Cost-Effective Electric Energy Bidding Strategies Applying with Particles Swarm Optimization and Adaptive Particle Swarm Optimization

Abstract

Electric energy market is to increase their profit for electricity providers (generators) in an open competitive electricity market and reduce reduced consumer costs by taking into account available power supply, power demand, market clearing prices (MCP) and constraints. The main contribution of this paper is provided more benefit for supplier and this new technique has used overcome the problem of electricity bidding. It may be highly important to manage the market of electricity as per the fair rules. In this paper, PSO and APSO are used to solve the bidding problem. PSO and APSO have many characteristics that similar to Genetic Algorithm (GA) evolutionary computational strategies. Firstly, by integrating the random solution and updating the generation, we get optimal results in the problem space. The possible solutions known as particles are flowing in every direction through the problem area in the PSO, following the optimal current (particle) solution. APSO is recommended for enhancing PSO efficiency (i.e. a different weight modification technique in APSO, weight varies according to the particle size). In the proposed method we use one numerical with six generators (GENCOs) and two large consumers (DISCOMs) are taken into consideration in which total profit is better than in APSO as compared to PSO.