulation was then modeled in the ABB Planning & Risk (“PAR”) production cost model to determine the system energy cost differential of providing regulation up and regulation down.


50

Flex Reserve Service is needed to maintain generation and load balance in the event of a reduction of online wind generation of 100 MW or more in 30 minutes due to reductions in wind speed. This can be met by:

• Generating units that are on-line but unloaded, and/or

• Generating units off-line but capable of starting in 30 minutes. Black Hills has around 450 MW of existing resources that can qualify to provide Flex Reserves. Black Hills is long on Flex Reserve capacity, therefore, the additional cost to the Company to integrate more wind and solar if there is no other use for this Flex Reserve capacity is zero. Black Hills is subject to Public Service Company of Colorado (“Public Service”) Schedule 3 and Schedule 16 tariff rules. The criteria of these FERC tariffs make it difficult for the Company to self‐regulate wind capacity. Public Service Schedules 3 and 16 tariff costs were included in the 2016 ERP modeling for wind only. Based on these tariffs, the Company used the wind integration adders shown in Table 6-2 for 30 MW and 60 MW wind options, escalating at 2.5% annually, in its 2016 ERP modeling.

Table 6-2 Wind Integration Cost Assumptions ($/MWh)

2018

30 MW Wind $4.46

60 MW Wind $4.66

Adding the Regulation Energy and Flexible Reserve costs produces a net integration cost that captures the majority of the expected costs for new wind and solar projects in the Black Hills service territory. This is shown in Table 6-3. While there are a number of other factors that can be taken into account when performing a wind and solar integration analysis, such as increased O&M from regulating units, ancillary service charges, and more frequent ramping of baseload units, these items are difficult to quantify and are typically considered a small overall factor in most integration studies. The scenarios represent incremental additions on top of Black Hills’ existing Busch Ranch Wind Project. The 60 MW wind scenario represents a 90 MW total wind analysis.


51

Table 6-3 Net Integration Costs

Incremental Renewable Scenario

Energy Cost ($/MWh)

Schedule 16 Capacity

Cost ($/MWh)

Total ($/MWh)

60MW Wind $1.23 $4.25 $5.48

90 MW Wind $0.95 $4.07 $5.02

120 MW Wind $0.97 $4.11 $5.08

30 MW Solar $0.96 - $0.96

60 MW Solar $1.22 - $1.22

The Company used the solar integration adders shown in Table 6-3 (energy cost $/MWh) for 30 MW and 60 MW solar options, escalating at 2.5% annually, in its 2016 ERP modeling.

5.2 Accreditable Capacity of Wind and Solar Black & Veatch calculated the accreditable capacity of future levels of wind and solar resources utilizing an ELCC analysis to determine the percentage of the nameplate capacity that can be counted on for reserve margin planning purposes. The ELCC approach takes into consideration the load carrying capability for all 8760 hours in a year. For this analysis Black & Veatch used the ELCC methodology that compares the reliability impact of wind and solar resources to a “perfect generator.” ELCC is calculated based on a monthly Loss of Load Expectation (“LOLE”) metric. LOLE is the amount of time during which system capacity is unable to meet system load. The calculated ELCC at different wind and solar penetrations on the BHCE system can be seen in Figure 6-2.


52

Figure 6-2 Calculated Wind and Solar ELCC

The solar ELCCs are higher than those for wind since their load profiles provide peak output closer to times when Black Hills most needs the power. As more and more solar comes onto the system, the benefit of similar output profiles leads to diminishing returns since the hours that incremental solar can contribute to key loss of load hours is limited. Conversely, wind has a much different output profile, leading to a somewhat flatter ELCC contribution as more capacity is placed on the system. Table 6-4 shows the ELCC for incremental wind and solar additions to the Black Hills system.


53

Table 6-4 Incremental Wind and Solar ELCC

Type Total Incremental

(MW) ELCC (%)

Wind 30 30

Wind 60 27

Wind 90 23

Wind 120 20

Wind 150 19

Solar 30 45

Solar 60 37

Solar 90 31

Solar 120 27

Solar 150 23

A summary of the key findings from the analysis completed by Black & Veatch can be seen in Table 6-5. This table shows the regulating capacity reserves over the current base case as calculated by the CPS2 model, the energy cost from the PAR analysis, the capacity cost per Public Service Schedule 16, and the results of the ELCC analysis. Note that each case represents incremental additions on top of the status quo; thus, the 60 MW wind resource represents a 90 MW total wind analysis from the ELCC results presented in Section 4 of the Variable Integration Cost Study.

Table 6-5 Cost for Integration and ELCC Summary

Case Energy Cost

($/MWh)

Capacity Cost*

($/MWh)

Total Cost

($/MWh)

ELCC (%)

Wind Cases

60 MW $1.23 $4.25 $5.48 23%

90 MW $0.95 $4.07 $5.02 20%

120 MW $0.97 $4.11 $5.08 19%

Solar Cases

30 MW $0.96 -- $0.96 45%

60 MW $1.22 -- $1.22 37% * Uses PSCo Schedule 16 for wind cases, assumes zero cost for self-regulation of solar cases due to excess of flexible capacity.

6.0 Transmission Resources The Company’s service territory generally follows the Arkansas River Valley in Colorado from the Royal Gorge, west of Cañon City, to La Junta, east of Pueblo. The


54

major load centers are the cities of Pueblo and Cañon City with significant smaller load centers in the Cripple Creek area and the area near Rocky Ford. The Company’s transmission system also follows the Arkansas River and consists of 585 miles of 115-kV transmission lines. The Company’s transmission system and the interconnection to neighboring entities is shown in Figure 7-1.

Figure 7-1 Existing Black Hills Transmission System

6.1 Local Transmission Planning Process Black Hills recognizes the importance of stakeholder involvement throughout the transmission planning process, and considers a stakeholder to be any person, group or entity that has an expressed interest in participating in the planning process, is affected by the transmission plan, or can provide meaningful input to the process that may affect the development of the final plan. Stakeholders are encouraged to participate in Black Hills’ transmission planning through the regular meetings held by the Transmission Coordination and Planning


55

Committee (“TCPC”) as part of the annual study process under FERC Order No. 890. The TCPC is an advisory committee consisting of individuals or entities that are interested in providing input to Black Hills’ Transmission Plan. The TCPC study process consists of a comprehensive evaluation of the Black Hills and surrounding transmission systems for critical scenarios throughout the 10-year planning horizon. Stakeholders are notified of the initial meeting at the start of the study cycle and invited to participate. An opportunity is provided to comment on the scope of the study at this point in the process. Relevant system modeling data is requested from the stakeholders, as well as any economic study or alternative scenario requests. The study cases are compiled the data and study scope are finalized. A stakeholder meeting is held to review preliminary study results and discuss potential solutions to any identified problems. This process allows the TCPC to develop a comprehensive transmission plan to meet the needs of all interested parties. A final stakeholder meeting is held as necessary to approve the study report and Local Transmission Plan (“LTP”). Following each meeting, contact information for the transmission planner performing the study is provided to allow for ongoing questions or comments regarding the study process. Updates on the progress of the TCPC study efforts are also provided to regional planning groups, such as the CCPG, to promote involvement from a larger stakeholder body.

6.2 Regional Transmission Planning Process In addition to the local TCPC planning process, Black Hills participates in a wider planning effort at the sub-regional and regional levels through the Western Electricity Coordinating Council (“WECC”), WestConnect, and the Colorado Coordinated Planning Group (“CCPG”). WECC is the forum responsible for coordinating and promoting BES reliability in the entire Western Interconnection. The WECC includes committees that focus on transmission planning. The Transmission Expansion Planning Policy Committee (“TEPPC”) prepares economic models and performs high-level assessments of transmission congestion and expansion needs on an interconnection-wide basis. The Planning Coordination Committee (“PCC”) is responsible for preparing reliability models and performs assessments of the interconnection based on performance standards developed by the North American Electric Reliability Corporation (“NERC”). WestConnect is one of four planning “regions” within WECC established for regional transmission planning to comply with FERC Order No. 1000, Transmission Planning

and Cost Allocation by Transmission Owning and Operating Public Utilities (“Order 1000”). At the end of 2015, WestConnect had 23 members, including 15 Transmission Owners, 5 Independent Transmission Developers, and one Key Interest Group. The WestConnect footprint includes 9 western states. One additional Transmission Owner is expected to join WestConnect in 2016. WestConnect includes 3 sub-regional planning groups (“SPGs”): CCPG, Southwest Area Transmission Group (“SWAT”), and Sierra Subregional Planning Group (“SSPG”).


56

CCPG, which was formed in 1991, is a planning forum that cooperates with state and regional agencies to ensure a high degree of reliability in planning, development and operation of the transmission system in the Rocky Mountain Region. Many CCPG participants are involved in specialized work groups and subcommittees—for example, the Conceptual Planning Work Group (“CPWG”) and the TPL Studies Work Group—which are responsible for conducting technical, environmental, and cost studies for specific projects, focused geographic areas and/or expansion needs. Black Hills and the other Transmission Planners in Colorado have a long history of coordinated transmission planning with each other. Given the integrated nature and ownership of the transmission grid in Colorado, coordinated transmission planning has been commonplace in Colorado before it was a requirement. Internally, and through WestConnect and CCPG, each company performs annual system assessments to verify compliance with reliability standards to determine related system improvements and to demonstrate adherence to the standards and criteria set forth by NERC and WECC. Compliance is certified annually. During the coordinated planning process, a wide range of factors and interests are considered by the companies, including, but not limited to:

• the needs of network transmission service customers to integrate loads and resources;

• transmission infrastructure upgrades necessary to interconnect new generation resources;

• the minimum reliability standard requirements promulgated by NERC and WECC;

• bulk electric system considerations above and beyond the NERC and WECC minimum reliability standard requirements;

• transmission system operational flexibility, which supports economic dispatch of interconnected generation resources; and

• various regional and sub-regional transmission projects planned by other utilities and stakeholders.

This comprehensive internal, regional, and sub-regional planning process ensures that transmission plans continue to be carefully coordinated with all TPs in the State of Colorado.

6.3 Transmission Constraints

There are constraints that exist on the Black Hills transmission system under circumstances of high power transfers combined with certain contingency events. These constraints should be taken into consideration when exploring locations for future generation interconnections. Transfer capability limitations on the Black Hills transmission system as they pertain to the future siting of resources are dependent on the location and size of the resource being proposed. For resources sited external to the Black Hills transmission system, limitations may exist at the


57

interfaces between the Black Hills transmission system and the interconnection points with neighboring transmission systems as shown in Figure 7-2. Proposed resources that are sited within the Black Hills system may experience transfer limitations between the resource location and Black Hills’ load centers. These limitations are generally evaluated on an individual basis within the generator interconnection request process. The transfer capability of the transfer paths on the BHCE transmission system is shown in Table 7-1. The transmission system is currently constrained when transferring power from the Pueblo area into the Cañon City load center, or through the system from Portland to West Cañon. The conceptual West Station-West Cañon 115 kV project will address this constraint in the 2019-2020 timeframe. Siting future generation on the 115 kV or 69 kV system between Portland and Cañon City may help to alleviate this constraint. There is also a general constraint on the Black Hills transmission system when moving power from Baculite Mesa toward Midway under contingency conditions. There are limited paths for energy to flow out of Baculite Mesa, and following the loss of the Baculite Mesa-West Station 115 kV double circuit, the remaining outlet paths become constrained. Future generation additions at Baculite Mesa may require transmission upgrades to maintain unrestricted transfers under contingency conditions. This constraint is dependent on surrounding system conditions as well as the nature of the generation dispatched at Baculite Mesa. Another constraint on the Black Hills transmission system can exist when transferring power to the La Junta/Rocky Ford area under contingency conditions. The loss of the single 115 kV line feeding the area via the Boone substation requires the area load to be served from the single 69 kV line also terminating at Boone. By adding generation in the La Junta/Rocky Ford area, the likelihood of realizing a transmission constraint following a transmission outage is reduced.

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59

Table 7-1

Transfer Path Capability

Path Name Total Transfer

Capability (MW)

Available Transfer

Capability (MW)

Midway-BHCE 295 45

BHCE-Midway 181 58

BHCE-TSGT 74 69

BHCE-Reader 560 360

Reader-BHCE 560 295

BHCE-Canon City West 46 46

Canon City West-BHCE 237 222

BHCE-Boone 176 176

Boone-BHCE 176 141

6.4 Transmission Projects Black Hills annually files with the Commission its planned projects for the next three years in compliance with Rule 3206. In that report, Black Hills identifies the expected in-service date, estimated project cost, location, and compliance with corona noise and magnetic field requirements. The planned and conceptual projects submitted in Black Hills’ 2016 compliance filing are shown in Table 7-2. The Company does not have any proposed transmission additions that are the result of Section 210 of the Federal Power Act or other federal open access transmission (i.e. interconnection or transmission service) requirements.

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62

All of the projects shown in Table 7-2 and included in Black Hills’ 2016 Rule 3206 filing are tentatively planned to be complete and in service during the RAP. Figure 7-3 shows the current Black Hills transmission system with the planned projects in Table 7-2 identified by dashed lines.

Figure 7-3

Planned and Conceptual Black Hills Transmission System

6.5 Senate Bill 07-100 Transmission Projects

Colorado Senate Bill 07-100 (“SB-100”), codified at § 40-2-126(2), requires rate regulated Colorado utilities to perform the following on or before October 31 of each odd-numbered year:

(a) Designate Energy Resource Zones (“ERZ”s); (b) Develop plans for the construction or expansion of transmission facilities

necessary to deliver electric power consistent with the timing of the development of beneficial energy resources located in or near such zones;


63

(c) Consider how transmission can be provided to encourage local ownership of renewable facilities, whether through renewable energy cooperatives as provided in Colo. Rev. Stat. § 7-56-210, or otherwise; and

(d) Submit proposed plans, designations, and applications for Certificates of Public Convenience and Necessity to the Commission for simultaneous review.

The Black Hills 2015 SB-100 Study evaluated resource injections from three ERZs to determine resource injection capability. The point of injection for the evaluated ERZs is shown below:

• Nyberg 115 kV substation (ERZ-2);

• West Cañon 230 kV substation (ERZ-4); and

• Rattlesnake Butte 115 kV substation (ERZ-5).

The 2015 SB-100 Report identified several projects that would increase the ability of the transmission system to accommodate resource injections12 from each ERZ. These projects are described in Table 7-3. The resource injections shown in Table 7-3 are non-simultaneous injections that do not take into consideration potential injections from other ERZs.

12 The 2015 BHCE SB-100 Report considered single contingency events when identifying resource injection capability. Injection capability would be reduced when considering all applicable events as identified in NERC Reliability Standard TPL-001-4.

http://www.blackhillsenergy.com/sites/blackhillsenergy.com/files/bhe_coe_2015_bhce_sb_07_100_report.pdf

http://www.nerc.com/_layouts/PrintStandard.aspx?standardnumber=TPL-001-4&title=Transmission%2520System%2520Planning%2520Performance%2520Requirements


64

Table 7-3 Black Hills 2015 SB-100 Transmission Projects

ERZ 2016 Max Injection

(MW) Facility Upgrade Description

Facility Upgrade Cost ($)

New Max Injection

(MW)

Incremental Injection

Capability (MW)

2 250

Replacing the limiting conductor on the Nyberg-Airport Memorial 115 kV line (5 miles of 336 ACSR 30/7 Oriole) with 795 ACSR Drake increases the rating from 119 MVA to 221 MVA. This project is conceptual and no in-service date has been assigned.

$1.75M 420 170

4 200

Add a second 230/115 kV transformer (100 MVA+) at the West Cañon substation. This project is conceptual and no in-service date has been assigned.

$12M 330 130

4 330

Add a new 40-mile 115 kV transmission line (221 MVA) connecting West Station and West Cañon. The tentative in-service date for this conceptual project is Q4 2019.

$27.1M 400 70

5 219

The planned upgrade of the West Station 115 kV substation (Q4 2018) will increase the thermal rating of the Hyde Park-West Station 115 kV line from 120 MVA to 221 MVA. This is one limitation to achieving a 221 MVA rating on the Reader-Rattlesnake Butte 115 kV line.

$7.8M 221 2

5 219

By upgrading terminal equipment on the Pueblo Plant-Reader 115 kV line, the line’s rating would increase from 160 MVA to 182 MVA. This is the other limitation to achieving a 221 MVA rating on the Reader-Rattlesnake Butte 115 kV line. This project is conceptual and no in-service date has been assigned.

