Multi-Time-Scale Market Implementation on SGRS

Simulation Based Transactive
Energy - Spot, Forward and
Investment Decisions
Chin Yen Tee
Martin Wagner
Marija Ilic
Smart Grid in a Room Simulator
* Provisional Patent
Matlab algorithms
Actor orientation
Scalability
Distributed simulation
Event - Driven
DyMonDS*
Power system
Simulations
SGRS
State-machine driven
Vision of SGRS
Facility for researchers and policymakers to test novel,
intelligent decision making frameworks, control designs, and
institutional structures. Use case examples:
Distributed automated modelling of power system
dynamic  [1]
Retail market for reliability [2]
Participation of electric vehicle in retail market [3]
3
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+step():
State 0: Wait for 
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              Send Start Signal to Start Simulation
State 1: Wait for Forward and RT Bids
              Clear Forward and RT Market
              Send Dispatch and Prices
              
Iterate
State 2: Send Estimated Future Prices
  Wait for Investment Bids
Clear Investment Market (If Appropriate)
  Iterate
 
Multi Time-Scale Market
Implementation on SGRS
4
LSE
+step():
State 0: 
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              Send 
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State 1: Wait for 
Forward and RT Prices
              
Calculate Forward and RT market bids
RT market bids for time t
Forward market bids for time t + T
              Send Bids to ISO
  
Iterate
Gen
+step():
State 0: 
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State 1: Wait for 
Forward and RT Prices
              
Calculate Forward and RT market bids
              Send Bids to ISO
  
Iterate
State 2: Wait for Estimated Future Price
 
Calculate Investment Bids
 
Submit Bids to ISO
Iterate
Transmission Owner
+step():
State 0: 
Wait for Investment Start Signal
State 2: Wait for Estimated Future Price
 
Calculate Investment Bids
 
Submit Bids to ISO
Iterate
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Test on 6 Bus Test System
5
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Modified Garver 6 Bus Test System
Use NE ISO Historical Load Data to
build load model
Generation and transmission
investment under different market
structure
Spot Only
Spot + Independent Investment
and Forward Market
Spot + Coordinated Investment
and Forward Market
* Darker color: More expensive generation
**Blue line: Line 3 in results
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Differences in Investment
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7
Comparison of Spot and Forward Price for Coordinated (C) vs
Independent(I) Forward Clearing for Typical Load Days
 
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Generator 1 Revenue With and Without Forward
Market for Case with Independent Forward
Market (50 Real Time Load Realizations)
9
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Conclusion
Simulators such as the SGRS are well suited for simulation
based market design for transactive energy markets
Aid in understanding market effects (e.g. who wins/who
loses)
Aid in understanding interaction between markets at
different time scales
Help identify design questions for further evaluation
Modular, state-machine based simulation framework design
makes it easy to quickly test different market structures once
the basic framework has been established
11
References
[1]M. Wagner, K. Bachovchin, M. Ilic, "Computer Architecture
and Multi Time-Scale Implementations for Smart Grid in a
Room Simulator," 9th IFAC Symposium on Control of Power
and. Energy Systems (CPES), New Delhi, India, December
2015.
[2] Siripha Junlakarn (2015),Retail Market Mechanism in
Support of Differentiated Reliable Electricity Services, PhD
Dissertation, Carnegie Mellon University
[3] Jonathan Donadee (2015), Operation and Valuation of
Multi-Function Energy Storage Under Uncertainty, PhD
Dissertation, Carnegie Mellon University
12
Extended Slide Deck
** Whitepaper to be released in June 2016. Email 
ctee@andrew.cmu.edu
 to request copy of whitepaper.
13
SGRS Platform Overview
Vision of SGRS
Facility for user/algorithm designers to test
and scale their algorithms to real world
problems. Use case examples:
Distributed automated modelling of power
system dynamic  [1]
Retail market for reliability [2]
Participation of electric vehicle in retail market [3]
15
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Smart Grid in a Room Simulator
* Provisional Patent
Matlab algorithms
Actor orientation
Scalability
Distributed simulation
Event - Driven
DyMonDS*
Power system
Simulations
SGRS
State-machine driven
Transactive Energy Market Design
Many different forms/proposals
TeMix work of Ed Cazalet
Double auction market (E.g. GridWise Olympic
Peninsula Demonstration)
Fundamental ideas:
Distributed decision making at value
Multiple time-scale market
17
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Simulation-Based Market Design
18
ISO
+step():
State 0: Wait for 
Start Signal from LSE
              Send Start Signal to Start Simulation
State 1: Wait for Forward and RT Bids
              Clear Forward and RT Market
              Send Dispatch and Prices
              
