Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

As one of the most important renewable energy, hydropower is often asked to satisfy the load demand of power system at peak periods. Thus, the optimal operation of hydropower system is modelled to minimize the standard deviation of the residual load series obtained by subtracting the total power outputs of all the involved hydropower plants from the original load curve. Hence, this paper develops an improved grey wolf optimizer (IGWO) to effectively address the complex constrained optimization problem. In the proposed method, the quasi-oppositional learning is used to enhance the convergence rate of the swarm; the elite mutation operator is used to increase the probability of escaping from local optima; the elastic-ball strategy and heuristic constraint handling method are used to help infeasible individuals rebound to feasible space. Numerical experiments of 12 classical test functions demonstrate the feasibility of the IGWO method in the global optimization problems. Then, the developed method is applied to solve the optimal operation of two cascade hydropower systems. The results indicate that the proposed method outperforms several traditional methods in smoothing the peak loads of power system. To sum up, an effective solution tool is provided for the hydropower system operation optimization problem.