Multi-strategy gravitational search algorithm for constrained global optimization in coordinative operation of multiple hydropower reservoirs and solar photovoltaic power plants

Abstract

Recently, the solar photovoltaic power, a promising renewable energy, is witnessing a rapid development period. However, it is often difficult to perfectly capture the generation of solar photovoltaic plants because of various factors (like weather condition, solar radiation and human activities), increasing the operational risk and cost of power system. Hybrid energy system proves to be an effective measure to address this problem. Motivated by this practical necessity, this paper develops a novel hybrid gravitational search algorithm to solve the coordinative operation model of multiple hydropower reservoirs and solar photovoltaic power plants. In the proposed method, the gravitational search algorithm is set as the unified framework; the neighborhood search strategy is used to improve the convergence rate by considering the social information and individual experience; the adaptive mutation strategy is used to improve the population diversity by elite conservation and mutation operator; the modified elastic-ball strategy and constraint handling technique are used to enhance the solution feasibility. The simulation results of numerical functions demonstrate the superiority of the developed method in convergence rate and global search ability. The hydro–solar operation results in different cases show that compared with the traditional methods, the proposed method can yield high-quality scheduling schemes to alleviate the peak shaving pressure of power system. Thus, the novelty of this paper is to provide an effective HGSA method for solving the complex engineering optimization problem.