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Slide 1
Western Interconnection Flexibility Assessment Update to TEPPC
May 7, 2015 Arne Olson, Partner
Slide 2
22 About the Study WECC and WIEB are interested in
understanding power system reliability and flexibility needs under
higher renewable penetration There have been many stakeholder
requests for studies to help understand the operational issues
associated with higher renewables WECC is interested in
understanding and gaining experience with new reliability planning
tools WECC and WIEB engaged E3 and NREL to study operational needs
Funding for E3 work from WECC and WIEB (through ARRA) Funding for
NREL work from DOE
Slide 3
33 Study Goals Assess the ability of the fleet of resources in
the Western Interconnection to accommodate high renewable
penetration while maintaining reliable operations Quantify the
size, magnitude and duration of operating challenges resulting from
high renewable penetration Investigate potential flexibility
solutions, including: Renewable dispatch as an operational strategy
Regional coordination Flexible supply and demand-side resources
Energy storage Transmission Learn about how to do flexibility
modeling and planning Institutional solutions Physical
solutions
Slide 4
44 Project Team Partnership between WECC, WIEB, NREL and E3
WECC and WIEB provide general project oversight role E3 directs
technical work NREL provides data, HPC resources, technical
support, and insights from previous experience E3 Arne Olson,
Partner Nick Schlag, Project Manager Dr. Elaine Hart Ryan Jones Ana
Mileva E3 Arne Olson, Partner Nick Schlag, Project Manager Dr.
Elaine Hart Ryan Jones Ana Mileva NREL Bri-Mathias Hodge, Project
Manager Carlo Brancucci Martinez-Anido Greg Brinkman NREL
Bri-Mathias Hodge, Project Manager Carlo Brancucci Martinez-Anido
Greg Brinkman WECC Dan Beckstead Vijay Satyal Donald Davies Branden
Suddeth WECC Dan Beckstead Vijay Satyal Donald Davies Branden
Suddeth WIEB Tom Carr Maury Galbraith WIEB Tom Carr Maury
Galbraith
Slide 5
55 Project Elements 1.Renewable energy capacity value analysis
Effective load-carrying capability (ELCC) method using E3s
Renewable Energy Capacity Planning Model (RECAP) 2.Power system
flexibility analysis Regional model using E3s Renewable Energy
Flexibility (REFLEX) model for PLEXOS run on NRELs High Performance
Computing environment 3.Stakeholder participation opportunities
1.Technical review committee 2.Executive advisory group
Slide 6
66 Cases Studied 2024 Common Case Few reliability or
flexibility issues anticipated Primary purpose of case is
calibration 2024 High Renewables Case Renewable penetration that is
high enough to show interesting operational challenges Composition
of case now being determined in consultation with technical review
committee
Slide 7
77 Regional Focus Study focuses on five regions Explicitly
control interactions between regions through modeling of interties
Most regions share characteristics appropriate for a resource
planning study: Similar weather and load patterns across the region
Limited internal transmission constraints Some degree of regional
coordination already Limited reliance on other regions
Slide 8
8 RECAP MODEL RESULTS
Slide 9
99 E3 Renewable Energy Capacity Planning Model (RECAP)
Flexibility Assessment utilizes RECAP, E3s non-proprietary model
for evaluating power system reliability and resource capacity value
under high renewable penetration Initially developed to support
CAISO renewable integration modeling Used by a number of utilities
and state commissions Will be transferred to WECC as part of study
process
Slide 10
10 Calculating LOLP LOLP is determined by comparing the
distributions of potential load and resource states and calculating
the probably that load exceeds generation Gross load distribution
Net thermal generation distribution LOLP comes from the chance that
net load exceeds net thermal generation Gross load Net thermal
generation LOLP
Slide 11
11 Adding Renewables After adding renewables to the system, net
loads are reduceddistribution shifts to left LOLP decreases in
every hour (nearly) Gross load distribution Net thermal generation
distribution Net load distribution with renewables Renewable net
load Gross load Thermal generation Reduction in LOLP with increase
in renewables
Slide 12
12 Calculating ELCC Since LOLE has decreased with the addition
of renewables, adding load will return the system to the original
LOLE The amount of load that can be added to the system is the
effective load carrying capability (ELCC) Original system LOLE LOLE
after renewables Additional load to return to original system LOLE
= ELCC
Slide 13
13 Portfolio vs. Marginal ELCC Values 1.The cumulative
portfolio capacity value is used for resource adequacy planning Due
