Title

Market power and efficiency in a computational electricity market with discriminatory double-auction pricing

Campus Units

Economics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2002

Journal or Book Title

IEEE Transactions on Evolutionary Computation

Volume

5

Issue

5

First Page or Article ID Number

504

Last Page

523

DOI

10.1109/4235.956714

Abstract

Abstract: This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

Comments

This is a working paper of an article from IEEE Transactions on Evolutionary Computation 5 (2002): 504, doi:10.1109/4235.956714