Stochastic Day-ahead Optimal Scheduling of Active Distribution Networks with Renewable Energy Sources and Electric Vehicles

Abstract

The integration of renewable energy sources and electric vehicles bring challenges to active distribution networks (ADNs) due to their stochastic and intermittent behaviors. To capture the uncertainties, a stochastic day-ahead optimal scheduling model of an ADN is proposed to minimize power losses and voltage deviations in this paper. First, a deterministic day-ahead optimal scheduling model of an ADN is formulated given a series of operational constraints. Second, the deterministic model is generalized into a stochastic model considering uncertainties in the renewable generation and power loads. Several statistical models are developed to depict charging curves of battery electric vehicles with and without the demand response, so as to model an electric vehicle charging stations. Further, a Gaussian distribution model and K-means are adopted to estimate prediction errors of renewable energy sources and electric vehicle charging stations by constructing classical stochastic scenarios. Finally, discussion and analysis are performed on a modified IEEE 33-bus distribution network. The simulation results show that the proposed stochastic model can provide optimal day-ahead scheduling strategies and probability distributions of voltages and power losses to account for the uncertainties.