An Agent-Based Computational Laboratory for Wholesale Power Market Design

Transcription

1 An Agent-Based Computational Laboratory for Wholesale Power Market Design Project Director: Leigh Tesfatsion (Professor of Econ & Math, ISU) Research Associate: Junjie Sun (Fin. Econ, OCC, U.S. Treasury, Wash, D.C.) Research Assistant: Hongyan Li (PhD. Cand, Electrical & Comp Eng., ISU) funded in part by the National Science Foundation and the Electric Power Research Center Work in Progress

2 Presentation Outline Project Context Agent-Based Modeling of Electricity Systems (AMES) The AMES Market Package (Java) Graphical User Interface for AMES Market Package Dynamic 5-Node 5 Test Case Conclusion AMES Market Package Home Page (Code Release Site): 2

3 Project Context In April 2003, the U.S. Federal Energy Regulatory Commission (FERC) proposed a new market design for U.S. wholesale power markets. It involves a complicated interweaving of real and financial markets for energy, ancillary services, and financial instruments. This project is developing a computational open- source test-bed (AMES=Agent-based Modeling of Electricity Systems) with learning traders for studying the dynamic reliability and efficiency of this new market design. 3

4 ISOs/RTOs Adopting Versions of Design to Date 4

5 Why Resistance to FERC s Proposed Market Design? Regions Already Operating Under the Design Key cited problems: Lack of sufficient performance testing; Price volatility (across the transmission grid & over time). Stakeholders in Southeast and Northwest Key cited problems: Lack of sufficient performance testing; Questions about suitability given special local conditions (TVA, hydroelectric power...).

6 Actual Electricity Prices in the MISO September 5, 2006, 14:30 Note this price, $

7 Five Minutes Later 79% drop in price in 5 minutes! 7

8 Our AMES Market Project (AMES = Agent-based Modeling of Electricity Systems) Who might benefit? Researchers/teachers Industry stakeholders Policy makers

9 AMES Market Project (AMES = Agent-based Modeling of Electricity Systems) Target Features Research/teaching grade test-bed (2-500 pricing nodes) Operational validity (structure, rules, behavioral dispositions) Permits dynamic testing with learning traders Permits intensive experimentation with alternative scenarios Free open-source Java implementation (full access to code) Easily modified (extensible/modular architecture) 9

10 AMES Market Project Iterative Participatory Modeling Approach Joining together with industry stakeholders and researchers from multiple disciplines in a repeated looping through 4 stages of analysis: Field work and data collection; Scenario discussion/role-playing games; Agent-based test-bed development; Intensive computational experiments. NOTE: See Barreteau et al. (JASSS, 6-1,2003) 6

11 Key Components of AMES Market Test-Bed (Based on Business Practices Manuals for MISO/ISO-NE) Traders Sellers and buyers Follow market rules Learning abilities Two-settlement process ( contracts Day-ahead market (double auction, financial ( differences Real-time market (settlement of AC transmission grid Independent System Operator System reliability assessments Day-ahead bid-based unit commitment Real-time dispatch Sellers/buyers located at various transmission nodes ( LMP ) Congestion managed via Locational Marginal Pricing 11

12 AMES Market Test-Bed: Agent Hierarchy World Traders Transmission Grid Markets ISO Buyers Sellers Reliability Commitment Dispatch Settlement Load Serving Entities Generators Bilateral FTR Day- Ahead Real- Time Re-Bid Period 12

13 Activities of AMES ISO During Each Operating Day D Real- Time (Spot) Market for D Real-Time Settlement 00:00 11:00 17:00 18:00 23:00 Day-Ahead Market for D+1 (ISO collect bids/offers from LSEs and Generators) ISO evaluates demand bids and supply offers Re-Bid Period for D+1 ISO solves D+1 DC OPF problem & posts D+1 commitment/price schedule Day-Ahead Settlement

14 Dynamic Interactions in No-Shock Case (Day-Ahead Market Contracts Carried Out As Planned Without Need for Real-Time Market Transactions) Learning Module GUI Profits Bids Buyers (LSEs) LMPs Bids Profits Offers Sellers (Generators) LMPs Offers Market Operator DC-OPF Solution Module Input Data Output Data G5 LSE 3 G4 G1 G2 LSE 1 G3 LSE 2

15 AMES Market Test-Bed: Software Development to Date Given Gen supply behavior Learned Generator supply behavior ( situation (Typical econ. lit. assumption) (Actual MISO/ISO-NE No trans./cap. constraints Transmission/capacity constraints ( situation (Typical econ. lit. assumption) (Actual MISO/ISO-NE No market power mitigation ISO oversight & MP mitigation ( situation (Typical econ. lit. assumption) (Actual MISO/ISO-NE Fixed demand bids (loads) Price-sensitive LSE demand bids ( situation (Typical econ. lit. assumption) (Actual MISO/ISO-NE No GUI capability GUI capability. 15

