A novel chance constrained joint optimization method under uncertainties in distribution networks

Abstract

The joint optimization in distribution networks considering the uncertainties in wind power or photovoltaic (PV) outputs is a larger scale stochastic mixed integer nonlinear programming (MINLP) problem. However, how to efficient and accurate solve the problem under uncertainties is still a challenge. To handle the uncertainties, a scenario based chance constrained programming model is established. To improve the accuracy, the network reconfiguration and capacitor control are simultaneously performed by optimizing serious voltage and shunt current sources in the model by using equivalent network transformation. To improve calculation efficiency in dealing with chance constraints, a cluster of scenarios satisfying the confidence level is formed for optimization. To avoid introducing plenty of binary variables for the radial constraint and improving calculation efficiency, a heuristic method opening loops one by one is developed. The numerical simulations on distribution networks show the efficiency and accuracy of the proposed algorithm over the existing methods.