With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

The rise of new energy and the wide application of electric vehicles (EVs) have led to the substantial expansion of distribution network in recent years. The problems such as the decline of transmission reliability and the rise of power loss in distribution network are becoming increasingly serious. Therefore, distribution network can greatly improve its reliability and quality of power supply voltage through changing topology. This work built a distribution network reconfiguration (DNR) model with new energy and EVs firstly. Then, the position of bus tie switches and the reactive power regulation range of new energy and EVs were proposed as decision variables. A multi-objective evolutionary algorithm would be applied to this DNR model and the optimization results will be obtained when considering the line loss and voltage deviation as the objective function. In order to get different optimal compromise solutions with the changes of actual environment, this work employed a new decision-making method named Prevalence Effect Method (PEM). Finally, a high-quality strategy of DNR and reactive power regulation will be obtained.