$7.8M 221 2


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7.0 Future Resource Analysis and Selection

7.1 Resource Need The Company developed a load and resource balance to compare its annual peak demand with the annual peak capability of existing resources. The load and resource balance highlights the year in which forecast load exceeds resources and indicates a need for additional generation. The load and resource balance takes into account the planning reserve requirement. Based on the peak demand forecast developed by the Company, including the impacts of the DSM plan and expected large customers load additions, the Company does not have a capacity deficit during the RAP. Based on modest load growth during the entire Planning Period and the addition of the PAGS LM6000 and Peak View Wind Project in 2017, the first year that Black Hills has a capacity deficit is 2029.

The load and resource balance for the RAP, which includes the base load forecast and existing resources, purchases and interruptible contracts, is shown in Table 8-1. The load and resource balance for the entire Planning Period is included in Appendix G.


66

Table 8-1 Load and Resource Balance (2016-2022)

2016 2017 2018 2019 2020 2021 2022

Peak Demand 398 403 408 413 418 417 414

DSM (3.1) (8.5) (14.2) (16.7) (16.7) (16.7) (16.7)

Net Peak Demand 395 395 394 396 401 401 397

Existing Resources **

Pueblo Diesels 10 10 10 10 10 10 10

Airport Diesels 10 10 10 10 10 10 10

Rocky Ford Diesels

10 10 10 10 10 10 10

PAGS 1 90 90 90 90 90 90 90

PAGS 2 90 90 90 90 90 90 90

Busch Ranch Ownership*

3.3 3.3 3.3 3.3 3.3 3.3 3.3

Peak View Wind

13.8 13.8 13.8 13.8 13.8 13.8

PAGS LM6000 40 40 40 40 40 40

Total Resources 213 267 267 267 267 267 267

Purchases and Interruptible Contracts

PAGS PPA 200 200 200 200 200 200 200

MPS 5 5 5 5 5 5 5

Busch Ranch PPA*

3.3 3.3 3.3 3.3 3.3 3.3 3.3

Cargill Purchase 50

Interruptible Contracts

9.5 9.5 5 5 5 5 5

Total Resources and Purchases

481.2 485.0 480.5 480.5 480.5 480.5 480.5

15% Reserve Margin (MW)

59.3 59.2 59.1 59.5 60.1 60.1 59.6

Total Capacity Requirement (Peak plus Reserves

454.6 454.0 452.9 456.0 461.0 460.7 456.6

Total Resources minus Total Capacity Requirement

In MW 26.5 31.0 27.6 24.5 19.5 19.8 23.9

% Excess 6.7 7.9 7.0 6.2 4.9 4.9 6.0 *23% of Busch Ranch and Peak View capacity counts as accredited capacity. **Summer rated capacity shown.


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7.2 Analysis


68

The Company undertook capacity expansion and production costing modeling to determine the portfolio of future resources that meets the needs of its customers over the Planning Period in the least cost manner. Subsequent to those analyses, the Company conducted scenario analysis and risk analysis. Capacity expansion modeling is a process used to determine the appropriate type, size, and timing for economic resource additions for utilities. The utility’s existing generation resources and future resource alternatives are input into a capacity expansion model with a forecasted load. The model simulates utility operation and “serves” the forecasted load with the utility’s existing resources and economically “selects” additional resources from the list of available resource alternatives. The typical criterion for evaluation is the expected present value of revenue requirements (“PVRR”) subject to meeting load plus reserves and various resource planning constraints, such as Colorado’s RES. Production cost modeling simulates the hourly operation of the resources available to a utility and is used to forecast system cost and risk exposure. A production cost model includes an hourly dispatch model, with a load forecast and fixed resources to serve that load. The model simulates a load every hour, then economically serves that load with the available resources, and captures the associated cost. Production cost modeling can also be completed using multiple iterations with changing variables. This form of modeling provides a measure of risk associated with the modeled plan subject to changing variables. Scenario analysis was conducted during which the Capacity Expansion module was used to derive optimal resource expansion plans. The scenarios include variations in inputs representing the significant sources of portfolio cost variability and risk. These inputs include the load forecast, the price of natural gas, and potential enactment of carbon tax or other similar mechanisms. Utilities must plan for the future electricity needs of their customers in an environment of significant uncertainty. Thus, the analysis conducted for this 2016 ERP examined resource needs under a variety of possible future conditions. Stochastic analysis and risk profile compilation were among the risk techniques examined. The stochastic analysis conducted by ABB examined a wide range of uncertainties that resulted in 50 unique future scenarios for price determination and evaluation of a given portfolio of resources. The scenarios are driven by variations in market price drivers (e.g. peak demand and energy forecast, natural gas price, oil price, coal price, unit availability and capital costs) and take into account statistical distributions, correlations and volatilities. Cumulative probability distributions, also known as risk profiles, were used to visually assess the results of the stochastic analysis. All of the deterministic modeling used in the 2016 ERP analysis was performed by Black Hills using ABB’s System Optimizer and PAR. The Company retained ABB to


69

provide analytical services in support of the 2016 ERP. ABB reviewed the capacity expansion and production cost modeling completed by the Company. This included verifying input data from ABB’s 2015 WECC Fall Reference Case and reviewing modeling results. Using the Company’s modeling results, ABB used its Strategic Planning powered by MIDAS Gold® Corporate Finance module to model the financial and risk simulations.

7.3 Base Plan Analysis and Alternative Plans Rule 3604(k) of the Commission’s ERP Rules requires that the resource plan contain:

Descriptions of at least three alternate plans that can be used to represent the costs and benefits from increasing amounts of renewable energy resources, demand-side resources, or Section 123 resources as defined in paragraph 3602(q) potentially included in a cost-effective resource plan. One of the alternate plans shall represent a baseline case that describes the costs and benefits of the new utility resources required to meet the utility’s needs during the planning period that minimize the net present value of revenue requirements and that complies with the Renewable Energy Standard, 4 CCR 723-3-3650 et seq., as well as with the demand-side resource requirements under § 40-3.2-104, C.R.S. The other alternate plans shall represent alternative combinations of resources that meet the same resource needs as the baseline case but that include proportionately more renewable energy resources, demand-side resources, or Section 123 resources. The utility shall propose a range of possible future scenarios and input sensitivities for the purpose of testing the robustness of the alternate plans under various parameters.

The process used to determine the base resource portfolio for the Company over the planning horizon began by examining plans to meet the RES as well as alternative plans with higher levels renewable energy resources or Section 123 resources. As per the Rules, one of these plans shall represent a base case that describes the costs and benefits of the new utility resources required to meet the utility’s needs during the planning period that minimizes the PVRR and that complies with the RES as well as the demand-side resources requirements. The Company refers to this plan as the Base-with-RES Plan.

7.4 Base-with-RES Plan The Base-with-RES Plan was developed in accordance with the following RES requirements:


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Rule 3604(k) states that the baseline case and alternative plans must comply with the RES. That standard requires the Company to provide specific percentages of renewable energy and/or recycled energy according to the following schedule:

• 20% of its retail electricity sales in Colorado for the years 2015-2019; and

• 30% of its retail electricity sales in Colorado for the year 2020 and for each following year.

Black Hills must also have a certain percentage of its retail sales produced by either wholesale DG or retail DG, regardless of technology type, according to the following schedule:

• 1.75% of its retail electricity sales in 2015 and 2016;

• 2% of its retail electricity sales in 2017-2019; and

• 3% of its retail electricity sales in 2020 and each following year. At least one-half of the DG requirement must be generated by retail DG systems located on-site at customers’ facilities or premises. The Colorado RES currently includes credit multipliers for four types of projects. These multipliers cannot be combined. Two of these types of projects are relevant to the Company.

1. For purposes of compliance with the renewable energy standard, each kilowatt-hour of eligible energy generated by an early eligible energy resource shall be counted as 1.25 kilowatt-hours of eligible energy. Busch Ranch Wind Project qualifies as an early Eligible energy resource.

2. Electricity generated at a “community-based project” – a project not greater than 30 MW in capacity that is located in Colorado and owned by individual residents of a community or by an organization or cooperative that is controlled by individual residents, or by a local government entity or tribal council – can receive 150 percent credit for RES-compliance purposes.

The Capacity Expansion model was used to develop the Base-with-RES Plan by using constraint logic to select the optimal resources to meet the RES throughout the Planning Period. The constraint logic was set up to require the model to economically select renewable technologies to meet the RES. The following assumptions were made:

• All existing conventional resources were included as available resources

• Eligible energy resources included: o Busch Ranch PPA and Ownership – 29.04 MW. o Peak View Wind Project – 60 MW. o Existing and authorized On-site Solar – 15 MW. o Vestas 1.8 MW wind turbine.


71

• No carbon tax.

• DSM demand and energy reductions in compliance the Company’s 2016-2018 DSM Plan. The impacts of the DSM plan were assumed to reduce load.

• Firm seasonal market purchases up to 75 MW (2016-2021).

• Firm seasonal market purchases up to 50 MW after 2021.

• RECs were used for compliance with the RES.

• Eligible energy resource options included 30 MW and 60 MW generic wind resources and 10 MW, 30 MW and 60 MW solar resources.

• A waste-to-energy resource and a sodium sulfur battery were included as Section 123 resource options.

• DG requirements were met with existing on-site solar and CSG, proposed solar on-site and CSG in the Company’s 2018 – 2021 RES Compliance Plan, the Vestas 1.8 MW wind turbine and the Busch Ranch Wind Project.

7.5 Alternative Plan 1

Pursuant to Rule 3604(k) two alternative plans were modeled. The first alternative plan (labeled Alternative Plan 1) incorporated the same assumptions as included in the Base-with-RES Plan and the following assumption:

• The level of required Eligible energy resources or Section 123 resources was increased such that the increased requirement could be fulfilled by adding a single facility.

7.6 Alternative Plan 2 The second alternative plan (labeled Alternative Plan 2) incorporated the same assumptions as included in the Base-with-RES Plan and the following assumptions:

• The level of required Eligible energy resources or Section 123 resources was increased such that the increased requirement could be fulfilled by adding multiple facilities.

The resources added over the Planning Period for each of these three plans are shown in Table 8-2. The deterministic PVRRs for these plans are shown in Figure 8-1.


72

Table 8-2 Expansion Plans

Year Base-with-RES Alternative Plan 1

Alternative Plan 2

2016

2017

2018


2020

2021

2022

2023

2024

2025


2027

2028

2029

2030

2031

2032 (2) LMS100, 25

MW SFMP (2) LMS100, 25

MW SFMP (2) LMS100, 25

MW SFMP

2033 25 MW SFMP 25 MW SFMP 25 MW SFMP


2035 25 MW SFMP (2) 10 MW Solar,

25 MW SFMP (2) 10 MW Solar,

25 MW SFMP



2038 60 MW Wind, 25

MW SFMP 60 MW Wind, 25

MW SFMP 60 MW Wind, 25

MW SFMP

2039 25 MW SFMP 25 MW SFMP

Sodium Sulfur Battery 10 MW,

25 MW SFMP

2040 25 MW SFMP 25 MW SFMP

(2) Sodium Sulfur Battery 10 MW,

25 MW SFMP SFMP denotes seasonal firm market power of 25, 50 or 75 MW


73

Figure 8-1 Deterministic 25 Year PVRR (2016-2040)

Source: ABB Advisors

Projected annual capacity factors for existing resources in the Base-with-RES Plan are shown in Table 8-3. Projected emission rates for the Company’s existing resources are found in Confidential Schedule K-16, Appendix K.

Table 8-3 Base-with-RES Plan

Annual Capacity Factors during the Resource Acquisition Period (%)

2016 2017 2018 2019 2020 2021 2022

PAGS CT 1 3.2 3.3 4.7 3.7 3.9 3.9 3.7

PAGS CT 2 0.8 0.5 0.4 0.3 0.5 0.8 0.6

PAGS LM6000 0.0 0.1 0.1 0.2 0.2 0.1 0.1

Pueblo Diesels 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Airport Diesels 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Rocky Ford Diesels 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Busch Ranch Wind Project

38 38 38 38 38 38 38

Peak View Wind Project

41.5 41.5 41.5 41.5 41.5 41.5 41.5

$2,059.8 $2,060.2 $2,065.0

$1,700

$1,800

$1,900

$2,000

$2,100

$2,200

Million $

25 Year PVRR


74

7.7 Retail Rate Impact Analysis

The Base-with-RES Plan that was modeled incorporates all of the additional renewable energy resources that would be required to achieve the RES requirements during the RAP and the Planning Period. This plan adds 60 MW of wind resources in 2019, 30 MW of wind resources in 2026, and 60 MW of wind resources in 2038. However, in order to determine whether this plan complies with the RES, it is necessary to consider the net retail rate impact of this plan under the RES Rules. Rule 3661(a) provides that “the net retail rate impact of actions taken by an investor owned QRU [qualifying retail utility] to comply with the renewable energy standard shall not exceed two percent of the total electric bill annually for each customer of that QRU.” The Company collects a monthly 2 percent surcharge from each of its customers which funds the RESA. This is the money available to pay the net incremental cost of Eligible energy resources. At the end of 2015, the Company had a negative balance in its RESA account of $4,043,450 due to funds advanced by the Company to acquire Eligible energy resources. The Company receives interest at its weighted-average cost of capital on this negative balance. As was demonstrated in the Company’s Peak View Wind Project proceeding, wind resources can be added for no or very little net incremental cost. When wind energy is being generated, the utility generates less from conventional resources. As a result, the utility avoids the cost of fuel (natural gas in the Company’s case), economy energy purchases, and variable O&M costs. Natural gas prices are forecasted to remain low for a number of years which means that the avoided costs of wind will remain low. The Peak View Wind Project modeling showed that the avoided costs associated with the conventional generation that was displaced by the wind resource was greater than the cost of the wind resource. To determine the net incremental cost of Eligible energy resources, the Company compared two scenarios to estimate the costs and benefits of each system over the RES Planning Period. (Rule 3661(h)). The first scenario is a “RES plan” that reflects the utility’s plans and actions to acquire new Eligible energy resources necessary to meet the RES. This is the Base-with-RES Plan in this 2016 ERP. The second scenario is a “No-RES plan,” which reflects the utility’s resource plan that models the new Eligible energy resources in the RES plan with new nonrenewable resources reasonably available. Net incremental cost is determined over a ten-year RES Planning Period. Rule 3661(f). In the Company’s 2018-2021 RES Compliance Plan (Attachment LS-2), the Company has presented a RES/No-RES comparison for the addition of the 60 MW of wind resources in 2019. To determine the net incremental cost of the 60 MW of wind in the Base-with-RES Plan, the following two portfolios were compared:


75

o Base-with-RES and 2019 60 MW Wind Resource - This plan includes the 60 MW Wind Resource in 2019 and the Eligible energy resources that have been locked-down in prior proceedings.

o No-RES Plan - This model includes all of the Company’s existing

conventional resources and the Eligible energy resources that have been locked-down in prior proceedings.

The portfolios were compared through computer modeling so that the benefits associated with the addition of wind could be captured. Those benefits are the avoided costs of fossil fuel expense, purchased power expense, and variable O&M production expense. The RES/No-RES Comparison showed that the addition of 60 MW of wind energy in 2019 would provide approximately $74 million of avoided cost savings over the ten-year RES Planning Period, from 2019 through 2027. The cost of the 60 MW of wind over the same time period, 2019 through 2027, is projected to be $76 million. Thus, the addition of 60 MW of a wind resource in 2019 would result in a net incremental cost of approximately $2 million over the ten year RES Planning Period. Over the 2016 ERP Planning Period (2016-2040), the modeling shows that the 60 MW wind resource in 2019 would provide approximately $305 million of avoided cost savings from 2019 through 2040. The cost of the 60 MW of wind resource over the same time period, 2019 through 2040, is projected to be $235.6 million. Thus, the addition of 60 MW of wind energy in 2019 would result in a net incremental savings of approximately $69.3 million over the 25 year 2016 ERP Planning Period. The Company used the same methodology to calculate the net incremental cost of the two other Eligible energy resources identified in the Base-with-RES Plan – a 30 MW wind resource in 2026 and 60 MW wind resource in 2038. The Company compared a model that included only the 30 MW wind resource in 2026 to a model that excluded all Eligible energy resources except Eligible energy resources that have been locked-down in previous proceedings. This same comparison was completed for the 60 MW wind resource in 2038. Tables A-1, A-2, A-3, and A-4 in Appendix A shows the avoided cost calculations associated with the proposed Eligible energy resources in 2019, 2026, and 2038, the impact of the proposed resources on the Company’s RESA balance and the Company’s REC compliance with the addition of the proposed Eligible energy resources.


