Multi-Agent Simulations of the Electricity Market in Central Europe

Transcription

1 Multi-Agent Simulations of the Electricity Market in Central Europe Audun Botterud *, Vladimir Koritarov, Prakash R. Thimmapuram Argonne National Laboratory Argonne, IL USA *

2 Outline Background Agent-based modeling and simulation Applications in electricity markets Electricity Market Complex Adaptive Systems (EMCAS) Market operation GenCo bidding strategies and price-responsive demand Case Study Central Europe Assumptions Case 1: production cost Case 2: impact of CO2 emission allowances Case 3: strategic bidding in Germany Case 4: demand response Conclusions and Future Work 2

3 Outline Background Agent-based modeling and simulation Applications in electricity markets Electricity Market Complex Adaptive Systems (EMCAS) Market operation GenCo bidding strategies and price-responsive demand Case Study Central Europe Assumptions Case 1: production cost Case 2: impact of CO2 emission allowances Case 3: strategic bidding in Germany Case 4: demand response Conclusions and Future Work 3

4 Agent-Based Modeling and Simulation (ABMS) ABMS uses sets of agents and a framework for simulating their decisions and interactions An agent Is a software representation of a decision-making unit Is a self-directed (autonomous) object with specific individual traits Typically exhibits bounded rationality under limited information Agent framework allows agents to interact in complex, dynamic ways Emerging patterns Convergence not guaranteed ABMS is well suited for analyzing decentralized decision-making in restructured electricity markets Strategic behavior, market power Market design 4

5 Some Applications to Electricity Markets Several applications to hypothetical test power systems/markets Nicolaisen et al (2001) Ernst et al (2004) Krause et al (2005) Botterud et al (2005) Also applications to real-world markets Bower and Bunn (2001): NETA rules, England&Wales Bunn and Oliveira (2001, 2003): NETA rules, England&Wales Bagnall (2005): UK Electricity market Cirillo et al (2006): New electricity market in Illinois, USA 5

6 Outline Background Agent-based modeling and simulation Applications in electricity markets Electricity Market Complex Adaptive Systems (EMCAS) Market operation GenCo bidding strategies and price-responsive demand Case Study Central Europe Assumptions Case 1: production cost Case 2: impact of CO2 emission allowances Case 3: strategic bidding in Germany Case 4: demand response Conclusions and future work 6

7 Electricity Market Complex Adaptive Systems (EMCAS) The main agents in the electricity market are represented in EMCAS: Electricity Markets Complex Adaptive Systems (EMCAS ( EMCAS) Regulator GenCos Generation Company TransCos Transmission Company Power Markets (Pool, Bilateral, and Ancillary) ISO/RTO DisCos Distribution Company Generation Company Transmission Company Scheduling Dispatch Settlement Distribution Company Industrial Consumers DemCos Residential Consumers Service Consumers 7

8 Simulated Market Operation in EMCAS Different generation technologies Thermal, hydro, wind, etc. Hourly chronological simulations Bid-based scheduling (day-ahead) and dispatch (real-time) based on DC-OPF Planned and forced generator outages Calculation of prices and profits based on two-settlement system Day-ahead (DA) schedule at DA price Deviations from DA at real-time (RT) price EMCAS can simulate the electricity market under different assumptions about agent behavior and market rules, e.g.: Congestion management; price and bid caps GenCo strategic bidding; demand response 8

9 GenCo Bidding Heat Rate Curve Incremental Cost Curve Block Bid Prices Heat Rate (Btu/kWh) Incr. Cost ($/MWh) Bid Prices ($/MWh) Heat Rate Curve Average Incr. HR Curve Power Output (MW) Power Output (MW) Power Output (MW) Production cost bidding Bidding according to incremental heat rate and cost curve Represents competitive bidding Generators may not recover all operating costs Impact of CO2 allowances price Marginal cost of CO2 emissions added to bid blocks (full cost or fraction) Addition in cost for each block depends on fuel type and heat rate 9

10 GenCo Strategic Bidding Physical Withholding based on System Reserve (PWSR) GenCo withholds capacity in hours when the expected SR is below a specified limit GenCo tries to reduce SR with a target amount in those hours by withholding units GenCo bids production cost for remaining units Fixed Increment Price Probing (FIPP) GenCo increases its bid w/fixed percentage for the accepted hours GenCo decreases its bid w/fixed percentage for the rejected hours A lower limit on bid price can be specified true false Bid, is accepted d h Bid d+ 1, h : = ( 1+ α ) Bid d, h Bid d+ 1, h : = ( 1 α ) Bidd, h 10

11 Price Responsive Demand in Day-Ahead Market Price Cap DA Demand curve DA Supply curve P ref Continuous demand curve with constant elasticity, ε. P DA L ref L DA Load in RT market, L RT, is fixed (inelastic) and equal to L DA 11

12 Outline Background Agent-based modeling and simulation Applications in electricity markets Electricity Market Complex Adaptive Systems (EMCAS) Market operation GenCo bidding strategies and price-responsive demand Case Study Central Europe Assumptions Case 1: production cost Case 2: impact of CO2 emission allowances Case 3: strategic bidding in Germany Case 4: demand response Conclusions and future work 12

13 Central Europe Assumptions General Data The data used are highly aggregated and from several sources All simulations are for one year (representing 2006) Purpose is to illustrate use of ABMS for electricity market analysis Numerical results have limited validity 13

