Two-stage Voltage Optimization Control for Active Distribution Network Considering Source-Load Uncertainties

Aiming at achieving voltage optimization control of active distribution network ( ADN) with high proportion of distributed generators (DGs) and large-scale random loads of electric vehicles (EVs), a two-stage coordinated voltage optimization control method of ADN considering the uncertainties of sources and loads was proposed. In the day-ahead stage, an hour-level time-scale active-reactive power joint optimization model considering source-load prediction error and EV uncertainty was constructed. Taking capacitor banks and OLTC as main voltage control units, the day-ahead active power output plan and the intra-day switching plan of main voltage control units were formulated to minimize the voltage violation risk. In the intra-day stage, the voltage state of ADN is obtained through the state estimation, and the online voltage sensitivity is used as the basis of multi-resource collaborative control. Based on the model predictive control technique, a linearized voltage prediction model and a rolling optimization model were established to realize the voltage closed-loop control. The control goal is to minimize the voltage deviation of each node and make the controllable resources track the output arrangement made in the day-ahead stage. Taking the IEEE-33 distribution network as a simulation example, the effectiveness of the two-stage voltage optimization control method proposed in this paper was verified.