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Table 8-4 Incremental Cost/Saving of Eligible Energy Resources

($ Million)

Avoided Costs

Costs Incremental Cost/Saving

2019 60 MW Wind $304.9 $235.6 $69.3

2026 60 MW Wind $127.8 $157.3 $29.4

2038 60 MW Wind $70.7 $63.4 $7.3

Over the 25-year 2016 ERP Planning Period, the retail rate impact of the addition of the 60 MW wind resource in 2019, 30 MW wind resource in 2026, and the 60 MW wind resource in 2038 is shown in Schedule A-4 in Appendix A. The Company forecasts that the RESA will have a positive balance by 2020 if the Company meets all of its existing and authorized REC obligations and the Commission approves the Company’s proposed 2018-2021 solar programs and the acquisition of up to 60 MW of Eligible energy resources in 2019 through a solicitation process. Schedule A-4 also shows that over the course of the 2016 ERP Planning Period the Company will have sufficient RESA funds to acquire Eligible energy resources in 2026 and 2038.

7.8 Scenario Analysis Scenario analysis was conducted during which the Capacity Expansion module was used to derive optimal resource expansion plans. The scenarios include variations in inputs representing the significant sources of portfolio cost variability and risk. These scenarios and a brief description of the scenario variables are listed below:

1. Base-with-RES Plan

• See Section 8.4 above 2. Environmental Scenario

• Same build assumptions as Base-with-RES Plan

• CO2 emissions prices from ABB’s CO2 Tax scenario

• Gas and market prices from ABB’s CO2 Tax scenario 3. High Load Scenario


• High load forecast 4. Low Load Scenario


• Low load forecast 5. High Gas Scenario


• Gas and market prices from ABB’s High Gas scenario 6. Low Gas Scenario


77


• Gas and market prices from ABB’s Low Gas scenario 7. NYMEX Gas Price Scenario


• Gas prices from the Company’s NYMEX gas price forecast (describe in Section 3.4.1)

The electric market price, natural gas price and CO2 tax cost forecasts used during the scenario analysis are included in Confidential Schedules K-1 through K-15, Appendix K. Capacity Expansion modeling results (resource portfolios) for these scenarios are shown in Table 8-5. The PVRRs for the scenario analysis are shown on Figure 8-2.


78

Table 8-5 Optimal Expansion Plans – Scenario Analysis

Year Base-

with-RES Low Load

High Load

Low Gas Price

High Gas Price

NYMEX Gas Price

Environmental

2016

2017

2018 60 MW Wind

2019 60 MW Wind

60 MW Wind

60 MW Wind

60 MW Wind

60 MW Wind

60 MW Wind

2020

2021

2022

2023

2024 25 MW SFMP

2025 25 MW SFMP

2026 30 MW Wind

30 MW Wind

30 MW Wind, 25

MW SFMP 30 MW Wind

30 MW Wind

30 MW Wind

30 MW Wind

2027 25 MW SFMP

2028 50 MW SFMP

2029 50 MW SFMP

2030 50 MW SFMP

2031 50 MW SFMP

25 MW SFMP

2032

(2) LMS100, 25 MW SFMP

LMS100, 50 MW SFMP

(3) LMS100

65 MW 6FA, LMS100, 50 MW SFMP




2033 25 MW SFMP

50 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

2034 25 MW SFMP

50 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

2035 25 MW SFMP

50 MW SFMP

25 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

2036 25 MW SFMP

50 MW SFMP

25 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

2037 50 MW SFMP

50 MW SFMP

25 MW SFMP

50 MW SFMP

50 MW SFMP

50 MW SFMP

50 MW SFMP

2038

60 MW Wind, 25

MW SFMP

60 MW Wind, 50

MW SFMP

60 MW Wind, 25

MW SFMP

60 MW Wind, 50

MW SFMP

60 MW Wind, 25

MW SFMP

60 MW Wind, 25

MW SFMP

60 MW Wind, 25

MW SFMP

2039 25 MW SFMP

50 MW SFMP

25 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

2040 25 MW SFMP

50 MW SFMP

50 MW SFMP

50 MW SFMP

25 MW SFMP

25 MW SFMP

25 MW SFMP

SFMP denotes seasonal firm market power of 25, 50 or 75 MW


79

Figure 8-2

Base-with-RES Plan and Scenarios – Deterministic PVRRs (2016-2040)

Source: ABB Advisors

7.9 Risk Analysis

Utilities must plan for future customer needs for electricity in an environment of significant uncertainty. Thus, the analysis conducted for the 2016 ERP examined uncertainty under a variety of possible future conditions. Stochastic analysis and risk profile compilation were among the risk techniques examined. 8.9.1 Stochastic Analysis ABB’s PAR includes a stochastic model to simulate volatility in electricity, fuel prices, and loads. PAR has a regression tool that uses historical data to calculate the stochastic properties of these variables, including their volatility, short-term mean-reversion, and the correlations among the random time-series. Monte Carlo simulation is then performed, with sample random draws from specified distributions. The power simulation model uses these random paths to optimize commitment and dispatch along each random path. Also the PAR stochastic modeling framework allows the user to specify for each stochastic entity either a normal or lognormal distribution. Load was modeled with a normal distribution

$2,059.8 $2,059.8 $2,062.9 $2,053.2

$2,132.1

$2,009.9

$2,059.8 $2,060.2 $2,065.0

$1,700

$1,800

$1,900

$2,000

$2,100

$2,200

$2,300

Million $

25 Year PVRR


80

while the market prices and natural gas prices were modeled with a lognormal distribution. In PAR three different variable types are set as stochastic variables: load, market price and natural gas price. Stochastic runs were performed using 50 iterations in PAR for the 9 different scenarios. The model calculated the short-term volatilities, shown in Table 8-6 and mean reversion rates shown in Table 8-7 for the stochastic variables. Correlations between the variables were also calculated and are shown in Table 8-8, Table 8-9 and Table 8-10. During the stochastic evaluations, the prices and associated uncertainties provide sufficient information about the market to allow for proper evaluation of alternatives. For example, high gas prices would generally result in high on-peak prices for market power.

Table 8-6 Short-Term Volatilities

BHCE Load

Electric Price AZ-

PV

Electric Prices CO-East Natural Gas Price

January 0.039 0.083 0.199 0.083

February 0.036 0.169 0.088 0.140

March 0.038 0.126 0.117 0.116

April 0.036 0.123 0.050 0.086

May 0.043 0.087 0.094 0.032

June 0.060 0.136 0.146 0.0670

July 0.053 0.115 0.174 0.071

August 0.043 0.077 0.105 0.019

September 0.056 0.059 0.117 0.052

October 0.038 0.119 0.092 0.158

November 0.037 0.057 0.077 0.092

December 0.036 0.228 0.221 0.198

(Source: ABB Advisors)


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Table 8-7 Mean Reversion Rates

BHCE Load

Electric Price AZ-

PV

Electric Prices CO-East

Natural Gas Price

January 0.241 0.000 0.000 0.000

February 0.112 0.000 0.124 0.014

March 0.252 0.299 0.323 0.202

April 0.203 0.101 0.055 0.000

May 0.287 0.300 0.000 0.257

June 0.175 0.101 0.220 0.000

July 0.220 0.488 0.327 0.311

August 0.222 0.559 0.377 0.077

September 0.162 0.000 0.481 0.092

October 0.282 0.608 0.350 0.389

November 0.179 0.000 0.000 0.000

December 0.198 0.113 0.509 0.000 (Source: ABB Advisors)

Table 8-8

Load Correlations

Electric Price AZ-PV


Natural Gas Price

January 0.763 -0.181 0.245

February -0.534 0.155 -0.614

March 0.389 -0.618 -0.252

April -0.477 -0.259 -0.517

May 0.300 -0.830 -0.129

June 0.817 -0.506 -0.225

July 0.540 0.571 0.761

August -0.118 -0.452 -0.308

September 0.171 0.211 -0.360

October -0.266 -0.469 -0.275

November -0.033 -0.671 -0.048

December -0.118 -0.158 0.249 (Source: ABB Advisors)


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Table 8-9 AZ-PV Market Price Correlations

AZ-PV Correlations


Natural Gas Price

January 0.219 0.480

February 0.301 0.951

March -0.400 0.388

April 0.323 0.822

May -0.107 0.443

June -0.243 -0.299

July -0.282 0.113

August -0.122 0.535

September 0.668 0.480

October 0.847 0.956

November -0.270 0.623

December 0.591 0.895 (Source: ABB Advisors)

Table 8-10

CO-East Market Price Correlations

CO-East Correlations

Natural Gas Price

January 0.643

February 0.318

March 0.328

April 0.560

May 0.539

June 0.440

July 0.847

August 0.252

September 0.528

October 0.728

November 0.236

December 0.584 (Source: ABB Advisors)

In addition, using Strategic Planning’s Stratified Monte Carlo sampling program, ABB created 50 future scenarios for portfolio capital cost evaluation. Uncertainty


83

draws were made for the capital cost of the resource additions in the portfolio evaluation. These capital cost draws were combined with the uncertainty draws from the PAR runs. The uncertainties shown in Table 8-11 were examined in the 2016 ERP and resulted in 50 future scenarios for price development and portfolio evaluation.

Table 8-11 Uncertainty Variable Range Multipliers

Uncertainty

Uncertainty Range

Multiplier Combustion Turbine Capital Cost 1.0-1.10

Combined Cycle Capital Cost 1.0-1.10

Reciprocating Engine Capital Cost 1.0-1.15

Wind Capital Cost 1.0-1.20

Battery Capital Cost 0.9-1.10

Each scenario was designed to maintain a minimum 15 percent reserve margin. However, when exposed to demand uncertainty in the risk analysis, there are certain scenarios where the expansion plan did not meet the 15 percent reserve margin. In the stochastic scenarios that had a reserve margin deficit, the installed capacity (“ICAP”) price from the 2015 WECC Fall Reference case was used as a proxy for the capacity market to meet the target 15 percent reserve margin 8.9.2 Risk Profiles

During the stochastic analysis, the expansion plans optimized for each case remain the same. The analysis examines the costs of each expansion plan assuming 50 different “futures” and tabulates the PVRR expected for each of those 50 futures. A risk profile for each expansion plan is then constructed using all 50 of those “future” PVRR points. For this study, ABB combined the Company’s PAR production results with the Strategic Planning financial results. Figure 8-3 shows the risk profiles for the Base-with-RES Plan, Alternative Plan 1, Alternative Plan 2, and the scenarios. In Figure 8-3, with the exception of the low load and low gas scenario, the Base-with-RES plan is the closest to the left and has the lowest PVRR in all years. The capacity expansion module develops resource expansion plans over a 30-year horizon. Because the study period was only 25 years, the impact of the capacity deficit for the Low Load scenario in later years was not included in the PVRR, therefore making it a lower cost plan than the Base-with-RES Plan. The Low Gas scenario builds one LMS 100 and a 6FA unit in 2032 instead of two LMS 100s included in the Base-with-RES Plan. The PVRRs in these two cases are within 0.3 percent of each other. The


84

benefit of the LMS 100 over the 6FA is not fully captured in the study period because they were both brought on-line late in the study period. One can view the risk profile to determine the probability that PVRR will be a particular value. Using the Base-with-RES Plan as an example in Figure 8-3, there is a 90 percent probability that PVRR could be as much as $2.112 billion with an expected value of $2.068 billion. From the prior deterministic simulation, the PVRR value was $2.060 billion under “base case” conditions. The $8 million difference between the expected value and the deterministic value is “real option value” or extrinsic value. This reflects the risk of the Base-with-RES Plan with future uncertainty.

Att

ach

me

nt

LS

-1

20

16

ER

P

85

Fig

ure

8-3

R

isk

Pro

file

s –

All

Pla

ns

an

d S

cen

ari

os

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% $1,900

$1,950

$2,000

$2,050

$2,100

$2,150

$2,200

$2,250

Cumulative Probability

Present Value of Revenue Requirements (Millions $)

Risk Profiles

Base

Environmental

High Gas

Low Gas

High Load

Low Load

Nymax Gas

Base Alt 1

Base Alt 2


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7.10 Preferred Plan

As a result of the load and resource balance, capacity expansion, production cost modeling, retail rate impact evaluations and risk analysis, Black Hills is recommending a Preferred Plan that does not include the addition of any new capacity resources during the RAP. However, based on bid pricing that were received in the Company’s 2014 All-Source Solicitation, the Preferred Plan does include the addition of 60 MW of wind resources in 2019. The Company used bid data submitted in the 2014 All-Source Solicitation as inputs in its modeling and based on the bid prices, the forecasted cost of natural gas and the forecasted electric prices the model identified a 60 wind resource in 2019 as an economical option for energy. In addition, with the acquisition of 60 MW of wind resources in 2019 Black Hills will be able to acquire all of the RECs required by the RES Statute and RES Rules through 2025. By 2026, the Company will likely need to acquire additional Eligible energy resources in order to stay in compliance with the RES. However, based on the Commission’s current electric resource planning and RES rules these additions will be considered in future resource plans. Beyond the RAP, Black Hills’ load and resource balance shows a small capacity deficit in 2029 growing by a few megawatts each year until the beginning of 2032 when the Company’s contract for 200 MW of generation expires. The modeling identified an optimal portfolio to replace this expiring contract, however, based on current Commission electric resource planning rules the Company will be required to complete at least two resource plans prior to considering the appropriate replacement capacity for this contract. Black Hills’ Preferred Plan recommends that the Company engage in a Phase II competitive solicitation to acquire up to 60 MW of Eligible energy resources by 2019. This solicitation and subsequent analysis will allow the Company to determine if Eligible energy resources can be acquired at a cost that will provide savings for customers, meet the requirements of the retail rate impact and generate sufficient RECs such that Black Hills will comply with the RES standard through 2025. The planning assumptions used in this 2016 ERP will underlie the evaluation of proposals received in response to a Company solicitation in a Phase II of this 2016 ERP Proceeding. The Company has included a list of General Planning Assumptions in Appendix H that were used in the 2016 ERP modeling and will be used in a solicitation process. These assumptions represent “base case” assumptions. Sensitivity analysis will be performed in which certain of these assumptions are altered in accordance with any Commission directives. The Company has indicated in the General Planning Assumptions table those assumptions that will be updated for the evaluation of proposals.


87

7.11 2018 through 2021 RES Compliance Plan

The Company is filing, concurrently with this 2016 ERP, its 2018 through 2021 RES Compliance Plan. The RES Plan is being filed by Black Hills pursuant to the RES established by the RES Statute and implemented by the RES Rules. The RES Plan details how the Company will comply with the RES Rules covering compliance years 2018 through 2021, the RAP and the 10-year RES Compliance Period.


88

8.0 Contingency Plan Rule 3609(c) requires the utility to develop contingency plans for the RAP and to provide, under seal, a description of its proposed contingency plans for the acquisition of (1) additional resources if actual circumstances deviate from the most likely estimate of future resource needs developed pursuant to Rule 3610, or (2) replacement resources in the event that resources are not developed in accordance with a Commission-approved plan under Rule 3617. 13 Black Hills’ Preferred Plan does not include the addition of any new capacity resources during the RAP therefore a contingency plan for the acquisition of new resources or replacement resources in the event that resources are not developed in accordance with a Commission-approved plan is not required. Black Hills has sufficient existing resources and resources that are currently under construction to supply the projected capacity and energy needs of its customer through the RAP. 9.0 RFPs and Model Contracts As required by Rule 3604(i), the Company is filing in Volume III of this 2016 ERP the proposed RFP(s) and model contracts the Company intends to use to solicit Section 123 resources and Eligible energy resources discussed in Section 6.3 of this 2016 ERP.

13 Black Hills is not filing contingency plans under seal because the 2016 ERP does not include the addition of any new capacity resources during the RAP. To the extent this would require a waiver of Rule 3609(c), such a waiver is hereby requested.


89

10.0 Confidential and Highly Confidential Information

As required by Rule 3604(j), the Company must provide a list of the information related to the resource plan proceeding that the utility claims is confidential and a list of the information related to the resource plan proceeding that the utility claims is highly confidential. The utility shall also list the information that it will provide to owners or developers of a potential resource under Rules 3613(a) and (b). The utility shall further explicitly list the protections it proposes for bid prices, other bid details, information concerning a new resource that the utility proposes to build and own as a rate base investment, other modeling inputs and assumptions, and the results of bid evaluation and selection. The protections sought by the utility for these items shall be specified in the motion(s) submitted under Rule 3603(b). For good cause shown, the utility may seek to protect additional information as confidential or highly confidential by filing the appropriate motion under Rule 1100 of the Commission’s Rules of Practice and Procedure in a timely manner.