16 Code Release: The AMES Market Package (Java) The AMES Market Test-Bed has been implemented entirely in Java as the AMES Market Package The AMES Market Package has been released as free open-source software under the terms of the GNU General Public License (GPL) For software downloads, manuals, and research pubs, see the AMES Market Package Home Page: 16

17 GUI for AMES Market Package Provision of standard graphical user interface (GUI) functions for: Creation, modification, analysis, and storage of scenarios Parameter initialization and editing Specification of different methods for market participants (e.g. learning methods) Output table and chart displays Visualization of transmission grid (in progress) 17

18 GUI Tool Bar and Screens for Data Input and for Output Display (Charts,Tables, ) 18

19 GUI Data Input for a New Scenario Step 1: Input basic system attributes (e.g. total numbers of generators, LSEs and transmission grid nodes. Step 2: Input transmission grid line/node attributes (e.g.line locations, reactances, thermal limits, base voltages, ) Step 3: Input Generator and LSE attributes Step 4: Input learning methods/parameter values Step 5: Step 5: Input parameter values for simulation control (e.g.simulation stopping rule, random seed value, ) 19

20 Output Chart Illustration 20

21 Output Table Illustration LMPs (Nodal Prices) at 8:00AM on Successive Days 21

22 Illustrative Dynamic 5-Node Test Case Dynamic Extension of Static ISO-NE/PJM Training Example with LSE Fixed Demands (Loads) 600 G5 Node 5 Node 4 load LSE Cheap $ Thermal Limit 250 G4 Peaker $$$ Node 1 Node Node 3 G1 G LSE 1 G3 LSE 2 Cheap $ load $$ load 22

23 Daily LSE Fixed Demands (Load Profiles) for Dynamic 5-Node Test Case Load (in MWs) Hour LSE1 at Node2 LSE2 at Node3 LSE3 at Node4 23

24 Hour-17 Outcomes with No Learning ( curves (Generator supply offers = True marginal cost 600 G5 Node 5 Node 4 Load=320 LSE $ Limit=250 CONGESTED G4 $$$ Node 1 Node 2 Node 3 G1 G LSE 1 G3 LSE 2 Load=449 Load=385 $ $ $$ 24

25 Day 422 Hour-17 Outcomes with Gen Learning (Each Generator has converged with Prob 0.999) G5 Node 5 Node 4 Load=320 LSE 3 G4 $ Limit=250 CONGESTED $$$ Node 1 Node 2 Node 3 G1 G2 LSE 1 G3 LSE 2 Load=449 $ $ $$ Load=

26 True Vs. Reported MC (Averaged)* on Day 422 ( Prob (Each Generator has converged with Gen 2: True vs. Reported Average MC Gen 3: True vs. Reported Average MC Marginal Cost Reported MC True MC Marginal Cost Reported MC True MC Power Production Level Power Production Level Gen 4: True vs. Reported Average MC Gen 5: True vs. Reported Average MC Marginal Cost Reported MC True MC Marginal Cost Reported MC True MC Power Production Level Power Production Level *NOTE: 20-run averages. Typical convergence time = 62 days, max time = Omitted Gen 1 MC curve is similar to Gen 2 s.

27 Minimum Total Variable Costs on Day 422: No Learning Compared With Generator Learning 120, ,000 80,000 Cost 60,000 Gen Learning No Learning 40,000 20, Hour 27

28 Day Hour Power Production Outcomes No Learning vs. Generator Learning PG1 PG2 PG3 PG4 PG5 No Learning 24-Hour Power Production (Generator supply offers = true marginal cost curves) PG1 PG2 PG3 PG4 PG5 Generator Learning 24-Hour Power Production (Each Generator has converged to one offer with Prob 0.999)

29 Day Hour Locational Marginal Price Outcomes No Learning vs. Generator Learning LMP1 LMP2 LMP3 LMP4 LMP5 No Learning 24-Hour Locational Marginal Prices (Generator supply offers = true marginal cost curves) LMP1 LMP2 LMP3 LMP4 LMP5 Generator Learning 24-Hour Locational Marginal Prices (Much Higher!) (Each Generator has converged to one offer with Prob 0.999)

30 Dynamic 5-Node Test Case BOTTOM LINE: Learning Matters! 30

31 AMES Market Project Research in Progress Explore market power and market efficiency effects under alternative treatments for: price-sensitivity of LSE demand bids numbers and locations of Generators and LSEs learning capabilities price caps Explore reliability and market efficiency under normal conditions and unanticipated conditions (line and/or generator outages, demand changes, ) 31

32 Conclusion The AMES Market Package is an empirically-based open-source tool for research, teaching, and training applications. It provides a user-friendly GUI facilitating intensive computational wholesale power market experiments. Key issues currently under study: Do current U.S. wholesale power market designs ensure market efficiency and reliability under both normal and unanticipated conditions? What improvements to the designs could be made? AMES Market Package Home Page 32