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PhD Thesis Presentation hold on 4.25.2008 in Argentina

PhD Thesis Presentation hold on 4.25.2008 in Argentina

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    PhD Thesis Presentation PhD Thesis Presentation Presentation Transcript

    • Doctoral Examination Assessment of Short-term Strategic Behavior f h i h i in Electricity Markets Introduction Analysis Modeling Results Conclusions Ing. Pablo Frezzi San Juan, 25/04/2008
    • Introduction 1 Motivation (1) Characteristics of the present electricity markets Impossibility to store economically large amounts of electricity Low price elasticity of demand Repeated interaction Significant economies of scale Transmission constraints Market M k t concentration as a consequence of i ffi i t di tit t ti f inefficient divestiture and d consolidations Electricity markets are not perfectly competitive In contrast to perfectly competitive markets, market participants do not play a passive role as „price takers“ Price and market dynamic depend on market participants‘ strategies to maximize profits Strategic behavior: individual or group action to increase profits by means of overt or tacit agreements influencing the market variables Market po e individual profit-maximizing action a ke power: d dua p o ax g ac o Collusion: cooperative profit-maximizing action
    • Introduction 2 Motivation (2) Consequences of strategic behavior: Wealth transfer from customers to producers p Deadweigh loss and and reduction of social welfare Supply shortages pursue Price volatility l l Distortion of price signals which may lead to inefficient investments Physical ithh ldi Ph i l withholding Economic withholding E i ithh ldi Wealth transfer Demand Offer Wealth transfer Demand Offer Price Price Costs Costs PSM PSM PVM PVM Deadweigh loss Deadweigh loss Withheld Withheld capacity capacity 0 QSM QVM Quantity 0 QSM QVM Quantity Strategic behavior may affect the benefits pursued by liberalizing processes Need of models to reproduce actual strategic behavior in electricity markets
    • Introduction 3 Research Aim Development of a simulation model of electricity markets to reproduce and p y p assess the strategic behavior of market participants Specific aims: f Indentification and proof of exercise of strategic behavior in electricity markets Quantification of the influence of strategic behavior on the electricity price Analysis of the influence of individual behavior on the short-term dynamic of electricity markets, specially with signs of concentration y , p y g Identification of the most relevant causes of strategic behavior Analysis of the influence of transmission constraints on the individual behavior of the market participants and on the exercise of strategic behavior Application field pp Competition authorities Regulators
    • Analysis 4 Strategic Behavior Market power p Maximization of benefits by means of exploitation of market dominance Static context Unilateral d i d U il t l and independent behavior d tb h i Own theory and well understood Comprehensively researched o pe e ey e e e Well defined indices to quantify market power potential Tacit collusion Maximization of benefits by means of tacit coordination of strategies Dynamic context Multilateral and interdependent behavior No own theory Not enough researched Hardly any successful prosecution of tacit collusion due to lack of analysis models Tacit collusion has not been comprehensively researched in electricity markets p y y yet Need of models to detect and assess tacit collusion in electricity markets
    • Analysis 5 Tacit Collusion (1) Necessary conditions y Market concentration • Easy to coordinate and reach a tacit agreement • T an mi ion con t aint inc ea e market concentration Transmission constraints increase ma ket concent ation Repeated interaction • Coordination of strategies by means of learning processes • Daily repetition intensify the learning process Barriers to entry and exit • „sunk costs“ nonreversible investments sunk costs • No contestable market Coordination capacity • Coordination on specific collusive equilibria Punishment of deviation from collusive agreements • Discouragement of deviations from collusive agreements Electricity markets fulfill the necessary conditions for a tacitly collusive agreement to emerge and remain stable over time