10.1 Public Information The following information that is relevant to the 2016 ERP is, or will be, public information as the result of the Company filing the information in Phase I or Phase II of the 2016 ERP or as the result of a prior filing with the Commission, the State of Colorado or with federal agencies: 11.1.1 Company Information

• Annual Sales

• Annual Revenue

• Resource Need for RAP

• RES Status

• RESA o Balance o Forecast

• Peak Demand and Energy Forecast o Annual / Monthly Peak Demand o Annual / Monthly Total Energy Demand

• Total DSM Costs

11.1.2 Purchased Generation Resource Information

• Capacity

• Contract Duration

• Contract Modification Terms 11.1.3 Model Input Data


90

• Depreciation and Amortization Expense

• Capacity

• Average Heat Rate

• Fuel Type

• Expected Retirement Date

• Contract Duration

• Contract Modification Terms

• Inflation/Escalation Rate

• Federal Tax Rate

• State Tax Rate

• Discount Rate

• Weighted-Average Cost of Capital

• Wind Integration Costs

• Solar Integration Costs

• DSM Forecast

• Reserve Margin Requirements

• Spinning Reserve Requirement

• In-Service Dates

• Unit Capacities

• PPA In-service Dates

• PPA Retirement Dates

• PPA Capacities

• Generic Resources o Name Plate Capacity o Summer Peak Capacity o Capital Costs o Book Life o Fixed O&M o Variable O&M o Heat Rate Curves o Forced Outage Rates o Typical Annual Maintenance Requirements o CO2 Emission Rate o NOx Emission Rate o SO2 Emission Rate o PPA Pricing if applicable

11.1.4 Modeling Output Data

• Annual System Capacity Obligation

• Total System Capacity

• Capacity Additions (Expansion Plans)

• Capacity Retirements


91

• Total Emissions by Type

• Unit Emissions by Type

• Total Fuel Consumed

• Capacity Factors

• System Emissions o CO2 o SO2 o NOx o Mercury

• Average Cost per-kWh modeling output

• Total System Present Value of Revenue Requirements The models developed for the Company’s 2016 ERP contain thousands of data points that were used to represent the Company’s system. Model inputs are not contained in files that would be easily understood or manipulated. Specific questions concerning data input will receive an informative response. Worksheets developed by the Company for provision to the modeling vendor and output worksheets can be provided. 11.0 Confidential Information The Company will seek to protect the following proprietary information as confidential information:

11.1 Modeling Input Data

• Hourly Load Patterns

• Monthly On/Off Peak Market Prices

• Market Emission Assumptions

• Unit Variable O&M

• Unit Fixed O&M

• Fuel Costs

• Unit Contribution to Spinning Reserve

• SO2 Pricing

• NOx Pricing

• CO2 Pricing

• Unit Emission Rates o SO2 o NOx o CO2 o Mercury

• PPA Capacity Pricing (subject to contractual limitations)

• PPA Energy Pricing (subject to contractual limitations)

• PPA Energy Schedules (subject to contractual limitations)

• PPA Contribution to Spinning Reserves


92

• PPA Emission Rates o CO2 o SO2 o NOx o Mercury

• Hourly Wind Patterns

• Hourly Solar Patterns

• Generic Renewable Resources o Capital Costs o Fixed O&M o Variable O&M

11.2 Modeling Output Data

• Unit Level Generation

• Unit Level Capacity Factors

• Unit Level Fuel Consumed

• Unit Level Average Heat Rate

• Unit Level Total Variable O&M

• Unit Level Fixed O&M

• Fuel Cost o Coal Cost Projection o Gas Cost Projection

• Capacity

• Energy Purchased

• Cost of Energy Purchased

• Unit Level Emissions o NOx o SO2 o CO2 o Mercury

• PPA Maximum Capacities

• PPA Summer Accredited Capacities

• PPA Accredited Capacities

• PPA Generation

• PPA Capacity Factors

• PPA Total Energy Payments (subject to contractual limitations)

• PPA Total Capacity Payments (subject to contractual limitations)

• PPA Emissions o NOx o SO2 o CO2 o Mercury


93

12.0 Highly Confidential Information The Company considers the following proprietary information as highly confidential information:

• Unit Level Delivered Fuel Costs

• Hourly Market Price Data

• Unit Level Heat Rate Curves

• Unit Detailed Maintenance Schedules

• Bid Information of any sort (from the Company and from other entities)

• Pricing and any other commercially sensitive information regarding a PPA

• Certain Modeling Files

The Company believes that disclosure of the items listed above can cause irreparable harm to the Company’s trading operations, the Company’s ability to solicit cost-effective resources, and, ultimately, the Company’s customers. The Company will seek to limit access in accordance with the Commission’s rules. 13.0 Information that the Company will Provide Bidders The Company will provide the following information developed by the Company to bidders with respect to their own bids after initial bid screening and before Phase II modeling:

• Transmission Interconnection Costs 14.0 Implementation of Separation Policy The Company will implement a separation policy in the event that one of its affiliates decides to submit a bid in a Phase II acquisition process. 15.0 Protection of Bid Information, Modeling Inputs and Assumptions, and

Bid Evaluation Results The Company will seek to protect all bid information and bid evaluation results (including Company self-build proposals) that would reveal specific bid pricing or other bid information, as highly confidential information in accordance with the Commission’s rules, until completion of the resource acquisition process, i.e. until the last contract for a resource that meets a portion of the 2016 ERP resource need is signed. In accordance with Commission Rule 3613(k), upon completion of the resource acquisition process, the Company will post on its website the following bid information:

• Bidder Name

• Bid Price (Utility Cost for Utility–Owned Bid Proposals)


94

• Generation Technology Type

• Size of Facility

• Contract Duration (Expected Useful Life of Utility Resource)

• Purchase Option Details as relevant In accordance with the ERP Rule 3613(j), within fourteen months after completion of the resource acquisition process, the Company will make public confidential information that was redacted from testimony and reports by re-filing the testimony or reports in an un-redacted form. If any Company highly confidential modeling inputs and assumptions listed above under highly confidential information are entered into the record in any manner, the Company will seek to indefinitely continue the confidentiality protections ordered by the Commission.


95

16.0 Water Usage The Company’s generation facilities vary in their water consumption. Table 16-1 identifies the actual gallons consumed by the Company’s existing facilities in 2015 and the gallons consumed per MWh, also known as water intensity, for the current generation fleet.

Table 16-1

Water Resources – Existing Generating Facilities

Unit Name Fuel

Type

2015

MWh

Annual

Water

Consumption

(gallons)

Water Intensity

(gallons/MWh)

PAGS CT 1 Nat Gas 29,413 8,443,512 287.1

PAGS CT 2 Nat Gas 79,438 22,810,726 287.2

Pueblo Diesels #2 Oil Negligible Negligible Negligible

Airport Diesels #2 Oil Negligible Negligible Negligible

Rocky Ford Diesels

#2 Oil Negligible Negligible Negligible

Annual Total 31,254,238

Table 16-2 shows the expected annual water consumption for conventional resources that were included in the analysis of future resources in this 2016 ERP. Water consumption values were forecasted assuming a 5 percent capacity factor and 30 percent capacity factor for some of the possible resources and a 30 percent and 70 percent capacity factor was assumed for some resources.


96

Table 16-2

Water Resources – Potential Generating Facilities

Unit Name Fuel

Type

Annual Water

Consumption

5% Capacity

Factor

(Gallons)

Annual Water

Consumption

30% Capacity

Factor

(Gallons)

LMS 100 Nat Gas 5,870,000 35,200,000

LM 6000 Nat Gas 1,470,000 8,810,000

GE 6FA Nat Gas 1,390,000 8,340,000

Wartsila (1 unit)

Nat Gas 130,000 790,000

Wartsila (10 units)

Nat Gas 530,000 3,150,000

Unit Name Fuel

Type

Annual Water

Consumption

30% Capacity

Factor

(Gallons)

Annual Water

Consumption

70% Capacity

Factor

(Gallons)

CC 1x1 with DF Nat Gas 62,200,00 145,000,000

CC 2x1 Nat Gas 95,300,000 222,000,000


97

Appendix A

Estimated Net Incremental Cost Tables

Schedule A-1: 2016-2040 Estimated Avoided Costs and Net Incremental Cost of 2019 60 MW Wind Resource



Schedule A-4: Source and Use of Funds Available for Eligible Energy Acquisition


98

Appendix B

WECC Standard BAL-002-WECC-2 Guidance Document


99

Appendix C

Econometric Load Forecast Methodology

Multiple combinations of variables were tested in the development of the energy and demand forecasts. The models were refined to ensure that the estimates were logically reasonable (e.g., sales increase with CDDs) and statistically significant (or approaching statistical significance). Normal weather conditions are used to forecast energy and demand. Summaries of the final energy and demand equations are described in this appendix.

Peak Demand Forecast Methodology The Company’s system demand forecast is a system-level forecast inclusive of residential, commercial, industrial, and lighting sectors. Each month’s peak hours from 2006 to 2015 were used to model the monthly peak demand forecast. The peak demand model was estimated using Ordinary Least Squares (“OLS”). The resulting estimates were used in combination with normal weather and forecasted economic conditions to forecast peak demands. The model is as follows: �� !"� � �#$�%&'()(* �

��+�, -�.�/� �0 �( ��+��(, � .23

(43

Where:

�� = natural logarithm; �� = demand use without identified large volume customers; �� = cooling degree days, 60-degree threshold; �� = peak-hour cooling degree hours, 70-degree threshold; �� = heating degree days, 60-degree threshold; �� = peak-hour heating degree hours, 50-degree threshold; �� = monthly cooling degree daily average, 60-degree threshold; !" = gross regional product; �+�, -�.�/ = nonfarm employment; and �+��(, = indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. As the Black Hills service territory experiences hot and cold-weather peaks, the CDD, HDD, and MnthCDD variables account for cooling and heating buildup, while the CDH and HDH variables account for peak-hour cooling and heating requirements. The month variables account for seasonal differences in peak demands not due to weather, and the GRP and Nonfarmemp variables reflect how peak demand changes with economic conditions. The historical and forecasted values for these variables are included in Schedule C-6, Appendix C. The variable


100

statistical values are included in Schedule C-7, Appendix C. Schedule C-8 in Appendix C provides the resulting monthly demand forecast.

Residential Sales Forecast The residential sales forecast is the product of a forecast of residential use per customer (“UPC”) and a forecast of the number of residential customers served. Black Hills evaluated the following potential drivers of residential UPC: the residential price of electricity, personal income per household, heating degree days, cooling degree days, and the month of the year. Higher electricity prices and lower income may result in less electricity use, while colder than normal winters (more heating degree days) and hotter than normal summers (more cooling degree days) may result in more electricity consumption. The final residential UPC model is as follows: ��5/� � � �� 6�7 � ��/��89�_12�=� �

0 �( ��+��(, � .23

(43

Where:

�� = natural logarithm; 5/ = residential energy use per customer; �� = cooling degree days, 60-degree threshold; �� = heating degree days, 60-degree threshold; /��89�_12�= = 12-month moving average of real personal income per

household; and �+��(, = indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. As the Black Hills service territory experiences hot- and cold-weather peaks, the CDD and HDD variables account for cooling- and heating-related usage. The month variables account for seasonal differences in residential energy use per customer not due to weather, and the personal income variable reflects how residential UPC changes with income levels. The historical and forecasted values for these variables are included in Schedule C-10, Appendix C.14 The variable statistical values are included in Schedule C-11, Appendix C.

14 All models except the system peak demand model are estimated using the Prais-Winsten estimator. (The system peak demand model is estimated using OLS. The use of Prais-Winsten in that model was prevented by the inclusion of multiple observations in some months.) The Prais-Winsten estimator is intended to account for the effects of first-order serial correlation in the error term, which means that the error in a specific period of time is related to the error in the prior period of time. A failure to account for serial correlation (as in OLS estimation) does not bias the estimated coefficients, but it can result in inefficient estimates (higher standard errors).


101

The model for the number of customers is as follows: ��5>�>� � � ��?@ � ��8_12�=� � . Where:

�� = natural logarithm; 5>�> = residential customers; and ��8_12�= = 12-month moving average of the number of households.

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. The number of households variable explains the growth of the residential customers. The historical and forecasted values for these variables are included in Schedule C-12, Appendix C. The variable statistical values are included in Schedule C-13, Appendix C. The residential UPC and number of customers forecasts are converted from logs to levels and then multiplied together to produce the forecast residential sales as follows: �> �.>� � expE��5/�F � expE��5>�>�F Schedule C-8 and Schedule C-9, in Appendix C provides the resulting monthly and annual residential sales forecast, respectively. Commercial Sales Forecast The commercial sales forecast was completed using the same methodology as the residential sales forecast, by multiplying the commercial UPC model results by the number of commercial customers model results. Black Hills evaluated the following drivers of commercial UPC: the price of electricity, employment, regional economic expansion or contraction (“GRP”), weather and the month of the year. The final commercial UPC model is as follows:

��5/� � � �� !"_12�=� � �*'G7) �

��/-9.� �0 �( ��+��(, � .23

(43

Where:

�� = natural logarithm; 5/ = commercial energy use per customer; �� = cooling degree days, 60-degree threshold; �� = heating degree days, 60-degree threshold; !"_12�= = 12-month moving average of gross regional product; /-9. = average commercial revenue per-kWh sold; and �+��(, = indicator variables for each month.


102

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. As the Black Hills service territory experiences hot- and cold-weather peaks, the CDD and HDD variables account for cooling- and heating-related usage. The month variables account for seasonal differences in commercial energy use per customer not due to weather. The gross regional product variable reflects how energy changes with economic conditions, and the price variable demonstrates how changes in retail energy prices affect commercial energy consumption. The historical and forecasted values for these variables are included in Schedule C-14, Appendix C. The variable statistical values are included in Schedule C-15, Appendix C. Schedule C-8 and C-9, in Appendix C provides the resulting annual commercial sales amounts, respectively. The model for the number of commercial customers served is as follows:

��5>�>� � � �� !"_12�=� � �G() � �9�. �0 �( �23

(43�+��(, � . Where: �� = natural logarithm; 5>�> = commercial customers; !"_12�= = 12-month moving average of gross regional product; �9�. = monthly time trend; and �+��(, = indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. The month variables account for seasonal differences in commercial customers not due to weather. The gross regional product variable reflects how commercial customers change with economic conditions, and the time trend variable captures the historical trend of the commercial customer count, controlling for the other included variables. The historical and forecasted values for these variables are included in Schedule C-16, Appendix C. The variable statistical values are included in ScheduleC-17, Appendix C. The resulting commercial sales forecast is as follows: �> �.>� � expE��5/��F � expE��5>�>�F Schedule C-8 and Schedule C-9, in Appendix C provides the resulting monthly and annual commercial sales forecast, respectively.


103

Industrial Sales Forecast

The industrial sales forecast was developed from a single model. Variables that could impact industrial sales include the price of electricity, weather, employment, regional economic expansion or contraction and the month of the year. Higher electricity prices can result in less electricity use, while higher employment as well as colder than normal winters (more heating degree days) and hotter than normal summers (more cooling degree days) could result in more electricity consumption. The final industrial sales model is as follows:

��> �.>�� !"_12�=� �0 �( �23

(43�+��(, � . Where: �� = natural logarithm; > �.>�� = industrial sales without identified large volume customers; �� = cooling degree days, 60-degree threshold; !"_12�= = 12-month moving average of gross regional product; and �+��(, = indicator variables for each month.

In this equation, a and the b’s are estimated parameters; et is the error term; and t indexes observations. As the Black Hills service territory experiences hot-weather peaks, the CDD variable accounts for cooling-related usage. The month variables account for seasonal differences in industrial energy use not due to weather, and the gross regional product variable reflects how energy changes with economic conditions. The historical and forecasted values for these variables are included in Schedule C-18, Appendix C. The variable statistical values are included in Schedule C-19, Appendix C. Schedule C-8 and C-9, Appendix C provides the resulting monthly and annual industrial sales forecast, respectively.


