Economic environmental dispatch using an enhanced multi-objective cultural algorithm

Abstract

Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the EED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of EED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically.