Pacific Northwest Smart Grid Demonstration Project Technical Status Update for GWAC Transactive Energy Workshop Ron Melton and Don Hammerstrom 2012.03.28 PNWD-SA-9681 1
Pacific Northwest Smart Grid Demonstration Project Technical Status Update for GWAC Transactive Energy Workshop Ron Melton and Don Hammerstrom 2012.03.28 PNWD-SA-9681
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Pacific Northwest Demonstration Project What:
• $178M, ARRA-funded, 5-year demonstration
• 60,000 metered customers in 5 states
Why: • Quantify costs and benefits • Develop communications protocol • Develop standards • Facilitate integration of wind
and other renewables
Who: Led by Battelle and partners including BPA, 11 utilities, 2 universities, and 5 vendors
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Project Basics
Operational objectives Manage peak demand Facilitate renewable
resources Address constrained
resources Improve system
reliability and efficiency Select economical
resources (optimize the system)
Aggregation of Power and Signals Occurs Through a Hierarchy of Interfaces
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Some Definitions
• Transactive Control A single, integrated, smart grid incentive signaling approach utilizing an economic signal as the primary basis for communicating the desire to change the operational state of responsive assets.
• Transactive Incentive Signal (TIS) A representation of the actual delivered cost of electric energy at a specific system location (e.g., at a transactive node). Includes both the current value and a forecast of future values.
• Transactive Feedback Signal (TFS) A representation of the net electric load at a specific system location (e.g., at a transactive node). Includes both the current value and a forecast of future values.
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Role of a Transactive Node
• Respond to system conditions as represented by incoming Transactive Incentive Signals and Transactive Feedback Signals through
– Decisions about behavior of local assets – Incorporation of local asset status and other local information – Updating both transactive incentive and feedback signals
• Inputs are needed from node-owners to calculate incentive
and feedback signals • Each signal is a sequence of forecasts for a time-series, so
inputs will also be sequences of future (forecast/planned) values
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End-to-End View of Transactive Control System – TIS Example Below is an example of a signal being modified as it flows from supply towards consumption through the transactive network
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G
$ / M W h
Hours
$ / M W h
Hours
$ / M W h
Hours
Transmission - Level objectives
( e . g . Energy cost ; Trans Constraints )
Utility - Level objectives
( e . g . Avoid demand charges)
Local objectives ( e . g . Incent usage when local wind farm generating )
$ / M W
Hours
Final Incentive Signal received by Responsive Asset
Computing the Transactive Incentive Signal
(Energy Cost + Capacity Cost + Infrastructure Cost + Other Costs) TIS (t) = -------------------------------------------------------------------------------------- Total Energy Generated or Imported
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Transactive Control Feedback Loop
New incentive signals and feedback signals are generated on an event-driven basis. The most recently available information is used. Each signal responds to changes in the other, and the values converge .
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
-24 -21 -18 -15 -12 -9 -6 -3 0
Load
or P
rice
(rat
io to
fina
l)
Time (hr from present)
LoadPrice
History of Load (TFS) & Price (TIS) Forecasts for Hour 0
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Simple Example – Local EV Charging • Imagine the following situation:
– Three neighbors with electric vehicles and different charging strategies – All three fed by same distribution transformer – All three come home and want to do a fast charge at the same time!
• Problem – transformer is overloaded if all three fast charge at the same time
• Transactive control solution – – Transformer sees in feedback signal that all three plan to fast charge – Transformer raises value of incentive signal during planned charging
time to reflect decreased transformer life – Smart chargers and transformer “negotiate” through TIS and TFS until
an acceptable solution is found
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$0.00
$0.01
$0.02
$0.03
$0.04
$0.05
$0.06
05
101520253035404550
16 17 18 19 20 21 22 23 24 1 2 3 4 5 6
Pric
e, T
IS ($
/kW
h)
Load
, TFS
(kW
)
Time of Day (hr)
EV3-Saver EV2-NowEV1-Flex Base LoadMax.Demand TFSCapacity TIS
Transactive Control – An Illustration
$0.00
$0.01
$0.02
$0.03
$0.04
$0.05
$0.06
05
101520253035404550
16 17 18 19 20 21 22 23 24 1 2 3 4 5 6
Pric
e, T
IS ($
/kW
h)
Load
, TFS
(kW
)
Time of Day (hr)
EV3-Saver EV2-NowEV1-Flex Base LoadMax.Demand TFSCapacity TIS
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NW Region “Influence Map”--Topology
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Cut Plane
Flowgate
Regional Modeling
Alstom EMS
Alstom MMS Future state Estimation by optimization
BPA
3TIER