104

Appendix D

Load Profiles

Table D-1 and Figure D-1 Residential Daily Load Profiles (kW) – January Table D-2 and Figure D-2 Residential Daily Load Profiles (kW) – February Table D-3 and Figure D-3 Residential Daily Load Profiles (kW) – March Table D-4 and Figure D-4 Residential Daily Load Profiles (kW) – April Table D-5 and Figure D-5 Residential Daily Load Profiles (kW) – May Table D-6 and Figure D-6 Residential Daily Load Profiles (kW) – June Table D-7 and Figure D-7 Residential Daily Load Profiles (kW) – July Table D-8 and Figure D-8 Residential Daily Load Profiles (kW) – August Table D-9 and Figure D-9 Residential Daily Load Profiles (kW) – September Table D-10 and Figure D-10 Residential Daily Load Profiles (kW) – October Table D-11 and Figure D-11 Residential Daily Load Profiles (kW) – November Table D-12 and Figure D-12 Residential Daily Load Profiles (kW) – December Table D-13 and Figure D-13 Commercial Daily Load Profiles (kW) – January Table D-14 and Figure D-14 Commercial Daily Load Profiles (kW) – February Table D-15 and Figure D-15 Commercial Daily Load Profiles (kW) – March Table D-16 and Figure D-16 Commercial Daily Load Profiles (kW) – April Table D-17 and Figure D-17 Commercial Daily Load Profiles (kW) – May Table D-18 and Figure D-18 Commercial Daily Load Profiles (kW) – June Table D-19 and Figure D-19 Commercial Daily Load Profiles (kW) – July Table D-20 and Figure D-20 Commercial Daily Load Profiles (kW) – August Table D-21 and Figure D-21 Commercial Daily Load Profiles (kW) – September Table D-22 and Figure D-22 Commercial Daily Load Profiles (kW) – October Table D-23 and Figure D-23 Commercial Daily Load Profiles (kW) – November Table D-24 and Figure D-24 Commercial Daily Load Profiles (kW) – December Table D-25 and Figure D-25 Industrial Daily Load Profiles (kW) – January Table D-26 and Figure D-26 Industrial Daily Load Profiles (kW) – February Table D-27 and Figure D-27 Industrial Daily Load Profiles (kW) – March Table D-28 and Figure D-28 Industrial Daily Load Profiles (kW) – April Table D-29 and Figure D-29 Industrial Daily Load Profiles (kW) – May Table D-30 and Figure D-30 Industrial Daily Load Profiles (kW) – June Table D-31 and Figure D-31 Industrial Daily Load Profiles (kW) – July Table D-32 and Figure D-32 Industrial Daily Load Profiles (kW) – August Table D-33 and Figure D-33 Industrial Daily Load Profiles (kW) – September Table D-34 and Figure D-34 Industrial Daily Load Profiles (kW) – October Table D-35 and Figure D-35 Industrial Daily Load Profiles (kW) – November Table D-36 and Figure D-36 Industrial Daily Load Profiles (kW) – December


105

Figure D-1

Residential Daily Load Profiles – January

Table D-1

Residential Daily Load Profiles (kW) - January

Hour System Peak Day Average Weekday Average

Weekend/Holidays

1 0.8105 0.7564 0.9383

2 0.7795 0.7251 0.8937

3 0.7720 0.7132 0.8671

4 0.7800 0.7151 0.8557

5 0.8124 0.7420 0.8597

6 0.8836 0.8115 0.8820

7 0.9767 0.9226 0.9223

8 1.0318 0.9554 0.9883

9 1.0220 0.9078 1.0461

10 0.9734 0.8915 1.0805

11 0.9308 0.8740 1.0917

12 0.9110 0.8599 1.0883

13 0.8807 0.8418 1.0587

14 0.8465 0.8156 1.0250

15 0.8255 0.8051 1.0059

16 0.8437 0.8279 1.0128

17 0.9425 0.9280 1.0923

18 1.1388 1.0955 1.2385

19 1.2176 1.1477 1.2680

20 1.2077 1.1287 1.2451

21 1.1859 1.1002 1.2095

22 1.1196 1.0304 1.1428

23 1.0161 0.9203 1.0406

24 0.9163 0.8174 0.9364

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - January

System Peak Day Average Weekday Average Weekend/Holidays


106

Figure D-2

Residential Daily Load Profiles – February

Table D-2

Residential Daily Load Profiles (kW) - February


Weekend/Holidays

1 0.7788 0.7235 0.6980

2 0.7646 0.6947 0.6641

3 0.7578 0.6841 0.6482

4 0.7596 0.6847 0.6442

5 0.7849 0.7114 0.6557

6 0.8496 0.7816 0.6877

7 0.9478 0.8943 0.7479

8 0.9923 0.9124 0.8306

9 0.9876 0.8541 0.8893

10 0.9747 0.8264 0.9233

11 0.9611 0.8031 0.9327

12 0.9779 0.7856 0.9336

13 0.9757 0.7671 0.9241

14 0.9678 0.7430 0.9083

15 0.9870 0.7369 0.9055

16 1.0248 0.7561 0.9269

17 1.1128 0.8302 0.9755

18 1.2380 0.9698 1.0576

19 1.3131 1.0638 1.1106

20 1.2915 1.0584 1.0860

21 1.2459 1.0353 1.0567

22 1.1602 0.9723 0.9929

23 1.0412 0.8686 0.8938

24 0.9397 0.7721 0.7949

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - February



107

Figure D-3

Residential Daily Load Profiles – March

Table D-3

Residential Daily Load Profiles (kW) - March


Weekend/Holidays

1 0.8030 0.6376 0.6432

2 0.7722 0.6048 0.6081

3 0.7644 0.5919 0.5876

4 0.7674 0.5943 0.5897

5 0.7945 0.6183 0.5988

6 0.8643 0.6841 0.6288

7 0.9879 0.7927 0.6861

8 1.0316 0.8243 0.7582

9 0.9989 0.7778 0.8029

10 0.9838 0.7584 0.8189

11 0.9638 0.7438 0.8128

12 0.9385 0.7302 0.8020

13 0.9293 0.7172 0.7887

14 0.9175 0.6989 0.7709

15 0.9145 0.6916 0.7572

16 0.9454 0.7044 0.7654

17 1.0291 0.7590 0.7954

18 1.1587 0.8393 0.8431

19 1.2677 0.8984 0.8868

20 1.2663 0.9455 0.9343

21 1.2398 0.9522 0.9398

22 1.1656 0.8961 0.8796

23 1.0479 0.7938 0.7789

24 0.9450 0.6940 0.6786

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - March



108

Figure D-4

Residential Daily Load Profiles – April

Table D-4

Residential Daily Load Profiles (kW) - April


Weekend/Holidays

1 0.5238 0.5495 0.5771

2 0.4847 0.5142 0.5391

3 0.4654 0.4988 0.5213

4 0.4604 0.4987 0.5162

5 0.4798 0.5202 0.5250

6 0.5432 0.5867 0.5554

7 0.6476 0.7005 0.6177

8 0.6578 0.7231 0.6920

9 0.6120 0.6790 0.7467

10 0.6003 0.6631 0.7732

11 0.5952 0.6520 0.7766

12 0.6048 0.6465 0.7768

13 0.6202 0.6411 0.7730

14 0.6305 0.6299 0.7571

15 0.6544 0.6299 0.7480

16 0.6986 0.6506 0.7585

17 0.7814 0.7094 0.7865

18 0.8593 0.7749 0.8220

19 0.8890 0.8089 0.8367

20 0.8905 0.8428 0.8699

21 0.9242 0.8806 0.9047

22 0.8786 0.8298 0.8457

23 0.7600 0.7230 0.7413

24 0.6333 0.6180 0.6344

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - April



109

Figure D-5

Residential Daily Load Profiles – May

Table D-5

Residential Daily Load Profiles (kW) - May


Weekend/Holidays

1 0.5269 0.5437 0.5570

2 0.4874 0.5046 0.5153

3 0.4660 0.4855 0.4940

4 0.4622 0.4804 0.4835

5 0.4788 0.4981 0.4891

6 0.5361 0.5579 0.5145

7 0.6401 0.6606 0.5646

8 0.6642 0.6866 0.6242

9 0.6306 0.6564 0.6848

10 0.6248 0.6546 0.7297

11 0.6239 0.6547 0.7497

12 0.6223 0.6559 0.7613

13 0.6200 0.6561 0.7613

14 0.6086 0.6486 0.7548

15 0.6094 0.6518 0.7591

16 0.6327 0.6749 0.7788

17 0.7058 0.7375 0.8181

18 0.7723 0.8032 0.8507

19 0.8013 0.8270 0.8455

20 0.8064 0.8357 0.8477

21 0.8540 0.8668 0.8805

22 0.8272 0.8287 0.8460

23 0.7165 0.7221 0.7376

24 0.5973 0.6119 0.6235

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - May



110

Figure D-6

Residential Daily Load Profiles – June

Table D-6

Residential Daily Load Profiles (kW) - June


Weekend/Holidays

1 0.7786 0.6935 0.7171

2 0.6870 0.6227 0.6400

3 0.6328 0.5803 0.5927

4 0.6009 0.5552 0.5623

5 0.5934 0.5531 0.5486

6 0.6189 0.5856 0.5571

7 0.6697 0.6420 0.5960

8 0.7294 0.6880 0.6689

9 0.7816 0.7138 0.7601

10 0.8752 0.7702 0.8578

11 1.0035 0.8466 0.9602

12 1.1569 0.9411 1.0690

13 1.3098 1.0388 1.1765

14 1.4306 1.1186 1.2637

15 1.5292 1.1906 1.3083

16 1.6293 1.2553 1.3320

17 1.7229 1.3157 1.3573

18 1.8045 1.3592 1.3708

19 1.7908 1.3405 1.3459

20 1.6825 1.2698 1.2879

21 1.5851 1.2155 1.2250

22 1.4606 1.1479 1.1538

23 1.2532 0.9872 0.9965

24 1.0531 0.8218 0.8341

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - June



111

Figure D-7

Residential Daily Load Profiles – July

Table D-7

Residential Daily Load Profiles (kW) - July


Weekend/Holidays

1 0.8180 0.7683 0.7778

2 0.7280 0.6895 0.6927

3 0.6735 0.6392 0.6394

4 0.6370 0.6087 0.6035

5 0.6267 0.6010 0.5861

6 0.6474 0.6262 0.5903

7 0.6931 0.6737 0.6265

8 0.7498 0.7233 0.7045

9 0.8133 0.7666 0.8104

10 0.9152 0.8358 0.9300

11 1.0557 0.9282 1.0693

12 1.2182 1.0371 1.2269

13 1.3654 1.1473 1.3792

14 1.4738 1.2321 1.4996

15 1.5699 1.3041 1.5557

16 1.6650 1.3657 1.5439

17 1.7629 1.4208 1.5177

18 1.8377 1.4456 1.5093

19 1.8437 1.4025 1.4622

20 1.7536 1.3272 1.3709

21 1.6435 1.2615 1.2822

22 1.5113 1.1862 1.1865

23 1.2776 1.0288 1.0768

24 1.0584 0.8650 0.9491

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - July



112

Figure D-8

Residential Daily Load Profiles – August

Table D-8

Residential Daily Load Profiles (kW) - August

Hour System Peak

Day

Average

Weekday

Average

Weekend/Holidays 1 0.7368 0.7467 0.8022

2 0.6712 0.6739 0.7219

3 0.6341 0.6327 0.6687

4 0.6094 0.6025 0.6336

5 0.6100 0.5981 0.6158

6 0.6383 0.6280 0.6184

7 0.6944 0.6868 0.6492

8 0.7450 0.7193 0.7197

9 0.7912 0.7425 0.8232

10 0.8761 0.8049 0.9364

11 0.9847 0.8936 1.0650

12 1.1296 1.0071 1.2064

13 1.2761 1.1285 1.3479

14 1.3949 1.2374 1.4669

15 1.5036 1.3365 1.5624

16 1.5847 1.4215 1.6313

17 1.6874 1.5065 1.6582

18 1.7823 1.5595 1.6312

19 1.7840 1.5218 1.5490

20 1.6826 1.4269 1.4468

21 1.5871 1.3487 1.3670

22 1.4289 1.2163 1.2442

23 1.2038 1.0352 1.0666

24 1.0074 0.8689 0.9001

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - August



113

Figure D-9

Residential Daily Load Profiles – September

Table D-9

Residential Daily Load Profiles (kW) - September



2 0.6265 0.5948 0.6222

3 0.5865 0.5581 0.5765

4 0.5643 0.5379 0.5482

5 0.5646 0.5399 0.5365

6 0.6041 0.5805 0.5413

7 0.6869 0.6655 0.5702

8 0.6993 0.6810 0.6283

9 0.6957 0.6614 0.7008

10 0.7370 0.6861 0.7742

11 0.8186 0.7311 0.8519

12 0.9310 0.8018 0.9505

13 1.0606 0.8900 1.0650

14 1.1901 0.9812 1.1751

15 1.3121 1.0767 1.2814

16 1.4255 1.1792 1.3801

17 1.5472 1.2925 1.4621

18 1.6153 1.3525 1.5067

19 1.5637 1.3209 1.4480

20 1.4148 1.2579 1.3568

21 1.3143 1.1841 1.2630

22 1.1636 1.0532 1.1125

23 0.9762 0.8924 0.9421

24 0.8181 0.7457 0.7844

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - September



114

Figure D-10

Residential Daily Load Profiles – October

Table D-10

Residential Daily Load Profiles (kW) - October

Hour System Peak

Day Average Weekday

Average


2 0.5137 0.5040 0.5204

3 0.4866 0.4846 0.4971

4 0.4711 0.4787 0.4856

5 0.4680 0.4945 0.4883

6 0.4828 0.5504 0.5092

7 0.5223 0.6516 0.5562

8 0.5943 0.6850 0.6327

9 0.6696 0.6533 0.7001

10 0.7293 0.6499 0.7384

11 0.7747 0.6496 0.7540

12 0.8265 0.6544 0.7676

13 0.8979 0.6621 0.7823

14 0.9777 0.6654 0.7948

15 1.0667 0.6812 0.8168

16 1.1557 0.7175 0.8532

17 1.2209 0.7942 0.8963

18 1.2505 0.8733 0.9346

19 1.2371 0.9273 0.9651

20 1.2063 0.9370 0.9635

21 1.1070 0.8973 0.9148

22 0.9832 0.8225 0.8370

23 0.8224 0.7151 0.7303

24 0.6806 0.6115 0.6250

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - October



115

Figure D-11

Residential Daily Load Profiles – November

Table D-11

Residential Daily Load Profiles (kW) - November

Hour System Peak

Day

Average

Weekday

Average


2 0.8009 0.6289 0.6948

3 0.7759 0.6176 0.6757

4 0.7652 0.6208 0.6701

5 0.7693 0.6475 0.6824

6 0.7933 0.7171 0.7242

7 0.8444 0.8243 0.7984

8 0.9312 0.8320 0.9104

9 1.0118 0.7795 1.0101

10 1.0687 0.7554 1.0814

11 1.1029 0.7391 1.1219

12 1.1311 0.7260 1.1292

13 1.1533 0.7147 1.0922

14 1.1504 0.6985 1.0287

15 1.1508 0.6984 0.9857

16 1.1971 0.7258 0.9720

17 1.2916 0.8374 1.0094

18 1.3804 1.0026 1.0785

19 1.3445 1.0356 1.0861

20 1.2901 1.0156 1.0726

21 1.2623 0.9879 1.0542

22 1.1634 0.9215 0.9992

23 1.0454 0.8212 0.9054

24 0.9032 0.7292 0.8193

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - November



116

Figure D-12

Residential Daily Load Profiles – December

Table D-12

Residential Daily Load Profiles (kW) - December

Hour System Peak

Day Average Weekday

Average


2 0.8481 0.7674 0.8165

3 0.8344 0.7517 0.7907

4 0.8374 0.7516 0.7824

5 0.8618 0.7752 0.7926

6 0.9096 0.8379 0.8249

7 0.9833 0.9374 0.8897

8 1.0341 0.9680 0.9895

9 1.0242 0.9241 1.0556

10 1.0200 0.9013 1.0921

11 1.0091 0.8782 1.1055

12 0.9975 0.8571 1.1117

13 0.9777 0.8336 1.0862

14 0.9615 0.8073 1.0421

15 0.9579 0.8018 1.0130

16 0.9787 0.8334 1.0182

17 1.1052 0.9686 1.0908

18 1.3038 1.1632 1.2048

19 1.3395 1.2002 1.2239

20 1.3082 1.1912 1.2102

21 1.2713 1.1690 1.1890

22 1.2000 1.1014 1.1227

23 1.1002 0.9865 1.0170

24 0.9732 0.8745 0.9071

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

RESIDENTIAL - December



117

Figure D-13

Commercial Daily Load Profiles – January

Table D-13

Commercial Daily Load Profiles (kW) - January

Hour System Peak

Day Average Weekday

Average


2 6.1261 5.8699 6.1266

3 6.0457 5.8538 6.0926

4 6.2040 5.8683 6.0867

5 6.4215 6.0924 6.2159

6 6.9293 6.5651 6.4338

7 7.6661 7.3650 6.7587

8 8.1668 8.0170 6.7821

9 8.6650 8.5445 6.8392

10 8.9056 8.7653 6.8796

11 9.1134 8.8651 6.9539

12 9.0290 8.7866 6.8803

13 8.9893 8.6784 6.7874

14 9.0052 8.6698 6.6939

15 8.8358 8.5581 6.6577

16 8.6361 8.3921 6.6124

17 8.3828 8.1631 6.7920

18 8.4246 8.0820 7.2278

19 8.1037 7.7733 7.1621

20 7.8140 7.4676 6.9531

21 7.5394 7.1941 6.8179

22 7.1290 6.8066 6.6051

23 6.7021 6.3705 6.3418

24 6.4638 6.1065 6.1093

The System Peak Day was January 22, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - January