Iterate
State 2: Send Estimated Future Prices
  Wait for Investment Bids
Clear Investment Market (If Appropriate)
  Iterate
 
Multi Time-Scale Market
Implementation on SGRS
19
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+step():
State 0: 
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State 1: Wait for 
Forward and RT Prices
              
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RT market bids for time t
Forward market bids for time t + T
              Send Bids to ISO
  
Iterate
Gen
+step():
State 0: 
Wait for Start Signal
State 1: Wait for 
Forward and RT Prices
              
Calculate Forward and RT market bids
              Send Bids to ISO
  
Iterate
State 2: Wait for Estimated Future Price
 
Calculate Investment Bids
 
Submit Bids to ISO
Iterate
Transmission Owner
+step():
State 0: 
Wait for Investment Start Signal
State 2: Wait for Estimated Future Price
 
Calculate Investment Bids
 
Submit Bids to ISO
Iterate
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Test on 6 Bus Test System
21
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2
 
Modified Garver 6 Bus Test System
Use NE ISO Historical Load Data to
build load model
Generation and transmission
investment under different market
structure
Spot Only
Spot + Independent Forward
Market
Spot + Coordinated Investment
and Forward Market
* Darker color: More expensive generation
**Blue line: Line 3 in results
Independent vs. Coordinated
Forward and Investment Markets
22
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Simulation Assumptions
Simulated 4 years - 3 investment cycles. 1 representative day
per month simulated per year
 Future prices/bids estimated based on a stochastic
distribution of potential future load conditions
No investment lead time
Relatively simple bidding strategy used for testing
Generation bidding is non-gaming. (i.e SRMC for spot
market and LRMC for forward market)
Load bids consist of fix and elastic portion.  Load
penalized if its forward load purchase is less than 80% of
what it requires from the grid in real time.
23
Nodal Prices for Coordinated Investment + Forward Iterative
Market Clearing For Different Iteration
24
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25
Comparison of Spot and Forward Price for Coordinated (C) vs
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26
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27
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28
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(50 Real Time Load Realizations)
29
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)
Conclusion
Simulators such as the SGRS are well suited for simulation
based market design for transactive energy markets
Aid in understanding market effects (e.g. who wins/who
loses)
Aid in understanding interaction between markets at
different time scales (e.g. interaction between forward
market and investment decisions)
Simulation aided market design helps identify design
questions for further evaluation
30
References
[1]M. Wagner, K. Bachovchin, M. Ilic, "Computer Architecture
and Multi Time-Scale Implementations for Smart Grid in a
Room Simulator," 9th IFAC Symposium on Control of Power
and. Energy Systems (CPES), New Delhi, India, December
2015.
[2] Siripha Junlakarn (2015),Retail Market Mechanism in
Support of Differentiated Reliable Electricity Services, PhD
Dissertation, Carnegie Mellon University
[3] Jonathan Donadee (2015), Operation and Valuation of
Multi-Function Energy Storage Under Uncertainty, PhD
Dissertation, Carnegie Mellon University
31
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Vision of SGRS facility for researchers and policymakers to test novel decision-making frameworks, SGRS simulator for power system simulations, and simulation-based transactive energy market design. The process involves waiting for signals, calculating bids, submitting investment bids, and clearing markets in different states. Use of NE ISO historical load data to build load model and test various market structures like spot, independent investment, coordinated investment, and forward markets on a modified Garver 6-bus test system.

