Loss Minimization for Bipolar DC Distribution Grid Considering Probabilistic EV Charging Load Using Load Balancing Method

Abstract

This paper proposes a novel method for power loss minimization and voltage unbalance mitigation in bipolar DC distribution grid (DCDG) considering probabilistic electric vehicle (EV) charging load. In the proposed method, a power flow analysis for the bipolar DCDG is performed by G-matrix and Gauss's iteration methods. The Monte Carlo Simulation (MCS) is used to evaluate the impact of EV charging load demand in probabilistic manner. To reduce the impact of the voltage unbalance problem and minimize power loss of the system, particle swarm optimization (PSO) is employed to search for an optimal load connection type that can minimize voltage unbalance factor (VUF) and total power loss. The proposed method was tested with 21-bus DC bipolar system with several cases to verify the potential of the method. The results shown that the proposed method can successfully minimize total power loss and reduce the VUF of the system with probabilistic EV charging load consideration. Therefore, the proposed methodology can be useful for enhancing DCDG operation and mitigating the impact of EV charging.