A Model Predictive Control Method to Optimize Voltages for Active Distribution Networks with Electric Vehicles

The power output of distributed generators and loads in distribution networks has strong volatility and randomness, which makes the voltage overruns and abnormal problems more serious. In this paper, a real-time active distribution network voltage optimization strategy using model predictive control (MPC) based on the electric vehicles (EV) has been proposed, which uses only electrical information of grid connection point of EV. Firstly, a predictive model of network voltage is established based on the reference values given by the long-term optimization. Then a rolling optimization model with an EV is established, in which the objective function is to minimize the voltage deviation and power losses. With the rolling-horizon approach, the power exchange between both ends of EV is optimized and the predictive model is corrected through the voltage deviation feedback. This method effectively solves the problem that existing voltage optimization methods rely on global information are difficult to cope with rapid power fluctuations. Case studies on the IEEE 33-node system are carried out to verify the effectiveness of the proposed method. Results show that the voltage of the distribution network is enhanced and the power losses are significantly reduced, which provides a technical reference for operating new distribution networks.