  • Simulation
  • Transactive energy
  • Power systems
  • Market implementation
  • Smart grid

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  1. Simulation Based Transactive Energy - Spot, Forward and Investment Decisions Chin Yen Tee Martin Wagner Marija Ilic

  2. Smart Grid in a Room Simulator Matlab algorithms State-machine driven Distributed simulation Actor orientation SGRS Event - Driven DyMonDS* Power system Simulations Scalability * Provisional Patent

  3. Vision of SGRS Facility for researchers and policymakers to test novel, intelligent decision making frameworks, control designs, and institutional structures. Use case examples: Distributed automated modelling of power system dynamic [1] Retail market for reliability [2] Participation of electric vehicle in retail market [3] Focus of Today s Presentation: Simulation-based Transactive Energy Market Design 3

  4. Multi Time-Scale Market Implementation on SGRS Gen +step(): State 0: Wait for Start Signal LSE +step(): State 0: Wait for Load Series/Forecast Send Signal To ISO Start Simulation State 1: Wait for Forward and RT Prices Calculate Forward and RT market bids Send Bids to ISO Iterate State 1: Wait for Forward and RT Prices Calculate Forward and RT market bids RT market bids for time t Forward market bids for time t + T Send Bids to ISO Iterate State 2: Wait for Estimated Future Price Calculate Investment Bids Submit Bids to ISO Iterate Transmission Owner ISO +step(): State 0: Wait for Investment Start Signal +step(): State 0: Wait for Start Signal from LSE Send Start Signal to Start Simulation State 2: Wait for Estimated Future Price Calculate Investment Bids Submit Bids to ISO Iterate State 1: Wait for Forward and RT Bids Clear Forward and RT Market Send Dispatch and Prices Iterate State 2: Send Estimated Future Prices Wait for Investment Bids Clear Investment Market (If Appropriate) Iterate Additional Load Simulator object to generate load series and forecast 4

  5. Test on 6 Bus Test System Modified Garver 6 Bus Test System 1 5 Use NE ISO Historical Load Data to build load model Generation and transmission investment under different market structure Spot Only Spot + Independent Investment and Forward Market Spot + Coordinated Investment and Forward Market 4 3 2 6 5 * Darker color: More expensive generation **Blue line: Line 3 in results

  6. Independent vs. Coordinated Forward and Investment Markets Independent Forward and Investment Markets 0 T T+t T +2t t 2t @ t: Spot Market for t, Forward Market for time T+t @T: Investment Market @ 2t: Spot Market for 2t Forward Market for time T+2t Coordinated Forward and Investment Markets 0 T T+t T +2t t 2t @T: Investment Market + Forward Market for all t between T and 2T @ t: Spot Market for t @ 2t: Spot Market for 2t Modular, state-machine driven simulation design makes it easy to implement different multi-timescale market design once basic framework has been established. 6

  7. Differences in Investment Decisions Generation Investment Transmission Investment Case Spot Only None Investment in Line 3 Spot + Independent Forward Investment in Gen 1 and Gen 6 Investment in Line 3 Spot + Coordinated Forward None Investment in Line 3 7

  8. Comparison of Spot and Forward Price for Coordinated (C) vs Independent(I) Forward Clearing for Typical Load Days Coordinated forward and investment clearing typically results in lower forward price as it accounts for effect of transmission/generation investment Lower forward price discourages investment 8

  9. Generator 1 Revenue With and Without Forward Market for Case with Independent Forward Market (50 Real Time Load Realizations) Forward market in this case results in higher generator revenue on average market structure favors generator 9