    • Analysis 6 Tacit Collusion (2) Facilitating factors Symmetrical firms • Easy to achieve collusive agreement among firms with similar production costs Homogeneous product • Product variety reduces competition and thus increases concentration and coordination Transparency • Increases coordination and detection of deviations from collusive equilibria Stable and predictable demand • Revisionary processes with decreasing prices • Low price elasticity of demand Fragmented demand-side • Small and frequent orders ll d f d • Less incentives to defect • Short time-lags encourage coordination among market participants Uniform-price auction • Difficult detection of collusion Present electricity markets fulfill necessary conditions and facilitating factors and are thus prone to tacit collusion
    • Analysis 7 Tacit Collusion (3) Learning Learning abilities of agents p process Collusion Dynamic of electricity markets • Repeated interaction • Short-time lags • Adaptable behavior Punishment Deviation Agents Bids Results Market Reward Market participants learn the market dynamic and adapt their behavior
    • Analysis 8 Tacit Collusion in Liberalized Electricity Markets England & Wales g Tacit collusion between the two biggest generation companies in the 1990‘s 90% of the time, the price was set by the two biggest generation companies California Californian energy crisis between 1998 and 2001 Economic withholding exercised 60% of the time ld d f Germany High level of market concentration Some research reports prices much higher than cost estimators as a consequence of tacit collusion European Transmission System Operators (ETSO) Advice about the importance of market monitoring in Europe in order to ensure adequate market conditions Tacit collusion has become a worldwide problem
    • Modeling 9 Description of the Model Classic oligopolistic models g p Identification of equilibria, i.e. Nash equilibria Quantity and price competition Static d i l St ti and single-period models i d d l For market power assessment suitable Repeated games with imperfect public information d ihi f bli i f i Dynamic coordination among market participants Imperfect public information: • Price and quantity Non-public information: • Cost structure and past actions Present actions depend on public and non-public information Strategy function: dynamic behavior of market participants Repeated games with imperfect public information are adequate to reproduce tacit collusion
    • Modeling 10 Simulation Model Hourly assessment of tacit collusion on the generarion-side Availability of generation units Transmission constraints Fuel prices Regulatory framework Thermal efficiencies Mean nodal demand Generation portfolios G i f li Generation Agent Market Agent Decision-making: Generation scenarios Demand scenarios Maximization f benefits M i i ti of b fit Offers Off Minimization of policy function generation costs iterative repetition Results Assessment of rewards: Market settlement reward function Updating of information action-value function Database Time limits Simulation horizon: 1 month – 1 year Periodicity: 1 hour
    • Modeling 11 Decision-making Decision making of Generation Agents Portfolios with thermal plants Different thermal generation technologies Fuel prices exogenous variables Startup costs Objective Function max [ Earnings from energy sales – Variable costs ] Assessment of rewards Short-time uncertainties Availability f A il bilit of generating units ti it 120 Stochastic fluctuations of demand Supply function [€/MWh] Decision of other generation agents g g Marginal cost curve Strategy 80 Price competition (Bertrand competition) 60 Percent increase of the supply f f l function 40 Price increase = 0 „price taker“ 20 0 0 100 200 [MW] 400 Generation capacity
    • Modeling 12 Strategy Actualization Game theory with artificial intelligence (Reinforcement-Learning) Efficient Effi i t appraisal of optimal strategies t maximize profits i l f ti l t t i to i i fit Consideration of the characteristics of social behavior: • Exploitation of past actions p p • Exploration of new actions • Recency Strategy actualization act ali ation Softmax algorithm f(S) π(o)=σ Probability Agent function Strategy Soptimal Strategies Information I f ti Policy function Action A ti π: P li function Policy f ti o: Vector of information Reward σ: Strategy mix Environment The policy function and strategy actualization allow to reproduce the actual behavior of generation agents
    • Modeling 13 Short-term Short term Uncertainties Availability of g y generating units g Two-state Markov model λ Failure Unit Unit operable μ Reparation failed So Stochastic determination of generation scenarios ee o o ge e o e o 45 [GW] Generation 43 capacity 42 41 40 0 1st w 2nd w 3rd w 4th w 40 Stochastic demand scenarios [GW] Demand Stochastic Gauss-Markov model 20 Statistical information from January 10 Working system July day d Saturday Sunday 0 1 7 13 19 1 7 13 19 1 7 [h] 19 hour
