hybrid meta-heuristic algorithms

This study investigated the effect of distributed generation resources and demand-response program on the placement of charging/discharging stations and optimal exploitation programming of electric vehicles in a distribution network. Effective factors in the sitting of stations and optimal charge/discharge power in stations are a combination of technical and economic parameters. Minimization of network losses, minimization of voltage loss in feeders, smoothing network load curve, and THD reduction were assumed as technical parameters. As to the economic scope, the placement of stations and charge/discharge power were considered the most effective parameters. In other words, the costs of charging/discharging operations needed to be minimized in the stations to reach the lowest costs spent on purchasing power. A price-based demand-response program was incorporated into the simulations to manage loads on the customer side and smooth the load curve. We implemented genetic, particle swarm optimization, and imperialist competitive hybrid meta-heuristic algorithms to find the optimum operating point. We performed simulations in an IEEE standard 69-bus network. The problem was solved using the former hybrid algorithm, and optimal sites of stations and exploitation program of charge/discharge were specified. This study evaluated the effects of renewable energy resources and price-based demand-response program on the optimal placement of stations and optimal exploitation program of stations. Furthermore, it addressed the effects of an increase in the number of stations and a rise in charge/discharge capacity.