Regulation Methods for Rural New Energy Consumption Based on Photovoltaic-Storage-Charging Microgrid

With the continuous expansion of photovoltaic (PV) power generation scale and the rapid growth of electric vehicle (EV) ownership in rural areas, rural microgrids are facing challenges of three-phase unbalance, severe overload, and new energy consumption difficulties. To effectively enhance the new energy consumption capacity in rural areas, this paper proposes a regulation method for new energy consumption in rural microgrids. The BP neural network and LSTM model are used to accurately predict the PV power generation and load for the next 24 hours, respectively, providing data support for subsequent regulation. A comprehensive objective function is constructed, incorporating the minimization of three-phase unbalance degree, the reduction of forward/backward severe overload risks, and the maximization of PV consumption. The multi-objective particle swarm optimization (MOPSO) algorithm is introduced to optimize and solve the comprehensive objective function, obtaining optimal regulation index data to achieve precise control of devices such as charging piles and energy storage systems. Simulation results show that the proposed method can significantly reduce the three-phase unbalance degree of the distribution network, decrease the probability of severe overload, and improve the PV consumption rate, effectively ensuring the stable operation of rural microgrids and the efficient consumption of new energy.