Risk-constrained bidding strategy for Generation Companies in an open electricity market using Fuzzy Adaptive Particle Swarm Optimization

Abstract

This paper presents a novel methodology based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) for the preparation of optimal bidding strategies by power suppliers in a competitive electricity market. The gaming by participants in a competitive electricity market causes electricity market more an oligopoly than a competitive market. In general, Competition implies the opportunities for Generation Companies (Gencos) to get more profit and, in the mean time, the risk of not being dispatched. In this paper each participant can increase their own profit by optimally selecting the bidding parameters using FAPSO, where inertia weight is dynamically adjusted using fuzzy evolution. The proposed method is numerically verified through computer simulations on IEEE 30-bus system consist of six suppliers and two large consumers are participated in the bidding process. The results are compared with Genetic Algorithm (GA) and different versions of PSO. The Test results indicate that the proposed algorithm maximize profit, converge much faster and more reliable than GA and different versions of PSO.