A Study of Central Auction Based Wholesale Electricity MarketsS. Ceppi and N. Gatti

S. Ceppi and N. Gatti

2Index

• Problem Analysis• Context: electricity market• Problem: finding the market equilibria• State of the Art

• Original Contributions• Introduction of the auction mechanism• Finding the auction optimal solution• Finding the equilibria in the market

• Conclusions and Future Works

S. Ceppi and N. Gatti

Problem Analysis

S. Ceppi and N. Gatti

Italian Electricity Market

S. Ceppi and N. Gatti

Wholesale Market

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S. Ceppi and N. Gatti

State of the Art

• Problem: finding the best prices• Microeconomic problem → Game Theory

• Two approches in literature:1. Multi-agent simulation [Praca et al., 2003]

• Usually there is not any theoretical guarantee that adaptive/learning agents can converge to the optimal strategy

2. Game theoretical: POOLCO model [Hobbs, 2001]• It is based on the Cournot oligopoly: generators choose

only the amount of electricity to sell, while the price is a function of electricity demand and supply

• It admits a unique equilibrium• In the equilibrium all the generators bid the same price• No real-world auction mechanism is considered

S. Ceppi and N. Gatti

Original Contributions

S. Ceppi and N. Gatti

Real-World Auction Mechanism

• Generator:• Action: bid a price per electicity unit for each local region in

which it produces• Goal: maximize its utility

• Electricity Market Manager (EMM):• Actions:

• Choose the generators from which to buy electricity • Choose the amount of electricity to buy from each generator

• Goal: minimize the clearing price• Constraints:

• Satisfy the customers’ demand of electricity• Generators capacities• Network capacity• Market rules

S. Ceppi and N. Gatti

Market Rules

S. Ceppi and N. Gatti

Winner Determination

• Steps:• Bids collection• Bids ordering• Bids acceptance until the customers’ demand is satisfied

S. Ceppi and N. Gatti

Finding Equilibrium Strategies

• Solution Concept: Nash equilibrium in pure strategies• Reduction of the model

• Infinite possible actions → finite possible actions• Search algorithm based on Best Response Dynamics• Use of Tabu List to ensure that the algorithm ends• Implementation

• Static• Dynamic

S. Ceppi and N. Gatti

Conclusions and Future Works

S. Ceppi and N. Gatti

Conclusions

• Context: wholesale electricity market based on a central auction• Original Contributions:

• Enrichment of the model presented in literature with a real-world auction mechanism

• Greedy Algorithm to find the best solution of the Winner Determination Problem for the auction

• Computation of the equilibrium strategy using a solving algorithm based on best response search

• The introduction of the auction mechanism leads to an equilibrium which is different from that obtained in its absence:• There exist multiple equilibria• Generators, in general, bid different prices at the

equilibrium

S. Ceppi and N. Gatti

Future Works

• Efficiency improvement of the algorithms for:• Winner Determination Problem• Equilibrium Computation

• Equilibrium characterization by Evolutionary Game Theory for the selection of one equilibrium when multiple equilibria exist

• Study of the auction mechanism in the presence of uncertain information

• Bayesian Games perspective• Mechanism Design perspective