118

Figure D-14

Commercial Daily Load Profiles – February

Table D-14

Commercial Daily Load Profiles (kW) - February

Hour System Peak

Day Average Weekday

Average


2 6.2925 5.9196 5.7674

3 6.2357 5.8805 5.7322

4 6.2962 5.8736 5.7384

5 6.4552 6.1019 5.8418

6 6.9201 6.6009 6.0496

7 7.6581 7.4185 6.3988

8 8.1050 7.9612 6.3973

9 8.6603 8.5285 6.5716

10 8.9019 8.7102 6.7459

11 8.9794 8.8161 6.8910

12 9.0182 8.7575 6.9232

13 8.8777 8.6799 6.8930

14 8.9510 8.6998 6.8435

15 8.8629 8.5941 6.8366

16 8.8397 8.4392 6.8278

17 8.5332 8.1539 6.8965

18 8.3425 7.9680 7.1206

19 8.2453 7.8635 7.1846

20 8.0405 7.5771 6.9967

21 7.7541 7.2743 6.7895

22 7.4096 6.8920 6.5143

23 6.9530 6.4158 6.2278

24 6.7682 6.1484 5.9922

The System Peak Day was February 26, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - February



119

Figure D-15

Commercial Daily Load Profiles – March

Table D-15

Commercial Daily Load Profiles (kW) - March

Hour System Peak

Day Average Weekday

Average


2 5.9627 5.4522 5.3280

3 5.9032 5.4162 5.3113

4 5.8864 5.4204 5.3067

5 6.0657 5.5938 5.4216

6 6.5342 6.1050 5.6298

7 7.3239 6.8338 5.9358

8 7.8994 7.3841 5.9290

9 8.5725 7.9296 6.0557

10 8.8382 8.1300 6.2129

11 8.9456 8.2714 6.3771

12 8.8235 8.2506 6.4283

13 8.6558 8.1949 6.4477

14 8.7224 8.2758 6.4390

15 8.6356 8.2462 6.3998

16 8.4555 8.0275 6.3848

17 8.0881 7.6876 6.3906

18 7.8735 7.2974 6.4091

19 8.0208 7.1289 6.3807

20 7.7576 7.1674 6.4497

21 7.4265 6.8933 6.2712

22 7.0738 6.5314 5.9908

23 6.6639 6.0385 5.6929

24 6.4530 5.7368 5.4728

The System Peak Day was March 4, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - March



120

Figure D-16

Commercial Daily Load Profiles – April

Table D-16

Commercial Daily Load Profiles (kW) - April

Hour System Peak

Day Average Weekday

Average


2 5.3928 5.2893 5.1851

3 5.2545 5.2346 5.1318

4 5.2771 5.2443 5.1298

5 5.3881 5.4279 5.1908

6 5.8041 5.9029 5.4653

7 6.3829 6.5354 5.7037

8 7.1949 7.1898 5.6787

9 7.8706 7.8157 5.8950

10 8.3393 8.0510 6.1041

11 8.7786 8.2501 6.2827

12 8.9583 8.3049 6.3353

13 9.1603 8.3163 6.3415

14 9.3814 8.4343 6.4210

15 9.4760 8.4181 6.3785

16 9.3763 8.2194 6.2947

17 8.9635 7.8384 6.2432

18 8.2991 7.2989 6.1945

19 7.8937 6.9532 6.1352

20 7.6537 6.9359 6.2342

21 7.4926 6.8453 6.2012

22 6.9820 6.4436 5.9934

23 6.3886 5.9383 5.7384

24 5.9339 5.6382 5.3734

The System Peak Day was April 30, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - April



121

Figure D-17

Commercial Daily Load Profiles – May

Table D-17

Commercial Daily Load Profiles (kW) - May

Hour System Peak

Day Average Weekday

Average


2 5.2673 5.2998 5.1213

3 5.1564 5.2212 5.0480

4 5.1734 5.2179 5.0676

5 5.3743 5.4051 5.1555

6 5.8263 5.8372 5.3488

7 6.4774 6.4323 5.4124

8 7.2846 7.1799 5.6732

9 7.9977 7.8611 5.9015

10 8.4184 8.2218 6.1618

11 8.5750 8.4396 6.3764

12 8.5381 8.4955 6.5200

13 8.5887 8.4986 6.6052

14 8.7077 8.6233 6.6688

15 8.7228 8.5960 6.6632

16 8.5398 8.3884 6.5222

17 8.1129 7.9277 6.4382

18 7.5785 7.4179 6.2981

19 7.2402 7.0672 6.1718

20 6.9690 6.8987 6.0924

21 7.0215 6.8753 6.2310

22 6.5078 6.4731 5.9869

23 6.0299 5.9718 5.6378

24 5.6859 5.6506 5.3867

The System Peak Day was May 13, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - May



122

Figure D-18

Commercial Daily Load Profiles – June

Table D-18

Commercial Daily Load Profiles (kW) - June

Hour System Peak

Day Average Weekday

Average


2 6.2396 5.9042 5.8421

3 6.0553 5.7713 5.6997

4 6.0186 5.7190 5.6144

5 6.1986 5.8577 5.6392

6 6.5206 6.2249 5.7559

7 7.2672 6.8161 5.9128

8 8.2322 7.7271 6.2995

9 9.2830 8.6231 6.8041

10 9.9907 9.2289 7.3248

11 10.6721 9.7370 7.7876

12 11.0298 10.0290 8.1066

13 11.3552 10.2355 8.2864

14 11.5966 10.4933 8.3879

15 11.6251 10.5340 8.3709

16 11.5141 10.3640 8.2328

17 11.0537 9.8940 8.1079

18 10.4057 9.2159 7.9867

19 9.7030 8.5980 7.7374

20 9.2678 8.2167 7.4888

21 9.0235 8.0309 7.3856

22 8.4948 7.6044 7.1134

23 7.7845 6.9396 6.6306

24 7.3010 6.4526 6.2089

The System Peak Day was June 30, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - June



123

Figure D-19

Commercial Daily Load Profiles –July

Table D-19

Commercial Daily Load Profiles (kW) - July

Hour System Peak

Day Average Weekday Average Weekend/Holidays

1 6.8716 6.5007 6.4300

2 6.5769 6.3065 6.1727

3 6.3947 6.1483 6.0038

4 6.3227 6.0648 5.9243

5 6.4322 6.2275 5.9118

6 6.9052 6.6692 6.0655

7 7.4925 7.2389 6.1772

8 8.4856 8.1056 6.5530

9 9.5434 9.0223 7.1031

10 10.3344 9.6648 7.6582

11 11.1053 10.1875 8.1465

12 11.4957 10.4670 8.5299

13 11.7535 10.6334 8.7428

14 12.0227 10.8553 8.9100

15 12.0552 10.8718 8.8942

16 11.8646 10.6995 8.6869

17 11.4009 10.2209 8.4944

18 10.6573 9.4336 8.3267

19 10.2268 8.8070 8.0622

20 9.7114 8.4519 7.8197

21 9.4828 8.3249 7.7920

22 8.9058 7.8948 7.5082

23 8.0655 7.2494 7.0469

24 7.4763 6.7778 6.6820

The System Peak Day was July 23, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - July



124

Figure D-20

Commercial Daily Load Profiles – August

Table D-20

Commercial Daily Load Profiles (kW) - August

Hour System Peak

Day Average Weekday

Average


2 6.1814 6.2140 6.2684

3 6.0379 6.0530 6.0664

4 6.0162 5.9832 5.9798

5 6.1286 6.1281 6.0010

6 6.6034 6.6026 6.2196

7 7.1996 7.2814 6.3766

8 8.1430 8.1006 6.6661

9 9.1000 9.0320 7.1890

10 9.7939 9.6958 7.7193

11 10.4248 10.2706 8.2231

12 10.8481 10.6219 8.6124

13 11.1087 10.8050 8.8046

14 11.4591 11.1141 8.9674

15 11.6141 11.2043 9.0600

16 11.5397 11.0879 9.0483

17 11.1335 10.6071 8.9416

18 10.4179 9.8306 8.6363

19 9.8857 9.1770 8.2430

20 9.4290 8.7975 8.0267

21 9.1090 8.5574 7.9228

22 8.4249 7.9538 7.5015

23 7.6651 7.2372 7.0025

24 7.2856 6.7939 6.6149

The System Peak Day was August 12, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - August



125

Figure D-21

Commercial Daily Load Profiles – September

Table D-21

Commercial Daily Load Profiles (kW) - September

Hour System Peak

Day Average Weekday

Average


2 6.2642 5.9548 5.8813

3 6.1271 5.7809 5.7101

4 5.9905 5.7343 5.6604

5 6.0879 5.8619 5.7061

6 6.5382 6.3410 5.9283

7 7.3477 7.1451 6.1842

8 8.2061 7.8651 6.2702

9 9.2971 8.7784 6.5965

10 9.9856 9.4473 7.1074

11 10.6635 10.0042 7.5725

12 11.1658 10.3677 7.9637

13 11.3677 10.5632 8.2390

14 11.7025 10.8472 8.4352

15 11.7443 10.9109 8.5838

16 11.5256 10.7943 8.6794

17 11.0297 10.2912 8.6274

18 10.2980 9.5521 8.4474

19 9.5352 8.9317 8.0481

20 9.0480 8.7023 7.9162

21 8.6966 8.2691 7.6491

22 8.0664 7.6562 7.2034

23 7.2666 6.9639 6.6778

24 6.8462 6.5278 6.2950

The System Peak Day was September 1, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - September



126

Figure D-22

Commercial Daily Load Profiles – October

Table D-22

Commercial Daily Load Profiles (kW) - October

Hour System Peak Day Average Weekday Average Weekend/Holidays

1 5.3914 5.3201 5.2704

2 5.2530 5.1998 5.1348

3 5.1443 5.0912 5.0092

4 5.0415 5.0754 4.9645

5 5.0563 5.2391 5.0055

6 5.2679 5.7137 5.2127

7 5.5745 6.5118 5.5594

8 5.6618 7.1824 5.6521

9 5.9187 7.7791 5.8081

10 6.2806 8.0947 6.0404

11 6.7100 8.3770 6.2991

12 6.9534 8.5133 6.5029

13 7.1127 8.5933 6.6047

14 7.2385 8.7864 6.7219

15 7.4396 8.8011 6.7740

16 7.5775 8.6529 6.8506

17 7.6112 8.2555 6.8180

18 7.5036 7.7214 6.6907

19 7.3416 7.5109 6.6678

20 7.0824 7.2948 6.5834

21 6.7112 6.9026 6.3036

22 6.3987 6.4497 5.9875

23 6.0036 5.9073 5.6666

24 5.7227 5.5984 5.3962

The System Peak Day was October 11, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - October



127

Figure D-23

Commercial Daily Load Profiles – November

Table D-23

Commercial Daily Load Profiles (kW) - November


1 5.9725 5.6257 5.7786

2 5.8903 5.5455 5.5880

3 5.8891 5.4751 5.5190

4 5.9000 5.5185 5.5065

5 5.9795 5.7291 5.6107

6 6.1553 6.2408 5.8549

7 6.3457 6.9837 6.0988

8 6.2841 7.4884 6.0443

9 6.4337 8.0362 6.1133

10 6.6259 8.2679 6.2341

11 6.8001 8.4324 6.3061

12 6.8195 8.4497 6.3161

13 6.7534 8.3936 6.2978

14 6.6945 8.4775 6.2599

15 6.6559 8.3860 6.2425

16 6.6775 8.2077 6.2401

17 6.8720 7.9780 6.4098

18 7.0697 7.8564 6.6553

19 6.8849 7.5356 6.5834

20 6.7470 7.2670 6.4407

21 6.6271 6.9821 6.3167

22 6.3986 6.6049 6.1252

23 6.1592 6.1653 5.8999

24 5.9627 5.8968 5.7742

The System Peak Day was November 29, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - November