  10. Generator 1 Revenue With and Without Forward Market for Case with Coordinated Forward Market (50 Real Time Load Realizations) Forward market in this case results in lower generator revenue on average market structure favors load (Recall that generator did not invest in this case) 10

  11. Conclusion Simulators such as the SGRS are well suited for simulation based market design for transactive energy markets Aid in understanding market effects (e.g. who wins/who loses) Aid in understanding interaction between markets at different time scales Help identify design questions for further evaluation Modular, state-machine based simulation framework design makes it easy to quickly test different market structures once the basic framework has been established 11

  12. References [1]M. Wagner, K. Bachovchin, M. Ilic, "Computer Architecture and Multi Time-Scale Implementations for Smart Grid in a Room Simulator," 9th IFAC Symposium on Control of Power and. Energy Systems (CPES), New Delhi, India, December 2015. [2] Siripha Junlakarn (2015),Retail Market Mechanism in Support of Differentiated Reliable Electricity Services, PhD Dissertation, Carnegie Mellon University [3] Jonathan Donadee (2015), Operation and Valuation of Multi-Function Energy Storage Under Uncertainty, PhD Dissertation, Carnegie Mellon University 12

  13. Extended Slide Deck ** Whitepaper to be released in June 2016. Email ctee@andrew.cmu.edu to request copy of whitepaper. 13

  14. SGRS Platform Overview

  15. Vision of SGRS Facility for user/algorithm designers to test and scale their algorithms to real world problems. Use case examples: Distributed automated modelling of power system dynamic [1] Retail market for reliability [2] Participation of electric vehicle in retail market [3] Focus of Today s Presentation: Simulation-based Transactive Energy Market Design 15

  16. Smart Grid in a Room Simulator Matlab algorithms State-machine driven Distributed simulation Actor orientation SGRS Event - Driven DyMonDS* Power system Simulations Scalability * Provisional Patent

  17. Transactive Energy Market Design Many different forms/proposals TeMix work of Ed Cazalet Double auction market (E.g. GridWise Olympic Peninsula Demonstration) Fundamental ideas: Distributed decision making at value Multiple time-scale market SGRS designed to manage these fundamental ideas to test different transactive energy market designs. 17

  18. Simulation-Based Market Design 18

  19. Multi Time-Scale Market Implementation on SGRS Gen +step(): State 0: Wait for Start Signal LSE +step(): State 0: Wait for Load Series/Forecast Send Signal To ISO Start Simulation State 1: Wait for Forward and RT Prices Calculate Forward and RT market bids Send Bids to ISO Iterate State 1: Wait for Forward and RT Prices Calculate Forward and RT market bids RT market bids for time t Forward market bids for time t + T Send Bids to ISO Iterate State 2: Wait for Estimated Future Price Calculate Investment Bids Submit Bids to ISO Iterate Transmission Owner ISO +step(): State 0: Wait for Investment Start Signal +step(): State 0: Wait for Start Signal from LSE Send Start Signal to Start Simulation State 2: Wait for Estimated Future Price Calculate Investment Bids Submit Bids to ISO Iterate State 1: Wait for Forward and RT Bids Clear Forward and RT Market Send Dispatch and Prices Iterate State 2: Send Estimated Future Prices Wait for Investment Bids Clear Investment Market (If Appropriate) Iterate Additional Load Simulator object to generate load series and forecast 19

  20. Time Evolution on SGRS T_start: 2008-01-01T00:00:00.0Z T_step: PT60M LoadSimulator: LoadForecast: LSE: LoadForecast LSE,Gen: Bid ISO: Dispatch&Prices LSE,Gen: Bid T1: 2008-01-01T01:00:00.0Z Operation Cycle (1 Hour) ISO: Dispatch&Prices T2: 2008-01-01T02:00:00.0Z Events Wall Clock Time Simulation Time ISO: EstimatedPrice Trans,Gen:Investment Bid T8760: 2009-01-01T00:00:00.0Z T8761: 2009-01-01T00:01:00.0Z Investment Cycle (1 Year) T8762: 2009-01-01T00:01:00.0Z Step function of Object called T17520: 2010-01-01T00:00:00.0Z 20 20

