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

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

2. Index 2 • 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

3. Problem Analysis

4. Italian Electricity Market Unique Purchaser EMM Wholesale Market Retail Market

5. Wholesale Market Q Q Q Q Q Q Q K K € € € € € € € K K

6. State of the Art • Problem: finding the best prices • Microeconomic problem → Game Theory • Two approches in literature: • Multi-agent simulation [Praca et al., 2003] • Usually there is not any theoretical guarantee that adaptive/learning agents can converge to the optimal strategy • 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

7. Original Contributions

8. 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

9. Market Rules Macro Local Region Macro Local Region Macro Local Region

10. Winner Determination • Steps: • Bids collection • Bids ordering • Bids acceptance until the customers’ demand is satisfied

11. 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

12. Conclusions and Future Works

13. 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

14. 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