Research on Capacity Optimization Configuration of Light Storage Charging DC Microgrid

Due to the high investment cost and insufficient system stability of the photovoltaic charging DC microgrid, a capacity optimization model is constructed with the objective functions of annual net cost, which represents economic benefits, and power deviation rate, which represents system stability. Aiming at the problem of insufficient search ability and low optimization accuracy in solving optimization configuration models with non-zero solutions using traditional whale optimization algorithms, a multi strategy improved whale optimization algorithm is proposed, which integrates quasi adversarial learning mechanism, adaptive weights, nonlinear convergence factor, Gaussian difference mutation, and greedy selection. Finally, a case study was conducted on a residential area in Wuhan, and the results showed the feasibility of the model and the effectiveness of the improved algorithm.