Multi-Objective Optimization of User-Side Photovoltaic -Energy Storage Systems Operation Strategy

The stochastic characteristics of photovoltaic (PV) power generation impact the stability of power systems. Hence, PV power crucial for effective scheduling within PV-energy storage systems (ESS). The prediction model employs the improved artificial hummingbird algorithm-back propagation neural network (IAHA-BPNN). Based on the prediction results, a multi-objective function is constructed to optimize the strategy with the objectives of minimizing the cost of tariffs, minimizing the peak-to-valley deviation of the grid power and squaring the deviation of the grid power. The findings show that the model presents exemplary performance, and the operational strategy successfully mitigates demand. The reduction in electricity costs for users not only alleviates the burden of electricity bills but also facilitates enhanced energy management, thereby improving the efficiency and economic advantages of the PV-ESS.