Load Forecast Generation Schedules Outages
Network State
Gen. schedules Load forecasts
Transmission Zone TC Node Inputs
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Project Nodes
Alstom Grid Models
Transmission Zone 5 Node
Portland General Node
Transmission Zone 14 Node
Lower Valley Node
Idaho Falls Node
Lower Valley Asset System(s)
Salem Site Asset Systems
Idaho Falls Asset System(s)
Regional Conditions
“Local Conditions” for Transmission Zone Nodes
TIS – down arrow TFS – up arrow
Asset system input – down arrow Local inputs – up arrow
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Transactive Node Structure for Demo
TZ13- MT
ST10-Helena(BA09-NorthWestern
Energy)(UT09-NorthWestern
Energy)
ST11-Philipsburg(BA09-NorthWestern
Energy)(UT09-NorthWestern
Energy)
TZ12- Central Oregon TZ08-OR Cascades
TZ06-Northcentral Washington(BA04-BPA)
ST04-Ellensburg Renewable Park(Ut04-Ellensburg)
Canada@ Boundary
TS34 TS35
ST09-Milton-Freewater (UT08-Milton-Freewater)
TZ09- Southcentral OR
TS09
TZ04-Allston
TZ03-Paul
EasternMontana
TS03 TS06
FG02-N.Cascades North
FG01-Monroe-Echo Lake
FG04-R
aver Paul
FG05-PaulAllston
FG06-AllstonKeeler
FG10-W
. North of H
anford
FG08-W of Hatwai FG16-MT to NW
FG17-Lolo
FG20- LaG
rande
FG19-EnterpriseFG
18- W. of M
cNary
FG14-W. of Slatt
FG11-W
. of John Day
FG12-E. of John Day
FG07-S. Cascades
FG03-N.Cascades
South
FG09-E
. North of H
anford
Canada@ Custer
Wes
t CO
I
PD
CI
East COI
Wyoming
Nevada
FG15- Harney and Midpoint
Northern Montana
eg o a a d Subp oject a sact e Co t o odes & et o opo ogy
TS12
TS18 TS21 TS22
TS15
TS28 TS29
FG13
TS25
TIS/TFS Path (FGxx or TSxx)
TZ – Transmission ZoneBA – Balancing AuthorityUT – Utility (of Subproject)ST – Site (of Subproject)FG – Flowgate
Transactive Control (TC) NodeEIOC TC Nodes
ST02- UW Campus
ST01- Fox Island(UT01-Peninsula Light)
TZ02-West Washington(BA01-BPA) (BA02–SCL)(UT02-Seattle City Light)
TZ01-NW Washington
ST03-Salem(BA03-Portland General)
(UT03-PGE)
TZ05-Western OR
ST05-Reata(UT05-Benton PUD)
TZ07-Hanford(BA05-BPA)
TZ10-N. Idaho(BA07-BPA)
ST08-Haskil(UT07-
Flathead Electrid)
ST07-Libby(UT07-
Flathead Electric)
ST06-Pulman(BA06-Avista)(UT06-Avista)
ST14-DA &Energy
Management(UT11-Idaho Falls Power)
ST13- Loop Microgrid
(UT11-Idaho Falls Power)
TS33
ST12- Teton-Palisades
Power Interconnect(UT10-Lower
Valley)
TZ14- South Idaho(BA10-Pacificorp)
TZ11-NE Oregon(BA08-BPA)
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Transactive Node Inputs & Outputs
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Formalizing Transactive Control
• A formal model of transactive control has been designed with the following features:
– Scalable – Algorithmic – Support for interoperability
• A standardized approach is being promoted through design and implementation of a toolkit
– Well defined interfaces for utility asset systems – Simple, common, algorithms for updating transactive signals and
determining “control” signals to responsive asset systems
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Bulk Power System Inputs to TIS Calculation 1.0 Imported electrical energy
1.1 Non-transactive imported energy
2.0 Renewable energy resource 2.1 Wind energy 2.2 Solar energy 2.3 Hydropower
3.0 Fossil generation 4.0 General infrastructure cost
5.0 System constraints 5.1 Transmission flowgate 5.2 Equipment and line constraints
6.0 System energy losses 6.1 Transmission losses 6.2 Distribution losses
7.0 Demand charges 7.1 BPA demand charges
8.0 Market impacts 8.1 Spot market impacts
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Load Models
1.0 Bulk inelastic load 1.1 Bulk commercial load 1.2 Bulk industrial load 1.3 Bulk residential load 1.4 Small wind generator
negative load 1.5 small-scale distributed
generator negative load 1.6 Small-scale solar generator
negative load
2.0 General event-driven demand response
2.1 Commercial 2.2 Distribution system voltage
control 2.3 Residential behavior
2.3.1 Portals
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3.0 General time-of-use demand response
3.1 Battery storage 3.2 Commercial 3.3 Residential behavioral
3.3.1 Portals 3.4 Residential 3.5 Distribution system voltage control
4.0 General real-time continuum demand response
4.1 Battery storage 4.2 Commercial 4.3 Residential behavioral
4.3.1 Portals 4.4 Residential
Load Models--continued
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Cause and Effect Examples
• Three wind related scenarios:
– Case 1: Incentive for wind availability – Case 2: Incentive for wind ramp rate – Case 3: Incentive for balancing objective(s)
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Case 1: Incentive for wind energy availability • Predicted incentive signal increases when wind energy
decreases and visa versa • Incentive is communicated and mixed between
transactive nodes • Assets respond to improve consumption of wind
– When wind energy is available – Near where wind is available
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Transactive Node #1
Consider a Very Simple Topology
Transactive Node #2 22
Transactive Node #1
Assign Cost as a Function of Energy, Power and Time
Toolkit Resource Function #1
Toolkit Resource Function #2
P
$/M
Wh
P
$/M
Wh
Toolkit Resource Functions • Assign cost in a way that
will incentivize desired outcomes.
