Optimal scheduling of wind-solar hybrid energy storage microgrids using a multi-objective improved genetic algorithm

For the microgrid energy management and optimal scheduling problem, system operating cost and reliability cost are defined as the objective functions. To address the limitations of conventional genetic algorithms—such as insufficient solution accuracy, slow convergence, and a tendency to fall into local optima—an improved genetic algorithm (DC-NSGA-II) is proposed, in which an adaptive crowding-distance threshold is introduced to ensure a more exhaustive exploration of the search space. Considering that electrochemical energy storage, although characterized by high energy density, suffers from limited discharge duration and is therefore unsuitable for long-term stable system operation, a hybrid energy storage system integrating hydrogen storage and electrochemical storage is constructed to leverage the complementary advantages of different storage technologies. A hydrogen production system model comprising wind turbines, photovoltaic units, energy storage devices, and an alkaline electrolyzer is analyzed and developed. The improved genetic algorithm is employed to solve the optimal scheduling model, and comparative analyses are conducted against alternative optimization approaches. Simulation results demonstrate that applying the DC-NSGA-II algorithm to the wind-photovoltaic-hybrid energy storage optimization dispatch model outperforms conventional methods across three distinct seasonal scenarios. In both single-day and long-term dispatch scenarios, the proposed approach significantly reduces system operating costs and reliability costs, thereby verifying its feasibility and economic effectiveness.