    • Modeling 14 Market Agent Spot market p Opening of market and reception of energy bids from generation agents Hourly bids y Demand scenarios stochastic Gauss-Markov model Gauss Markov Clearance of the market and calculation of the hourly price through minimization of generation costs considering generation and transmission constraints Lagrange Relaxation: min L = min [ ∑(Generation costs) + i i ∑(G i ) β [ ∑(Demand) + ∑(Losses) - ∑(Generation) ] + ∑ ŋ (Transmission constraints) + ∑ ε (Generation constraints) ] Price calculation Price = β [ node factor ] - ∑ ŋ [ PTDF ] Losses Transmission constraints
    • Results 15 Model System 6 thermal generation technologies 100 generation plants with usual capacities in actual systems Total installed capacity 44,4 GW Emissions certificate 12 €/EUA 3 market concentration levels 100 GA: unconcentrated 10 GA: moderately concentrated Generation marginal cost curve Generation 120 5 GA: highly concentrated costs [€/MWh] Generation Technology Mix Zusammensetzung des Kraftwerksparks 80 Lignite Hard coal 60 CCGT (gas/oil) 40 Steam turbine (gas/oil) 20 Nuclear Gas turbine (Gas/oil) ( / ) 0 0 10 20 30 [GW] 50 Aggregate generation capacity
    • Results 16 Simulated Hourly Prices (1) a) Constant available generation capacity and deterministic demand g p y January July 120 120 [€/MWh] [€/MWh] 80 80 60 60 Price Price 40 40 20 Working 20 Working day Saturday Sunday day Saturday Sunday 0 0 1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19 hour hour PCM 100 GA 10 GA 5 GA Simulated prices considering coordination abilities are higher than generation marginal costs The higher the market concentration is, the higher prices are
    • Results 17 Simulated Hourly Prices (2) b) Stochastic availability of the g y generating units and deterministic demand g January July 120 120 [€/MWh] [€/MWh] 80 80 60 60 Price Price 40 40 20 Working 20 Working day Saturday Sunday day Saturday Sunday 0 0 1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19 hour hour PCM 100 GA 10 GA 5 GA Simulated prices considering coordination abilities are higher than generation marginal costs g The higher the market concentration is, the higher prices are
    • Results 18 Simulated Hourly Prices (3) c) Stochastic availability of the g y generating units and demand fluctuations g January July 120 120 [€/MWh] [€/MWh] 80 80 60 60 Price Price 40 40 20 Working 20 Working Saturday Sunday day Saturday Sunday day 0 0 1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19 hour hour PCM 100 GA 10 GA 5 GA Price differences are reduced due to information uncertainties Information uncertainties restrain influence of market concentration
    • Results 19 Comparative Analysis of Results (1) Monthly revenues and producer surpluses January July 1800 1800 [Mio. €] [Mio. €] 1400 1400 1200 1200 1000 1000 800 800 600 600 400 400 200 200 0 0 a b c a b c a b c a b c a b c a b c a b c a b c PCM 100 GA 10 GA 5 GA PCM 100 GA 10 GA 5 GA a) Constant available generation capacity and deterministic demand Producer surpluses b) Stochastic availability of the generating units and deterministic demand h l bl f h dd d d Generation costs c) Stochastic availability of the generating units and demand fluctuations Market concentration and information uncertainties play a key role when tacit collusion occurs
    • Results 20 Comparative Analysis of Results (2) Assessment of collusion by means of the Lerner Index Lerner I d L Index=(Price-Marginal generation cost)/Price (P i M i l ti t)/P i January July 0,5 0,5 Lerner Lerner- Lerner Lerner- Index Index 0,4 0,4 0,3 0,3 0,2 0,2 0,1 0,1 0 0 PCM 100 GA 10 GA 5 GA PCM 100 GA 10 GA 5 GA Scenario a S i Scenario b S i Scenario c S i Tacit collusion even with low levels of concentration Information uncertainties reduce extraordinary surpluses
    • Conclusions 21 Conclusions Research aim: Development of a simulation model of electricity markets to reproduce and assess the strategic behavior of market participants Analysis Characteristics and consequences of strategic behavior in electricity markets Necessary conditions and facilitating factors of tacit collusion Electricity markets are prone to suffer tacit collusion Modeling Mixed Model: • Game theory: repetitive game with imperfect public information • Artificial intelligence: Reinforcement Learning Results: Market concentration and information uncertainties play a key role in cases of tacit collusion Tacit collusion even with low concetration levels Main contributions Comprenhensive analysis of tacit collusion in electricity markets and its dynamic Identification of main influencing factors and their assessment on the market The simulation model is suitable to reproduce short-term strategic behavior
    • Doctoral Examination Assessment of Short-term Strategic Behavior in Electricity Markets Ing. Pablo Frezzi San Juan, 25/04/2008