Phasor particle swarm optimization for solving problem of pricing in electricity market

Abstract

Minimization of fuel costs in thermal power plants by adjusting electric power outputs from generators represents important problem in power system operation and control. This problem affects also merit order and pricing in the electricity market. In this article, the Phasor Particle Swarm Optimization (PPSO), which represents a meta-heuristic self-adaptive and non-parametric algorithm, is proposed for solving the problems of generation cost minimization and pricing in the electricity market. Performance of PPSO for solving the cost minimization problem is evaluated using the standard IEEE 30-bus test system with 6 generating units. Based on the results obtained, the PPSO outperforms all other meta-heuristic algorithms that have been applied in published literature to solving this problem. In addition, the PPSO, cost minimization model and equilibrium supply chain model are used in the analysis of a case study based on a real electricity market, and in particular retail, spot and offer prices. Moreover, the results have shown that the proposed optimization approach can be used for merit order correction and for reducing electricity prices, increasing the amount of electricity sold (purchased), and improving the efficiency of a spot electricity market.