基于深度强化学习的新能源配电网双时间尺度无功电压优化

A large amount of renewable energy is connected to the distribution network, and its volatility and intermittency are easy to cause frequent voltage fluctuation in distribution network. Traditional model-based reactive power and voltage optimization methods are highly dependent on the accurate modeling of the power grid, and their solution accuracy and calculation speed are difficult to meet the requirements for voltage control of distribution network with renewable energy. In this paper, a dual-time scale distribution network reactive power and voltage optimization method is proposed based on deep reinforcement learning. This method transforms the reactive voltage optimization problem of the power system into a Markov decision process, considers the differential regulation characteristics of reactive power compensation equipment and the characteristics of different deep reinforcement learning algorithms, and designs a dual-time scale optimization scheme by coordinated controlling for discrete equipment and continuous equipment. Among them, the switching plan of the shunt capacitor bank is formulated on the long-term scale to adjust the voltage deviation and minimize the network loss of the whole system; on the short-time scale, a rolling prediction window is set, and the SVG output plan is formulated to track the voltage change and solve the problem of frequent voltage fluctuation of distribution network due to the connection of renewable energy. Finally, the advantages of the data-driven scheme in the realization speed and effect of reactive power and voltage optimization are verified by an IEEE-33 node extension system.