Abstract：
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.
摘要：
随着光伏（PV）和电动汽车（EV）充电和更换电站连接到配电网的高渗透率，配电网线损增加、电压偏差等问题日益突出。传统无功补偿装置的应用和变压器分接头的改变一直难以满足配电网无功优化的需要。迫切需要提出新的无功调节方法，这些方法对电网的安全运行和成本控制具有至关重要的影响。因此，提出了利用光伏和电动汽车的无功调节潜力来降低配电网无功优化压力的思想。本文建立了光伏和电动汽车的无功调节模型,并提出了自己的无功可调容量动态评估方法。当优化目标仅设置为线路损耗和电压偏差时，上述模型通过五种不同的算法进行优化，并通过深度学习进行近似。仿真结果表明，深度学习的预测具有惊人的能力，可以拟合智能算法在实际应用中获得的Pareto前沿。
