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A Study of Central Auction Based
Wholesale Electricity Markets
S. Ceppi and N. Gatti
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Index
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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
Wholesale Market
S. Ceppi and N. Gatti
Retail Market
Wholesale Market
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S. Ceppi and N. Gatti
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State of the Art
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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
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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
Macro Local Region
Macro Local Region
S. Ceppi and N. Gatti
Macro Local Region
Winner Determination
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Steps:
• Bids collection
• Bids ordering
• Bids acceptance until the customers’ demand is satisfied
S. Ceppi and N. Gatti
Finding Equilibrium Strategies
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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
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Context: wholesale electricity market based on a central auction
Original Contributions:
• Enrichment of the model presented in literature with a realworld 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
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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
S. Ceppi and N. Gatti