128

Figure D-24

Commercial Daily Load Profiles – December

Table D-24

Commercial Daily Load Profiles (kW) - December


1 6.0795 6.0585 5.8120

2 6.0698 5.9792 5.7880

3 6.0910 5.9336 5.7868

4 6.1899 5.9581 5.8048

5 6.3461 6.1608 5.8878

6 6.8467 6.6645 6.1620

7 7.5203 7.4701 6.4848

8 7.9693 8.0312 6.3803

9 8.3134 8.5397 6.3529

10 8.4011 8.6743 6.3128

11 8.4520 8.7392 6.2839

12 8.2849 8.6364 6.2756

13 8.1591 8.4801 6.1859

14 8.2944 8.4882 6.1471

15 8.1748 8.3693 6.1112

16 7.9932 8.1857 6.1041

17 7.9357 8.1017 6.3501

18 7.9457 8.0390 6.7130

19 7.7396 7.7167 6.6180

20 7.4968 7.4536 6.4908

21 7.2902 7.2096 6.3963

22 6.9696 6.8864 6.2402

23 6.6321 6.4722 6.0466

24 6.4712 6.2138 5.8925

The System Peak Day was December 28, 2015

4.00

6.00

8.00

10.00

12.00

14.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

COMMERCIAL - December



129

Figure D-25

Industrial Daily Load Profiles – January

Table D-25

Industrial Daily Load Profiles (kW) - January


1 3066.2622 2911.9204 2527.8721

2 3201.9763 2906.1221 2548.3433

3 3500.7375 2949.6667 2546.7576

4 3476.7156 2950.1143 2549.1670

5 3470.2708 2998.8562 2578.7813

6 3522.0518 3029.5430 2595.7407

7 3589.5496 3031.0901 2592.1528

8 3629.7546 3037.6775 2557.8086

9 3663.3147 3072.9136 2529.2747

10 3675.7437 3115.6877 2536.8362

11 3789.0095 3105.5767 2578.4404

12 3679.1277 3115.8926 2568.4907

13 3784.4541 3052.9238 2541.8286

14 3760.3313 3076.9373 2557.1250

15 3814.7563 3060.1250 2623.0881

16 3807.9985 2999.7231 2668.8892

17 3684.0122 2969.0645 2670.2375

18 3810.2844 3049.3508 2781.7114

19 3683.4512 2966.7446 2734.6934

20 3720.5557 2944.7979 2535.6680

21 3728.2419 3011.5378 2707.4316

22 3747.6709 3013.3330 2768.7427

23 3722.7773 3002.0969 2782.7002

24 3733.8755 3015.5820 2755.3501

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - January



130

Figure D-26

Industrial Daily Load Profiles – February

Table D-26

Industrial Daily Load Profiles (kW) - February


1 2598.4182 3162.1091 3167.4209

2 2582.0984 3130.5420 3201.0537

3 2667.4939 3118.0200 3199.4048

4 2609.3853 3130.0483 3220.3672

5 2659.8533 3103.3030 3235.9551

6 2660.2998 3101.9753 3216.3052

7 2553.3953 3136.9985 3164.2017

8 2591.8250 3137.5901 3101.7078

9 2843.2483 3178.6833 3123.5142

10 3106.8555 3232.9233 3162.8252

11 3116.9709 3213.0647 3166.8906

12 3129.6570 3224.6431 3159.1597

13 3053.4224 3152.7666 3108.9690

14 3132.0212 3149.9783 3130.4077

15 3192.0378 3129.9421 3147.2202

16 3063.2788 3131.4670 3103.9653

17 3105.6785 3139.3738 3093.6328

18 3159.3418 3168.0852 3124.7520

19 3501.1501 3185.1846 3064.4219

20 3373.8164 3169.0090 3075.1479

21 3072.7097 3200.2771 3078.0884

22 3059.0847 3178.4783 3100.7988

23 3095.8232 3188.6445 3132.6421

24 3262.4749 3224.5732 3079.6248

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - February



131

Figure D-27

Industrial Daily Load Profiles – March

Table D-27

Industrial Daily Load Profiles (kW) - March


1 3093.5022 2602.1416 2670.3142

2 3073.2864 2593.6355 2644.7329

3 3069.5352 2602.3650 2631.4531

4 3151.6750 2589.0913 2611.2080

5 3111.9551 2644.3245 2655.4441

6 3154.8394 2663.1699 2605.8281

7 3165.0061 2663.9895 2519.5146

8 3212.8936 2684.4771 2474.6743

9 3318.8025 2740.7415 2445.6582

10 3471.3040 2786.4402 2450.7649

11 3482.1208 2787.5046 2473.9355

12 3459.7346 2743.2263 2560.8298

13 3290.6865 2645.1418 2496.7578

14 3232.4854 2641.4185 2473.3008

15 3022.6155 2656.9934 2470.5088

16 2828.2075 2651.5183 2403.2949

17 2843.9731 2604.0049 2420.8899

18 3144.2275 2585.3259 2412.8069

19 3206.6475 2556.6924 2396.6582

20 3176.9478 2602.2207 2477.3389

21 3229.5789 2610.4055 2509.5713

22 3136.6948 2588.0491 2530.1226

23 3134.2617 2618.2546 2518.5464

24 3207.7534 2647.1030 2542.6777

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - March



132

Figure D-28

Industrial Daily Load Profiles – April

Table D-28

Industrial Daily Load Profiles (kW) - April


1 3390.5461 2535.3276 2473.9028

2 3378.0000 2525.3618 2542.5784

3 3381.7251 2503.7209 2552.8364

4 3386.0154 2476.0793 2550.3999

5 3435.9866 2492.3550 2589.5286

6 3424.1987 2521.3816 2630.7256

7 3289.1152 2541.7207 2538.2637

8 3368.0444 2611.3206 2523.4321

9 3488.4272 2709.3000 2540.5654

10 3537.7671 2723.3738 2578.3865

11 3594.3933 2780.8086 2526.5859

12 3611.5615 2792.0913 2529.6362

13 3535.6030 2723.8467 2570.0342

14 3577.4807 2751.4709 2589.9077

15 3506.8813 2736.9128 2571.4250

16 3476.5427 2722.5354 2600.5508

17 3516.2100 2649.3186 2577.0496

18 3486.9060 2601.4106 2530.2070

19 3379.0229 2456.3518 2443.6211

20 3461.2102 2481.1318 2480.8376

21 3531.3755 2519.4109 2525.5806

22 3459.5632 2529.8254 2450.1646

23 3451.8005 2570.6965 2432.3892

24 3374.7786 2596.8330 2506.6709

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - April



133

Figure D-29

Industrial Daily Load Profiles – May

Table D-29

Industrial Daily Load Profiles (kW) - May


1 3411.5088 3014.2954 3008.0615

2 3481.0435 3050.0002 3183.2993

3 3551.9917 3079.3015 3176.1851

4 3523.6213 3113.8633 3123.7822

5 3623.8206 3180.6157 3191.4946

6 3591.3826 3140.9307 3256.9790

7 3490.9985 3074.1052 3280.1416

8 3593.0176 3103.5938 3206.5000

9 3734.4180 3136.6638 3038.7141

10 3528.0159 3130.2415 2885.0337

11 2993.8535 3120.6157 2858.2751

12 2915.1987 3074.7292 2855.1633

13 2895.4502 3007.3887 2861.3545

14 3108.2043 2981.6995 2839.7334

15 3543.2517 3043.9790 3046.7935

16 3696.5400 3047.2942 3054.3384

17 3269.3511 3021.1636 2901.8901

18 3292.7356 3026.8899 2812.1633

19 3372.2788 2993.0928 2689.0957

20 3120.9031 2983.9458 2679.7014

21 3640.7473 3072.1543 2616.3352

22 3684.5347 3012.5933 2629.3843

23 3621.8181 3005.9358 2659.3745

24 3631.6152 3029.7808 2667.2573

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - May



134

Figure D-30

Industrial Daily Load Profiles – June

Table D-30

Industrial Daily Load Profiles (kW) – June


1 3246.7825 3297.5654 3504.8240

2 3695.1804 3363.8926 3506.0972

3 3722.6287 3386.1228 3463.4858

4 3760.6597 3405.3662 3389.6328

5 3771.4309 3393.8193 3442.6997

6 3810.5696 3359.7231 3375.7056

7 3763.3557 3294.8538 3360.1860

8 3885.6567 3404.8716 3412.9644

9 3968.9199 3486.6311 3438.3772

10 3992.4656 3514.0339 3480.5090

11 4116.9175 3524.4868 3516.8022

12 4134.3545 3519.3394 3499.6340

13 3852.5701 3459.5251 3447.0259

14 3592.2212 3507.5779 3513.5610

15 3599.6421 3513.5928 3573.8547

16 3496.2998 3468.7153 3456.4858

17 3537.0264 3425.9316 3474.6865

18 3900.6575 3409.7131 3544.9290

19 3709.3569 3329.5867 3432.2832

20 3660.9722 3290.6853 3236.3337

21 3695.9978 3325.5220 3335.3643

22 3754.6406 3323.6538 3342.5735

23 3767.9495 3354.7976 3294.7727

24 3694.3279 3338.2090 3297.4614

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - June



135

Figure D-31

Industrial Daily Load Profiles – July

Table D-31

Industrial Daily Load Profiles (kW) - July


1 3691.8208 3329.4099 3588.9211

2 3655.2961 3306.4319 3597.2290

3 3665.2393 3303.2532 3590.4463

4 3621.9460 3314.6614 3623.3208

5 3652.4082 3349.8333 3632.4131

6 3577.0010 3343.5999 3617.1782

7 3545.8208 3309.2603 3393.2568

8 3628.5964 3321.9177 3313.6206

9 3700.2456 3366.9790 3419.6716

10 3726.0073 3401.0486 3413.0234

11 3837.3264 3412.2175 3427.6533

12 3836.0122 3410.7307 3440.9968

13 3903.6929 3339.4958 3494.4199

14 4063.8398 3335.7361 3742.6179

15 4146.3789 3328.7319 3648.9419

16 4104.0195 3342.6506 3723.9609

17 3999.3240 3394.1697 3688.8184

18 4019.0295 3365.2825 3691.5630

19 3937.2878 3300.0239 3611.5466

20 3975.8892 3289.7166 3545.7925

21 4008.8076 3401.9060 3591.7725

22 3994.0237 3391.3098 3593.6978

23 3862.4197 3338.2195 3612.2642

24 3896.1152 3305.4238 3587.9248

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - July



136

Figure D-32

Industrial Daily Load Profiles – August

Table D-32

Industrial Daily Load Profiles (kW) - August


1 3276.0210 3344.1538 3423.6423

2 3278.8318 3338.6479 3447.2122

3 3275.1487 3358.6729 3455.3208

4 3246.5293 3343.0186 3465.8862

5 3305.6741 3382.7603 3463.4346

6 3303.2798 3424.6660 3470.9443

7 3225.3694 3432.6252 3391.1030

8 3274.0032 3455.4211 3361.3379

9 3307.6477 3530.7368 3416.1333

10 3412.9268 3578.6152 3497.8110

11 3455.8953 3595.7212 3551.7017

12 3428.4377 3583.4365 3623.9775

13 3427.3147 3497.5554 3644.4878

14 3493.3169 3476.0366 3639.7554

15 3533.7305 3522.2813 3720.9980

16 3566.4082 3496.7820 3675.6064

17 3774.7280 3468.6733 3698.7598

18 3952.7903 3463.9871 3635.2942

19 3793.7844 3367.0642 3551.8164

20 3660.2864 3337.7495 3472.7041

21 3514.5090 3461.6738 3583.3264

22 3619.3005 3465.5852 3544.1191

23 3787.0369 3403.4685 3501.9893

24 3756.7402 3372.7070 3493.0864

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - August



137

Figure D-33

Industrial Daily Load Profiles – September

Table D-33

Industrial Daily Load Profiles (kW) - September


1 3525.2588 3109.3289 3492.8230

2 3582.0750 3124.2244 3492.4172

3 3573.9824 3131.3574 3467.6919

4 3542.1555 3168.8369 3454.2871

5 3625.3313 3211.3669 3449.4146

6 3603.6724 3207.5735 3450.6821

7 3379.6414 3137.5090 3453.7817

8 3149.7266 3136.6123 3401.0967

9 3214.3618 3200.2473 3411.9946

10 3219.5063 3254.0227 3421.3452

11 3193.0688 3332.5615 3444.1646

12 3325.6160 3358.4734 3434.0483

13 3443.5347 3345.1616 3262.9844

14 3543.4746 3373.4626 3082.0293

15 3608.7275 3390.2739 3092.8757

16 3593.6479 3393.0439 3094.9211

17 3603.7751 3339.9946 3127.3638

18 3455.4651 3286.2317 3107.6167

19 3312.2097 3265.8743 3212.6953

20 3321.3804 3272.3806 3155.5684

21 3754.0681 3321.3120 3158.4277

22 3736.1707 3294.3162 3237.0713

23 3648.9514 3194.4514 3196.0796

24 3663.4297 3190.1904 3173.4067

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - September



138

Figure D-34

Industrial Daily Load Profiles – October

Table D-34

Industrial Daily Load Profiles (kW) - October


1 2977.6750 2757.5681 2642.4395

2 3067.6321 2747.1042 2714.2273

3 3102.2842 2770.2546 2820.7476

4 3053.9280 2777.7307 2809.6243

5 3125.2761 2801.1328 2851.7297

6 3154.0200 2816.9124 2884.0708

7 3183.8320 2802.3975 2885.6150

8 3175.4519 2765.1724 2821.4941

9 3179.3984 2831.1174 2809.6301

10 3117.6221 2847.9358 2824.1943

11 3195.9839 2860.0852 2835.6226

12 3218.6509 2852.8584 2863.3335

13 3235.2261 2808.5410 2795.1978

14 3300.7607 2803.6057 2786.3213

15 3275.2251 2819.7046 2819.0088

16 3262.4719 2832.6912 2798.0405

17 3249.6206 2830.0027 2842.7813

18 3209.4363 2818.5178 2834.6714

19 3167.7251 2797.0085 2790.8848

20 2829.1875 2798.1790 2767.7734

21 2794.0413 2783.6624 2804.3237

22 2782.4009 2780.9370 2794.3887

23 2581.2688 2748.5764 2775.8096

24 2448.3293 2721.3687 2777.2671

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - October



139

Figure D-35

Industrial Daily Load Profiles – November

Table D-35

Industrial Daily Load Profiles (kW) - November


1 2666.0325 2813.1760 2732.4053

2 2853.4690 2814.2192 2700.4067

3 3118.0078 2780.2927 2743.1362

4 3118.1313 2761.9600 2756.4907

5 3150.6230 2789.1309 2774.8792

6 3138.2043 2727.1055 2791.7627

7 3094.4741 2634.9309 2815.4099

8 3065.8965 2705.0491 2783.8098

9 3107.1794 2782.1387 2766.3044

10 3134.7004 2760.2739 2773.9775

11 3127.2129 2801.5793 2768.9634

12 3070.3679 2776.6565 2756.7500

13 3181.3782 2737.0857 2728.8584

14 3187.9333 2762.2859 2709.2427

15 3242.7253 2792.0771 2701.9922

16 3231.2756 2771.6304 2689.6399

17 3173.5637 2737.6694 2711.6641

18 3286.4202 2821.6785 2747.7605

19 3162.8538 2774.2336 2574.4373

20 3185.8018 2781.6145 2507.9541

21 3222.6467 2852.0359 2544.5342

22 3212.6741 2892.8220 2534.4722

23 3205.7566 2896.3350 2527.6694

24 3234.8342 2877.2871 2570.1421

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - November



140

Figure D-36

Industrial Daily Load Profiles – December

Table D-36

Industrial Daily Load Profiles (kW) - December


1 3203.9041 2777.3477 2955.9924

2 3179.5378 2767.1428 2914.8701

3 3178.9331 2777.6052 2918.2588

4 3173.5901 2764.8601 2892.8633

5 3193.9897 2784.4099 2909.5254

6 3225.5417 2790.9341 2964.7183

7 3234.7896 2756.8984 2984.2461

8 3258.1226 2715.8967 2949.6096

9 3265.5376 2683.4714 2935.6355

10 3326.5771 2718.8149 2930.6396

11 3320.2087 2684.2517 2862.9297

12 3245.5464 2635.1689 2864.3501

13 3233.9170 2625.6062 2868.3735

14 3269.7405 2643.9375 2846.3877

15 3269.5322 2666.2083 2856.4004

16 3244.0569 2645.9492 2917.7085

17 3266.4827 2666.6941 2955.7224

18 3411.6399 2741.5681 3002.7783

19 3322.4856 2704.1699 2950.6487

20 3199.1497 2700.8396 2950.3428

21 3183.3689 2747.4321 2951.4070

22 3119.0710 2734.5715 2930.1597

23 3115.5254 2742.1292 2902.5979

24 3126.5994 2747.4463 2962.6443

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

Hour

INDUSTRIAL - December



141

Appendix E

Technology Characterization and Busbar Cost Analysis


142

Appendix F

Black Hills Variable Energy Resource Integration Study


143

Appendix G

2016 -2040 Load and Resource Balance


144

Appendix H

General Planning Assumptions

The planning assumptions shown on Table H-1 will underlie the evaluation of proposals received in response to any Company solicitation in Phase II of these 2016 ERP proceedings. Note that the following is not a complete listing of all assumptions that will be applied in the evaluation process. In addition, the assumptions noted below represent “base case” assumptions. Sensitivity analysis will be performed in which certain of these assumptions are altered in accordance with Commission directives.

Table H-1

General Planning Assumptions

Item 2016 ERP Assumption Updated in Phase II

Capacity credit for solar 37% No

Capacity credit for wind 20% No

CO2 price forecast $0/ton No

Conventional resource options considered

See Section 5.0 No

Cost of integrating renewable resources

See Section 6 No

DSM forecast See Section 3.7 If appropriate

Emissions costs Confidential No

Financial parameters See Table 3-3 Yes, as appropriate

General inflation rate 2.8% Yes

Interconnection costs applied to bids

Varies by resource Yes

Load forecast See Section 4.0 No

Market prices Confidential Yes

Natural gas prices Confidential Yes

Owned unit operating characteristics and costs

See Table 5-1 No

Owned unit retirement dates See Table 5.1 No

Planning period 25 years No

Planning reserve margin 15% minimum No

Power purchase contracts Varies by resource No

Renewable resource options considered

See Section 5.0 No

Resource Acquisition Period 7 years No

Seasonal firm market purchases See Section 3.4.4 No

Spinning reserve requirement Rocky Mountain Reserve Group requirements

No


145

Appendix I

Computer Modules Used for the Electric Resource Plan

Black Hills uses System Optimizer to produce unique resource portfolios across a range

of different planning assumptions. System Optimizer’s core logic handles both Capacity Expansion and Emissions Compliance decisions. The optimized capacity plan can be fed into the Planning and Risk module for a detailed analysis without changing base data or scenario data. The System Optimizer model operates by minimizing operating costs for existing and prospective new resources, subject to system load balance, reliability and other constraints. Over the 25-year planning horizon, it optimizes resource additions subject to resource costs and capacity constraints (summer peak loads plus a planning reserve margin). In the event that a retirement of an existing generating resource or contract expiration is assumed for a given planning scenario, System Optimizer will select additional resources as required to meet summer peak loads inclusive of a target planning reserve margin. To accomplish these optimization objectives, System Optimizer performs a time-of-day least cost dispatch for existing and potential planned generation, while considering cost and performance of existing contracts within Black Hills’ transmission system. Resource dispatch is based on a representative-week method. Dispatch determines optimal electricity flows between zones and includes spot market transactions for system balancing. The model minimizes the system PVRR, which includes the net present value cost of existing contracts, spot market purchase costs, generation costs (fuel, fixed and variable operation and maintenance, decommissioning, emissions, unserved energy, and unmet capacity), and amortized capital costs for potential new resources. The PAR software was used to analyze, report, and estimate the optimal dispatch of a generation portfolio against either a market price or a load requirement. PAR’s uncertainty modeling technology allows for the flexibility to perform either scenario or Monte Carlo simulation analysis of electricity based portfolios, thereby fitting in with either traditional analytical methods, or those based on modern financial theory. PAR is driven by ABB’s PROSYM chronological calculation engine for modeling power systems, developed for over 20 years to meet the needs of utilities facing portfolio planning decisions. PROSYM’s engine is used to simulate a portfolio’s operation by reflecting detailed unit operating constraints like start-up costs, ramp rate restrictions, minimum up and down times, and other plant dynamics, providing a credible analysis of asset valuation and risk exposure. Strategic Planning powered by MIDAS Gold® was utilized to measure and analyze the consumer value of competition. Strategic Planning includes multiple modules for an enterprise-wide strategic solution. These modules are: Markets, Capacity


146

Expansion, Portfolio, Financial, and Risk. The financial and risk modules were the only modules used for the 2016 ERP. The financial module allows the user the ability to model other financial aspects regarding costs exterior to the operation of units and other valuable information that is necessary to properly evaluate the economics of a generation fleet. The financial module produces bottom-line financial statements to evaluate profitability and earnings impacts. Risk module provides users the capability to perform stochastic analyses on all other modules and review results numerically and graphically. Stochastics may be performed on both production and financial variables providing flexibility not available in other models. Strategic Planning has the functionality of developing probabilistic price series by using a four-factor structural approach to forecast prices that captures the uncertainties in regional electric demand, resources and transmission. Using a Latin Hypercube-based stratified sampling program, Strategic Planning generates regional forward price curves across multiple scenarios. Scenarios are driven by variations in a host of market price “drivers” (e.g. demand, fuel price, availability, hydro year, capital expansion cost, transmission availability, market electricity price, reserve margin, emission price, electricity price and/or weather) and takes into account statistical distributions, correlations, and volatilities for three time periods (i.e. Short-Term hourly, Mid-Term monthly, and Long-Term annual) for each transact group. By allowing these uncertainties to vary over a range of possible values a range or distribution of forecasted prices are developed. Stratified sampling can be thought of as “smart” Monte Carlo sampling. Instead of drawing each sample from the entire distribution – as in Monte Carlo sampling – the sample space is divided into equal probability ranges and then a sample is take from each range.


