Genetic algorithm evolution of utility bidding strategies for the competitive marketplace

• Published in: IEEE transactions on power systems Vol. 13; no. 1; pp. 256 - 261
• Main Authors: Richter, C.W., Sheble, G.B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.1998; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Distributed computing; Economic data; Economic forecasting; Electric energy; Electricity supply industry deregulation; Energy; Energy economics; Exact sciences and technology; General, economic and professional studies; Genetic algorithms; Methodology. Modelling; Power engineering computing; Power generation; Power industry; Power system simulation; Senior members; Student members; Trade; Deregulation; Optimal strategy; Bidding; Genetic algorithm; Energy distribution; Economic optimization; Electric energy; Electric power production; Electric utility
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/59.651644

This paper describes an environment in which distribution companies (discos) and generation companies (gencos), buy and sell power via double auctions implemented in a regional commodity exchange. The electric utilities' profits depend on the implementation of a successful bidding strategy. In this research, a genetic algorithm evolves bidding strategies as gencos and discos trade power. A framework in which bidding strategies may be tested and modified is presented. This simulated electric commodity exchange can be used offline to predict whether bid strategies will be profitable and successful. It can also be used to experimentally verify how bidding behavior affects the competitive electric marketplace.