Cooperative constrained multi-objective dual-population evolutionary algorithm for optimal dispatching of wind-power integrated power system

Due to increasing uncertainty and multiple operational requirements of current power system, it brings great challenge to power dispatch of wind-power integrated power system. To address above problems, a cooperative constrained multi-objective dual-population evolutionary algorithm (CCMDEA) is proposed with uncertainty budget of wind power, which divides the intermittent power into different intervals with flexible uncertain parameters. On the basis of non-dominated sorting genetic algorithm III (NSGA-III), constraint violation (CV) is also involved to guide the population evolution combined with two defined populations: convergence population (CP) and diversity population (DP) to coordinate convergence and diversity performance of pareto-fronts, and a coordinated selection mechanism is designed to select suitable parents according to the state of evolutionary population, which can coordinate the convergence population and diversity population well. According to simulation results on two test systems, the efficiency and priority of the proposed algorithm is well verified, which also reveals that the proposed algorithm can be a viable way for solving power dispatching of wind-power integrated power system.