Predictive voltage control for a distribution network with renewable energy sources

This paper presents a predictive voltage control strategy for power distribution systems with renewable energy sources. A mixed-integer nonlinear programming problem was formulated and solved by genetic algorithm (GA). The on-load tap changer transformer and reactive power set point of wind and solar farms are determined. The day-ahead control horizon is considered and optimization is carried out at every hour. The wind and solar power are predicted by artificial neural network. The proposed methodology is implemented on a test distribution network to verify its effectiveness. It is demonstrated that the proposed method is capable of maintaining the system voltage close to the nominal as compared to the case of fixed control set-points.