to the complementarity of different resources the portfolio value
will be higher than the sum of each individual resource measured
alone May need to attribute the capacity value of the portfolio to
individual resources There are many options, but no standard or
rigorous way to do this 2.The marginal capacity value, given the
existing portfolio, is used in procurement Provides a measure of
the value of the next resource to be procured This value will
change over time with the mix of system needs & resources
Individual Solar Capacity Value Individual Wind Capacity Value
Combined Capacity Value
Slide 14
14 Reliability Metrics The RECAP model calculates conventional
power system reliability metrics: Loss of Load Probability (LOLP)
Loss of Load Expectation (LOLE) Loss of Load Frequency (LOLF)
Expected Unserved Energy (EUE) RECAP also calculates effective
capacity of renewables, demand response, and other dispatch-
limited resources: Effective Load Carrying Capability (ELCC) LOLE
Marginal ELCC Cumulative ELCC
Slide 15
15 Renewable penetration in the Common Case is approximately
20% of load (U.S. portion); wind and solar serve approximately 13%
of load: E3 and NREL have developed production profiles to reflect
the operational characteristics of these resources Common Case
Renewable Mix
Slide 16
16 Target Planning Reserve Margins RECAP estimates reserve
margins needed to achieve a target reliability threshold LOLF = 1
event in 10 years Target PRM needed to meet standard varies by
region Common Case above Target PRM for all regions TypeTarget PRM
Common Case PRM Basin14%17% California13%25% Northwest15%32%
Rockies17%19% Southwest15%32%
Slide 17
17 Marginal ELCC Curves by Technology and Region
SouthwestCalifornia Marginal ELCC = capacity contribution of next
increment of capacity of a given type Curves are illustrative they
assume a single technology
Slide 18
18 Marginal ELCC Curves by Technology and Region (Cont.)
BasinRockies Northwest
Slide 19
19 Observations on ELCC Values Marginal ELCC of solar PV at low
penetrations is 50-60% of nameplate capacity (except in NW) Aligns
well with commonly used heuristics At low penetrations, marginal
ELCC values for wind range from 15-30% of nameplate capacity
Slightly higher than common heuristics ELCC values exhibit
significant diminishing returns to scale, particularly solar PV
which shifts net load peak into the evening As penetration
increases, heuristics become increasingly inaccurate
Slide 20
20 Effect of Diversity on ELCC Values For a diverse portfolio,
ELCC of combined portfolio is higher than individual ELCC values At
20% of load: W = 3041, S = 6172, W+S = 12,861
Slide 21
21 Ongoing uses for RECAP by WECC The need for ELCC in WECCs
planning studies will increase as the penetration of variable
generation increases As part of the flexibility assessment project,
the RECAP model will be transferred to WECC staff to help support
modeling efforts TEPPC Common Case development Summer load
assessments Section 111(d) impacts As an open-source tool, RECAP
can also be shared with or modified by stakeholders
Slide 22
22 REFLEX MODEL STATUS
Slide 23
23 E3s Renewable Energy Flexibility (REFLEX) Model REFLEX
answers critical questions about flexibility need through
stochastic production simulation Captures wide distribution of
operating conditions through Monte Carlo draws of operating days
Illuminates the significance of the operational challenges by
calculating the likelihood, magnitude, duration & cost of
flexibility violations Assesses the benefits and costs of
investment to avoid flexibility violations Implemented as an add-on
to Plexos for Power Systems
Slide 24
24 WECC Flexibility Assessment Distinguishing Characteristics
The use of REFLEX for PLEXOS in this study is different from
conventional production cost modeling of the WECC in several
important respects: 1.Economic tradeoff between upward (loss of
load) and downward (curtailment) flexibility violations
2.Endogenous determination of load following reserves as function
of expected within-hour flexibility deficiencies 3.Stochastic
sampling of load, wind, solar, and hydro conditions 4.Sub-regional
study footprints with specified boundary conditions Import/export
limitations and maximum ramp rates
Slide 25
25 Renewable Dispatch is Used to Solve Upward Ramping Shortages
Model needs robust information on cost of upward vs. downward
shortages Cost of unserved energy due to ramping shortfall: very
high ($5,000-50,000/MWh) Cost of renewable dispatch: replace the
lost production ($50-$150/MWh) Limited Ramping Capability Unserved
Energy Limited Ramping Capability Renewable Curtailment Strategy to
Minimize Downward ViolationsStrategy to Minimize Upward
Violations
Slide 26
