Optimal power flow and prices in the electricity market using the hybrid PPSOGSA algorithm

Abstract

In this paper, the PPSOGSA algorithm is proposed to optimize the nodal prices and power flows transacted between the tiers of the supply chain (SC) in a deregulated electricity market. The hybrid PPSOGSA algorithm is a combination of phasor particle swarm optimization (PPSO) and gravitational search algorithm (GSA). The equilibrium model of SC was applied. The objective function is the function of total profit of participants in the SC. The applied cost functions of participants are nonlinear and non-separable. The results of PPSOGSA application are compared with the results of the modified projection method (MPM) for the numerical solution of the variational inequality of SC, which is applied in the literature for solving the same problem, and with the results of genetic algorithm (GA) as one of the basic meta-heuristic algorithms. The results showed that PPSOGSA gives the best results compared to MPM and GA. Moreover, in the case of PPSOGSA application the equilibrium conditions are fully satisfied while in the case of MPM the equilibrium conditions are satisfied with a small error. It is found that PPSOGSA converges in the much lesser number of iterations and gives better results than the MPM. This new application of PPSOGSA enables the handling of decision makers, who operate in the electricity market and optimize energy flow and prices, minimize payment cost and maximize profit.