147

Appendix J

Emissions Projections

Table J-1 Annual Projected SO2 Emissions from Existing Resources (Tons) Table J-2 Annual Projected SO2 Emissions from Generic Resources (Tons) Table J-3 Annual Projected CO2 Emissions from Existing Resources (Tons) Table J-4 Annual Projected CO2 Emissions from Generic Resources (Tons) Table J-5 Annual Projected NOx Emissions from Existing Resources (Tons) Table J-6 Annual Projected NOx Emissions from Generic Resources (Tons) Table J-7 Annual Projected PM Emissions from Existing Resources (Tons) Table J-8 Annual Projected PM Emissions from Generic Resources (Tons) Table J-9 Annual Projected Mercury Emissions from Existing Resources (Tons) Table J-10 Annual Projected Mercury Emissions from Generic Resources (Tons)


148

Table J-1

Annual Projected SO2 Emissions from

Existing Resources (Tons)

Year

Base-with-

RES

Alt Plan

1

Alt Plan

2

2016 0.09 0.09 0.09

2017 0.08 0.08 0.08

2018 0.11 0.11 0.11

2019 0.09 0.09 0.09

2020 0.10 0.10 0.10

2021 0.11 0.11 0.11

2022 0.10 0.10 0.10

2023 0.12 0.11 0.11

2024 0.11 0.12 0.12

2025 0.15 0.16 0.16

2026 0.17 0.16 0.16

2027 0.19 0.18 0.18

2028 0.19 0.18 0.18

2029 0.21 0.20 0.20

2030 0.24 0.23 0.23

2031 0.25 0.24 0.24

2032 2.52 1.73 1.73

2033 2.51 1.74 1.74

2034 2.52 1.77 1.77

2035 2.57 1.75 1.75

2036 2.58 1.71 1.71

2037 2.49 1.82 1.82

2038 2.52 1.56 1.56

2039 2.52 1.60 1.58

2040 2.52 1.58 1.56


149

Table J-2

Annual Projected SO2 Emissions

from Generic Resources (Tons)

Year

Base-

with-RES Alt 1 Alt 2

2016 - - -

2017 - - -

2018 - - -

2019 - - -

2020 - - -

2021 - - -

2022 - - -

2023 - - -

2024 - - -

2025 - - -

2026 - - -

2027 - - -

2028 - - -

2029 - - -

2030 - - -

2031 - - -

2032 0.76 0.75 0.35

2033 0.72 0.72 0.34

2034 0.70 0.70 0.33

2035 0.69 0.68 0.30

2036 0.70 0.72 0.34

2037 0.66 0.67 0.31

2038 0.81 0.81 0.37

2039 0.77 0.76 0.35

2040 0.77 0.77 0.35


150

Table J-3 Annual Projected CO2 Emissions from


Year Base-with-

RES Alt Plan

1 Alt Plan

2

2016 17,711 17,711 17,711

2017 16,529 16,529 16,529

2018 22,639 22,639 22,639

2019 18,242 18,242 18,242

2020 19,838 19,838 19,838

2021 21,013 21,013 21,013

2022 19,853 19,853 19,853

2023 22,909 22,909 22,909

2024 24,431 24,431 24,431

2025 31,240 31,240 31,240

2026 32,891 32,891 32,891

2027 36,184 36,184 36,184

2028 36,835 36,835 36,835

2029 40,666 40,666 40,666

2030 46,180 46,180 46,180

2031 47,799 47,799 47,799

2032 344,821 345,283 345,283

2033 347,169 347,522 347,522

2034 353,458 354,223 354,223

2035 367,424 350,890 350,890

2036 367,126 342,469 342,469

2037 375,514 364,059 364,059

2038 330,863 311,026 311,026

2039 339,216 320,682 316,711

2040 340,035 315,399 312,989


151

Table J-4 Annual Projected CO2 Emissions from

Generic Resources (Tons)

Year Base-with-

RES Alt

Plan 1 Alt Plan 2

2016 - - -

2017 - - -

2018 - - -

2019 - - -

2020 - - -

2021 - - -

2022 - - -

2023 - - -

2024 - - -

2025 - - -

2026 - - -

2027 - - -

2028 - - -

2029 - - -

2030 - - -

2031 - - -

2032 151,070 151,070 149,404

2033 144,416 144,416 144,111

2034 140,566 140,566 139,742

2035 137,386 137,386 135,876

2036 139,585 139,585 143,615

2037 131,176 131,176 133,935

2038 161,468 161,468 162,533

2039 154,872 154,872 151,039

2040 154,712 154,712 152,376


152

Table J-5 Annual Projected NOX Emissions from Existing

Resources (Tons)

Year Base-with

RES Alt Plan 1 Alt

Plan 2

2016 2.21 2.21 2.21

2017 2.08 2.08 2.08

2018 2.85 2.85 2.85

2019 2.31 2.31 2.31

2020 2.51 2.51 2.51

2021 2.65 2.65 2.65

2022 2.50 2.50 2.50

2023 2.90 2.90 2.90

2024 3.09 3.09 3.09

2025 3.98 3.98 3.98

2026 4.17 4.17 4.17

2027 4.60 4.60 4.60

2028 4.68 4.68 4.68

2029 5.17 5.17 5.17

2030 5.87 5.87 5.87

2031 6.09 6.09 6.09

2032 44.01 44.05 44.05

2033 44.15 44.20 44.20

2034 44.52 44.62 44.62

2035 46.27 44.15 44.15

2036 46.34 43.17 43.17

2037 47.30 45.97 45.97

2038 42.55 39.25 39.25

2039 43.47 40.35 39.84

2040 42.85 39.74 39.40


153

Table J-6 Annual Projected NOX Emissions from


Year Base-with-

RES Alt

Plan 1 Alt

Plan 2

2016 - - -

2017 - - -

2018 - - -

2019 - - -

2020 - - -

2021 - - -

2022 - - -

2023 - - -

2024 - - -

2025 - - -

2026 - - -

2027 - - -

2028 - - -

2029 - - -

2030 - - -

2031 - - -

2032 18.88 18.68 18.68

2033 18.05 18.01 18.01

2034 17.57 17.47 17.47

2035 17.17 16.98 16.98

2036 17.45 17.95 17.95

2037 16.40 16.74 16.74

2038 20.18 20.32 20.32

2039 19.36 19.03 18.88

2040 19.34 19.37 19.05


154

Table J-7 Annual Projected PM Emissions from


Year Base-with-

RES Alt

Plan 1 Alt Plan

2

2016 0.59 0.59 0.59

2017 0.56 0.56 0.56

2018 0.76 0.76 0.76

2019 0.62 0.62 0.62

2020 0.67 0.67 0.67

2021 0.71 0.71 0.71

2022 0.67 0.67 0.67

2023 0.77 0.77 0.77

2024 0.83 0.83 0.83

2025 1.07 1.07 1.07

2026 1.11 1.11 1.11

2027 1.23 1.23 1.23

2028 1.25 1.25 1.25

2029 1.38 1.38 1.38

2030 1.57 1.57 1.57

2031 1.63 1.63 1.63

2032 11.69 11.70 11.70

2033 11.72 11.73 11.73

2034 11.89 11.92 11.92

2035 12.36 11.79 11.79

2036 12.39 11.54 11.54

2037 12.64 12.29 12.29

2038 11.23 10.49 10.49

2039 11.47 10.78 10.64

2040 11.45 10.62 10.53


155

Table J-8 Annual Projected PM Emissions from


Year Base-with-

RES Alt

Plan 1 Alt

Plan 2

2016 - - - 2017 - - - 2018 - - -

2019 - - - 2020 - - - 2021 - - - 2022 - - - 2023 - - -

2024 - - - 2025 - - - 2026 - - - 2027 - - - 2028 - - -

2029 - - - 2030 - - - 2031 - - - 2032 5.04 4.98 4.98 2033 4.81 4.80 4.80

2034 4.69 4.66 4.66 2035 4.58 4.53 4.53 2036 4.65 4.79 4.79 2037 4.37 4.46 4.46 2038 5.38 5.42 5.42

2039 5.16 5.08 5.03 2040 5.16 5.17 5.08


156

Table J-9 Annual Projected Mercury Emissions from Existing

Resources (Tons)

Year Base-with-

RES Alt 1 Alt 2

2016 0 0 0 2017 0 0 0 2018 0 0 0

2019 0 0 0 2020 0 0 0 2021 0 0 0 2022 0 0 0 2023 0 0 0

2024 0 0 0 2025 0 0 0 2026 0 0 0 2027 0 0 0 2028 0 0 0

2029 0 0 0 2030 0 0 0 2031 0 0 0 2032 0 0 0 2033 0 0 0

2034 0 0 0 2035 0 0 0 2036 0 0 0 2037 0 0 0 2038 0 0 0

2039 0 0 0 2040 0 0 0


157

Table J-10 Annual Projected Mercury Emissions from Generic

Resources (Tons)

Year Base-with-

RES Alt 1 Alt 2

2016 0 0 0 2017 0 0 0 2018 0 0 0

2019 0 0 0 2020 0 0 0 2021 0 0 0 2022 0 0 0 2023 0 0 0

2024 0 0 0 2025 0 0 0 2026 0 0 0 2027 0 0 0 2028 0 0 0

2029 0 0 0 2030 0 0 0 2031 0 0 0 2032 0 0 0 2033 0 0 0

2034 0 0 0 2035 0 0 0 2036 0 0 0 2037 0 0 0 2038 0 0 0

2039 0 0 0 2040 0 0 0


158

Appendix K Confidential Price Forecasts

Confidential Schedule K-1 Natural Gas Price Forecast Confidential Schedule K-2 Fuel Oil Price Forecast Confidential Schedule K-3 Emission Costs (ABB WECC 2015 Fall Reference Case

CO2 Scenario) Confidential Schedule K-4 Seasonal Firm Market Price Forecasts- AZ PV Market

Base Case Confidential Schedule K-5 Seasonal Firm Market Price Forecasts- AZ PV Market

High Gas Case Confidential Schedule K-6 Seasonal Firm Market Price Forecasts- AZ PV Market

Low Gas Case Confidential Schedule K-7 Seasonal Firm Market Price Forecasts- AZ PV Market

CO2 Tax Case Confidential Schedule K-8 Market Clearing Price Forecasts- CO East Market- Base

Case Confidential Schedule K-9 Market Clearing Price Forecasts- AZ PV Market- Base

Case Confidential Schedule K-10 Market Clearing Price Forecasts- CO East Market- CO2

Tax Case Confidential Schedule K-11 Market Clearing Price Forecasts- AZ PV Market- CO2 Tax

Case Confidential Schedule K-12 Market Clearing Price Forecasts- CO East Market- High

Gas Case Confidential Schedule K-13 Market Clearing Price Forecasts- AZ PV Market- High

Gas Case Confidential Schedule K-14 Market Clearing Price Forecasts- CO East Market- Low

Gas Case Confidential Schedule K -15 Market Clearing Price Forecasts- AZ PV Market- Low

Gas Case Confidential Schedule K-16 Projected Emissions Rates for Existing Resources


159

Appendix L

Confidential Exhibit

Cost parameters for Waste-to-Energy PPA Price

Year

Waste-to-Energy PPA

Price Assumption

Annual Escalation

Rate (%)

2019 $47.50 2.5

*Assumed 2019 first year of operation.


160

Appendix M Model Request for Proposal for Intermittent Resources


161

Appendix N

Model Form Contract for Intermittent Resources


162

Appendix O Model Request for Proposal for Stand Alone Renewable Energy Credits


163

Abbreviations AMI – Advanced Metering Infrastructure AEO2016 Early Release - 2016 Annual Energy Outlook, Early Release BACT – Best Available Control Technology Black Hills - Black Hills Colorado Electric Btu – British Thermal Unit CACJA – Clean Air - Clean Jobs Act CAIR – Clean Air Transport Rule CAMR – Clean Air Mercury Rule CC – Combined Cycle CCPG – Colorado Coordinated Planning Group CCR – Code of Colorado Regulations CDD – Cooling Degree Days CDH – Cooling Degree Hours CEIP - Clean Energy Incentive Program CIS+ - Customer Information System CO2 – Carbon dioxide Commission – Colorado Public Utilities Commission CPCN – Certificate of Public Convenience and Necessity CPP – Clean Power Plan CPWG – Conceptual Planning Work Group C.R.S. – Colorado Revised Statutes CSG- Community Solar Garden CT – Combustion Turbine CV – Coefficient of Variation DG – Distributed Generation DSM – Demand-Side Management EIA – Energy Information Administration ELCC – Effective Load Carrying Capability EPA – Environmental Protection Agency EPC – Engineering, Procurement and Construction ERP – Electric Resource Plan ERZ – Energy Resource Zone FERC – Federal Energy Regulatory Commission FIP – Federal Implementation Plans GHG – Greenhouse Gas GRP - Gross Regional Product GWh – Gigawatthour HDD – Heating Degree Days HDH – Heating Degree Hours HRSG – Heat Recovery Steam Generator ITC – Investment Tax Credit


164

JDA – Joint Dispatch Agreement kV – Kilovolt kW – Kilowatt kWh – Kilowatthour LOLE – Loss of Load Expectation LOLH – Loss of Load Hours LTP - Local Transmission Plan MACT – EPA’s Mercury and Air Toxics Standard MPS – Missouri Public Service MW – Megawatt MWh – Megawatthour NAS – Sodium Sulfur NCDC - NOAA National Climatic Data Center NERC – North American Electric Reliability Corporation OLS – Ordinary Least Squares Order 1000 – FERC Order No. 1000 PAGS – Pueblo Airport Generation Station PAR- Planning & Risk PCC – Planning Coordination Committee PPA – Power Purchase Agreement PSD – Prevention of Significant Deterioration PSCo – Public Service Company of Colorado PV solar– Photovoltaics PV - Palo Verde, Arizona PVRR – Present Value of Revenue Requirements QRU – Qualifying Retail Utility RAP – Resource Acquisition Period RB – Rattlesnake Butte Substation REC – Renewable Energy Credit RES – Renewable Energy Standard RESA – Renewable Energy Standards Account RFP – Request for Proposals RMRG – Rocky Mountain Reserve Group SIP – State Implementation Plan SPG- Sub regional Planning Group SSPG – Sierra Sub regional Planning Group SWAT – Southwest Area Transmission Group TCPC - Transmission Coordination and Planning Committee TEPPC – Transmission Expansion Planning Policy Committee The Company – Black Hills Colorado Electric The RES Rules - Commission Rules 4 CCR 723-3-3650 et seq. The RES Statute – C.R.S., § 40-2-124 et seq.

UPC – Use per Customer VER – Variable Energy Resources W&P – Woods & Poole Economics, Inc. WACC – Weighted-Average Cost of Capital


165

WAPA - Western Area Power Administration WECC – Western Electricity Coordinating Council Western – Western Area Power Administration

2016 ELECTRIC RESOURCE PLAN - Black Hills Energy ELECTRIC RESOURCE PLAN Colorado PUC E-Filings System. Attachment LS-1 2016 ERP i Table of Contents ... Table 4-9 Peak Demand and Energy

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