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

2 Index 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 K K K K Q € Q € Q € Q € Q € Q € Q €

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