14 Central Europe Assumptions Network Geographic area Six countries: Germany (DEn, DEs), Poland (PL), Czech Republic (CZ), Austria (AT), Slovakia (SK), Hungary (HU) Boundary nodes: Switzerland/Italy (Ch+IT), Southeast Europe (SETSO+RO), and Ukraine (UA) Aggregate Network Configuration 14

15 Central Europe Assumptions Demand Hourly chronological loads for industry and households in each node Consumer payments for energy Germany: all consumers pay market price for energy Other countries: industry pay market price and households pay a regulated energy price of 20 /MWh Transmission and distribution charge: 20 /MWh for all consumers DEn DEs PL CZ AT SK HU CH+IT SETSO +RO Annual load [TWh] Peak load [GW] % Households % Industrial

16 Central Europe Assumptions Generation Hypothetical GenCos Aggregate representation of power plants (68 in total) Generic technology specifications with some variations in fuel price Installed plant capacity equal to 2005/2006 conditions Historical hydro conditions from 2003 Nuclear Coal Natural Gas Hydro Wind Total DE DE DE DE DE PL PL PL CZ CZ AT SK HU Total

17 Results - Case 1: Production Cost Simulated prices Very low prices with incr. production cost bidding. Average DA prices ( /MWh) : Most GenCos have an operating loss Some price differences due to congestion. Congestion from CZ: Percentage loading CZ-AT CZ-DEn CZ-DEs CZ-SK CZ-PL Time [hour] 17

18 Results - Case 2: Impact of GHG Emission Allowances CO2 Emissions [M tonnes] Average DA Price [ /MWh] CO2 emissions DA price DEn GHG Allowance Price [ /tonne CO2] 0 CO2 emissions drop as function of higher allowance price Geographical variations (increase in AT and SK) Prices increase as function of higher allowance price All except one GenCo make operating profits w/allowance price of 20 /tonne GHG allowances are assumed to be grandfathered No financial settlement of allowances included in this analysis 18

19 Results - Case 3: Strategic Bidding in Germany - PWSR Physical Withholding based on System Reserve (PWSR) Withholding when SR < 40 % Aiming at a reduction of 4 percentage points Applied to coal- and gas-fired plants Three scenarios 3a: GenCo DE3 applies PWSR strategy 3b: GenCo DE2+3 apply PWSR strategy 3c: All German GenCos apply PWSR strategy Simulated Results Percentage change in GenCo profits compared to Case 2: Case Scenario DE1 DE2 DE3 DE4 DE5 Total 3a DE b DE c DE All Small increase in German prices and consumer cost in scenarios 3a and 3b High increase in German prices and consumer cost in scenario 3c More than 100 hours of curtailment w/price equal to price cap 999 /MWh 19

20 Results - Case 3: Strategic Bidding in Germany - FIPP Fixed Increment Price Probing (FIPP) Daily bid adjustment: 10 % Lower bid price equal to production cost Applied to coal- and gas-fired plants Three scenarios 3d: GenCo DE3 applies FIPP strategy 3e: GenCo DE2+3 apply FIPP strategy 3f: All German GenCos apply FIPP strategy Simulated Results Percentage change in GenCo profits compared to Case 2: Case Scenario DE1 DE2 DE3 DE4 DE5 Total 3d DE e DE f DE All Small increase in German prices and consumer cost in scenarios 3d and 3e High increase in German prices and consumer cost in scenario 3f 20

21 Results - Case 4: Price-Responsive Demand All industrial demand in Germany price responsive Price elasticity of demand steps from % of reference load Repeated PWSR (case 4c) and FIPP (case 4f) with all German GenCos Simulated Percentage change in GenCo profits compared to Case 2 Physical withholding, PWSR: Case Scenario DE1 DE2 DE3 DE4 DE5 Total 3c DE All c DE All + DR Price probing, FIPP: Case Scenario DE1 DE2 DE3 DE4 DE5 Total 3f DE All f DE All + DR Price responsive demand clearly reduces market power potential 21

22 Simulated DA prices in DE-North, FIPP - Cases 2, 3d, 3e 60 Case 3e-Two GenCos Case 3d-One GenCo Case 2-Reference Price [ /MWh] Time [hour] 22

23 Simulated DA prices in DE-North, FIPP - Cases 3f, 4f Case 3f-All GenCos Case 4f-Demand Response Price [ /MWh] Time [hour] 23

24 Outline Background Agent-based modeling and Simulation Applications in electricity markets Electricity Market Complex Adaptive Systems (EMCAS) Market operation GenCo bidding strategies and price-responsive demand Case Study Central Europe Assumptions Case 1: production cost Case 2: impact of CO2 emission allowances Case 3: strategic bidding in Germany Case 4: demand response Conclusions and Future Work 24

25 Conclusions and Future Work Advantages of using ABMS for market power analysis More realistic market behavior than equilibrium models Detailed representation of physical system and market rules Some challenges with ABMS Complex to analyze results (often necessary to run a high number of simulations) Difficult to draw general conclusions Case study CO2 scheme increases price and GenCo profits, reduces CO2 emissions Market power only under strong assumptions about collusive behavior Price responsive demand an important counter-measure to market power Future work Implement more market design alternatives Further development of bidding strategies Modeling of long-term expansion decisions Visualization of results Improving input data for Central European case 25

26 Multi-Agent Simulations of the Electricity Market in Central Europe Audun Botterud *, Vladimir Koritarov, Prakash R. Thimmapuram Argonne National Laboratory Argonne, IL USA *

27 Summary of Results PWSR cases 27

28 Summary of Results FIPP cases 28