• Many different functions are possible, but acceptable functions must incur the same total cost over relatively long periods of time.
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Power from these Resources is Predicted into the Future
Transactive Node #1
Toolkit Resource Function #1
Toolkit Resource Function #2
P
$/MWh
P
$/MWh
0
2
4
6
8
10
12
0 6 12 18 24 30 36 42 48 54 60 66 72
Pow
er (M
W)
Time (h)
Power Generated at Transactive Node #1
Wind Farm Base Generation24
Compare Two Ways to Assign Unit Cost to these Resources
0
2
4
6
8
10
12
Uni
t Cos
t ($/
MW
h)
Time
Unit Costs - Now
Wind Base Generation Aggregate
0
2
4
6
8
10
12
14
16
18
Uni
t Cos
t ($/
MW
h)
Time
Unit Costs - Transactive Control
Wind Base Generation Aggregate25
Compare How Costs Accumulate
0
20
40
60
80
100
120
Hour
ly C
ost (
$/h)
Time
Hourly Resource Costs
Wind - Now
Wind- Transactive Control
Base Generation
0
2000
4000
6000
8000
10000
12000
Cost
($)
Time
Cumulative Cost
Transactive Control Now26
These two options are
identical over time
The Weighted Incentive (TIS) follows the Energy Exported from this Location
TIS
Transactive Node #1
0
5
10
15
TIS
($/M
Wh)
Time
TIS
-2
3
8
13
18
Win
d Po
wer
(MW
)
Wind Farm
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Responsive Assets and Toolkit Load Functions • Some system locations have controllable, responsive
generation and load assets • A “toolkit load function” is selected or created from scratch to
predict and model how the asset will respond as a function of – The incentive signal
• Absolute representation • Relative, statistical representation • History • Predictions
– Status of the asset – Other local information and conditions (e.g., weather)
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To Neighboring Locations that have Demand-Responsive Assets
Transactive Node #2
Toolkit Load Function #1
Toolkit Load Function #2
Toolkit Load Function #3
Incentive - $/MWh
ΔP
0
Incentive - $/MWh
ΔP
0
Incentive - $/MWh
ΔP
0
P
Incentive - $/MWh
Bulk Inelastic Load Function #4
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The Battery and Voltage Systems Figure Out When and How to Respond
• The battery and voltage control system used the entire predicted time horizon to determine when to best charge and discharge.
• The water heater system was not engaged by this modest event.
0
10
20
TIS
($/M
Wh)
TIS
TIS
-60
-40
-20
0
20
40
Chan
ge in
Pow
er (M
W)
Time
WH System Voltage Control Battery System
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Case 2: Incentive for Wind Ramp Rate • Predict rate of change in predicted wind energy
– Function of first and/or second derivatives of predicted wind resource
• Increase incentive at times wind will be decreasing and visa versa
– Encourage other generation resources or even curtailment of load at times that wind energy is decreasing
– Discourage other generation resources or even increase load at times that wind energy is increasing
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Case 2: Add Incentive for Rate of Wind Ramping
0
5
10
15
TIS
($/M
Wh)
Time
TIS
-50
-40
-30
-20
-10
0
10
20
30
40
Cha
nge
in P
ower
(MW
)
Time
WH System Voltage Control
Battery System
-2
3
8
13
18
Win
d Po
wer
(MW
)
Wind Farm
32
Applies where wind power changes
Increasingly richer
responses Superposed objectives and functions lead to …
Case 3: Incentive for Balancing Objective(s) • Create function to increment or decrement incentive
based on anticipated balance of resource and load – Increase incentive when there may be a deficit – Decrease incentive when there may be a surplus
• Address special circumstances, like times where wind farm production may become curtailed
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Case 3: Add Incentives for Balancing Objectives
-100
-50
0
50
100
Bala
nce
Sign
al ($
/h)
Balancing Signal
Deficit of Power
0
5
10
15
20
TIS
($/M
Wh)
TIS
-75
-25
25
75
Chan
ge in
Pow
er (M
W)
Time (h)
WH System Voltage Control
Battery System
0
2
4
6
8
10
12
Win
d Po
wer
(MW
)
Time (h)
Wind Power with Balancing
Wind farm shares balancing responsibility
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Surplus of Power
Signal modified to encourage others to share responsibility
Questions?
Ron Melton, Project Director [email protected] 509-372-6777 Don Hammerstrom, Principal Investigator [email protected] 509-372-4087 Project website: www.pnwsmartgrid.org
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