  21. Test on 6 Bus Test System Modified Garver 6 Bus Test System 1 5 Use NE ISO Historical Load Data to build load model Generation and transmission investment under different market structure Spot Only Spot + Independent Forward Market Spot + Coordinated Investment and Forward Market 4 3 2 6 21 * Darker color: More expensive generation **Blue line: Line 3 in results

  22. Independent vs. Coordinated Forward and Investment Markets Independent Forward and Investment Markets 0 T T+t T +2t t 2t @ t: Spot Market for t, Forward Market for time T+t @T: Investment Market @ 2t: Spot Market for 2t Forward Market for time T+2t Coordinated Forward and Investment Markets 0 T T+t T +2t t 2t @ t: Spot Market for t @ 2t: Spot Market for 2t @T: Investment Market + Forward Market for all t between T and 2T 22

  23. Simulation Assumptions Simulated 4 years - 3 investment cycles. 1 representative day per month simulated per year Future prices/bids estimated based on a stochastic distribution of potential future load conditions No investment lead time Relatively simple bidding strategy used for testing Generation bidding is non-gaming. (i.e SRMC for spot market and LRMC for forward market) Load bids consist of fix and elastic portion. Load penalized if its forward load purchase is less than 80% of what it requires from the grid in real time. 23

  24. Nodal Prices for Coordinated Investment + Forward Iterative Market Clearing For Different Iteration Converged in 3 Iteration. For convergence, need to impose rule that LSE bids have to be greater than the bids in the previous iteration. (Area for Further Study) 24

  25. Differences in Investment Decisions Generation Investment Transmission Investment Case Spot Only None Investment in Line 3 Spot + Independent Forward Investment in Gen 1 and Gen 6 Investment in Line 3 Spot + Coordinated Forward None Investment in Line 3 25

  26. Comparison of Spot and Forward Price for Coordinated (C) vs Independent(I) Forward Clearing for Typical Load Days Coordinated forward and investment clearing typically results in lower forward price as it accounts for effect of transmission/generation investment Lower forward price discourages investment 26

  27. Comparison of Spot and Forward Price for Coordinated (C) vs Independent(I) Forward Clearing for High Load Days In this case, this reduction in investment due to typically lower forward price results in greater volatility in spot prices for the coordinated case during peak load days. 27

  28. Generator 1 Revenue With and Without Forward Market for Case with Independent Forward Market (50 Real Time Load Realizations) Forward market in this case results in higher generator revenue on average market structure favors generator 28

  29. Generator 1 Revenue With and Without Forward Market for Case with Coordinated Forward Market (50 Real Time Load Realizations) Forward market in this case results in lower generator revenue on average market structure favors load (Recall that generator did not invest in this case) 29

  30. Conclusion Simulators such as the SGRS are well suited for simulation based market design for transactive energy markets Aid in understanding market effects (e.g. who wins/who loses) Aid in understanding interaction between markets at different time scales (e.g. interaction between forward market and investment decisions) Simulation aided market design helps identify design questions for further evaluation 30

  31. References [1]M. Wagner, K. Bachovchin, M. Ilic, "Computer Architecture and Multi Time-Scale Implementations for Smart Grid in a Room Simulator," 9th IFAC Symposium on Control of Power and. Energy Systems (CPES), New Delhi, India, December 2015. [2] Siripha Junlakarn (2015),Retail Market Mechanism in Support of Differentiated Reliable Electricity Services, PhD Dissertation, Carnegie Mellon University [3] Jonathan Donadee (2015), Operation and Valuation of Multi-Function Energy Storage Under Uncertainty, PhD Dissertation, Carnegie Mellon University 31

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