An Optimal Capacity Allocation Method for Integrated Photovoltaic-Storage-Charging System Based on Multi-Objective Artificial Hummingbird Algorithm

The capacity configuration of integrated photovoltaic (PV), energy storage, and charging systems requires balancing economic efficiency, reliability, and environmental benefits. However, dynamic load demand, intermittent PV output, and multi-source uncertainties pose challenges to precise configuration using conventional methods. To address this, a Multi-Objective Artificial Hummingbird Algorithm (MOAHA)-based optimal capacity allocation method for integrated PV-storage-charging systems (PSCSs) is proposed. A comprehensive structure for the integrated system is developed, and PV array and inverter models are implemented in OpenDSS to achieve rapid maximum power point tracking. A capacity degradation model for the energy storage battery is established, accounting for factors such as the number of charge-discharge cycles and depth of discharge. Furthermore, an electric vehicle model is incorporated, considering initial charging time, daily mileage, and energy consumption per unit distance. The objective function and constraints for optimal capacity allocation are formulated, and the artificial hummingbird algorithm is employed to solve the multi-objective optimization problem, thereby enabling optimal capacity allocation. Experimental results demonstrate that the proposed method achieves a configuration ratio of only 22.5%, the lowest comprehensive cost, reduced operating frequency and amplitude of energy storage, and a significant reduction in the deviation between the PSCS and actual demand.