Evolving cooperative bidding strategies in a power market

Abstract

This paper presents an evolutionary algorithm to generate cooperative strategies for individual buyers in a competitive power market. The paper explores how buyers can lower their costs by using an evolutionary algorithm that evolves their group sizes and memberships. The evolutionary process uncovers interesting agent behaviors and strategies for collaboration. The developed agent-based model uses PowerWorld simulator to incorporate the traditional physical system characteristics and constraints while evaluating individual agent's behavior, actions and reactions on market dynamics. Simulation results on IEEE 14-bus system show that the evolutionary approach evolves mutually beneficial strategies that enhance the buyer's profitability. The buyers learn to achieve substantial cost savings by forming groups and adjusting their demand curves, without sacrificing much in desired power consumption.