RISK-CONSTRAINED OPTIMAL BIDDING STRATEGY FOR A GENERATION COMPANY USING SELF-ORGANIZING HIERARCHICAL PARTICLE SWARM OPTIMIZATION

Abstract

This article proposes optimal bidding strategies for a generation company (GenCo) considering risk of profit variation by self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients (SPSO-TVAC). Based on a trade-off technique, the expected profit maximization and risk minimization are achieved. Nonconvex operating cost functions of thermal generation units and minimum up/down time constraints are cooperated to provide the optimal bid prices in a day-ahead uniform price spot market. The rivals’ bidding behavior is estimated by Monte Carlo simulation. Test results indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimization (IWAPSO) and genetic algorithm (GA) in searching the optimal bidding strategy solutions.