26 Stochastic Sampling From a Range of Conditions In order to
ensure robust sampling results, RECAP and REFLEX sample from a
broad range of load, wind, & solar conditions Historical data
matched up based on month of year, day type (i.e. load level) Range
of available data
Slide 27
27 Capturing Transmission in Flexibility Assessment Multiple
options for representing interregional power flows have been tested
123 Original project plan Single-Zone Models Each region modeled
independently with no internal transmission Imports and exports
captured through supply curves Offers simplest modeling framework,
but difficult to represent interregional power exchange Single-Zone
Models Each region modeled independently with no internal
transmission Imports and exports captured through supply curves
Offers simplest modeling framework, but difficult to represent
interregional power exchange Zonal Model Loads and resources
grouped together by region Regions linked together by transport
model Provides macro level view of interregional power exchange,
but ignores individual line and path flow limits Zonal Model Loads
and resources grouped together by region Regions linked together by
transport model Provides macro level view of interregional power
exchange, but ignores individual line and path flow limits Nodal
Model All nodes in WECC (25,000) represented Dispatch solution is
constrained by DC OPF and enforced line limits Provides greatest
fidelity of transmission system, but requires significant
development and is computationally intensive Nodal Model All nodes
in WECC (25,000) represented Dispatch solution is constrained by DC
OPF and enforced line limits Provides greatest fidelity of
transmission system, but requires significant development and is
computationally intensive Model Complexity Final project plan
Slide 28
28 Zonal Topology Zonal topology chosen based on aggregations
of interregional WECC paths California Northwest Basin Rocky
Mountain Southwest NW to CA P65 P66 CA to BS P24 P28 P29 SW to CA
P46 RM to SW P31 BS to SW P35 P78 P79 All Paths P14: ID to NW P18:
NT - ID P24: PG&E - Sierra P28: Intermtn - Mona P29: Intermtn
Gonder P30: TOT 1A P31: TOT 2A P35: TOT 2C P38: TOT 4B P46: WOR
P65: COI P66: PDCI P76: Alturas Project P78: TOT 2B1 P79: TOT 2B2
P80: MT SE BS to NW P14 P18 P76 P80 BS to RM P30 P38
Slide 29
29 Regional Ramping Constraints Production simulation models
tend to overstate ramps on interties compared to historical levels
Constrained case limits intertie ramps based on historical levels
Example: Historical vs. Modeled Flows over Path 46 Upward Ramps
Downward Ramps Source:
http://www.wecc.biz/Lists/Calendar/Attachments/5076/130124_DWGMeeting_E3_Presentation-PCM.pdf
Duration (hrs)
Slide 30
30 Strawman High Renewables Case High renewables case should
have enough wind and solar generation to illuminate significant
flexibility constraints Test simulations of this Strawman case,
while preliminary, provide some useful insights into challenges at
higher penetrations to be shared today
Slide 31
31 Strawman Limited-Draw Results: California Curtailment: ~6%
RG Overgeneration occurs regularly and periodically, especially in
the spring months Overgeneration is solar-driven and occurs in the
middle of the day Hydro, pumped storage, and imports help to meet
nighttime load April Day Strawman High Renewables All dispatchable
plants reduce output to minimum levels Curtailment due to solar
oversupply
Slide 32
32 Strawman Limited-Draw Results: Northwest Curtailment: ~3% of
RG Overgeneration conditions occur during high hydro and/or high
wind conditions Curtailment may be concentrated during nighttime or
could persist through day if wind output remains high More
day-to-day variability in conditions within seasons compared to
regions with high solar penetration April Day Strawman High
Renewables Curtailment due to simultaneous high wind & hydro
conditions
Slide 33
33 Strawman Limited-Draw Results: Northwest Curtailment: ~3% of
RG Overgeneration conditions occur during high hydro and/or high
wind conditions Curtailment may be concentrated during nighttime or
could persist through day if wind output remains high More
day-to-day variability in conditions within seasons compared to
regions with high solar penetration April Day #2 Strawman High
Renewables During lower hydro conditions, high wind may not result
in curtailment
Slide 34
34 Strawman Limited-Draw Results: Southwest Curtailment: ~3% of
RG Coal plants are cycled down the middle of the day to accommodate
solar, but curtailment still occurs Steep morning down-ramp and
evening up-ramp of coal, hydro, and gas are challenging operational
conditions April Day Strawman High Renewables Large coal ramps
require further investigation Curtailment due to solar
oversupply