A dual time scale Voltage/Var control method for active distribution networks based on data-driven

In order to smooth the impact of distributed photovoltaic and load fluctuations on the distribution network, this paper proposes a dual time scale distribution network reactive voltage control method based on data-driven and source-load uncertainty. The proposed method can be divided into two stages, the first stage is centralized optimization, which performs long time scale scheduling of slow regulation devices in active distribution networks, and converts the original centralized optimization problem into a second-order cone mixed-integer planning problem after second-order cone and linearization theory. The second stage is based on data-driven distributed control, which is based on the information communication between the regional controllers to achieve short time scale optimization control of photovoltaic inverters, where the regional controllers are modeled based on deep neural networks. In order to make the model training more effective, the paper uses an improved generative adversarial network to generate a large number of source-load samples conforming to the real distribution for the training of the regional controllers. Through the analysis of the improved IEEE33 model, the optimal control of reactive voltage with dual time scales is achieved, which keeps the voltage within the safety range while significantly reducing the network loss, and the computation time of the regional controller based on the data-driven model is reduced by 96.52% compared to the centralized control, which makes the frequent control possible in the face of more complex power systems.