Multi-Period Optimization of Energy Demand Control for Electric Vehicles in Unbalanced Electrical Power Systems Considering the Center Load Distance of Charging Station Areas

Abstract

The rise of plug-in electric vehicles (EVs) impacts the energy demand of power systems. This study employed a multi-period power flow analysis on the IEEE 123 node test system, which was optimized for the installation of 6-position EV charging stations. Temporal load shifting was utilized to control the charging intervals of electric vehicles. Non-dominated Sorting Genetic Algorithm (NSGA-II) was applied to determine the optimal locations for installing EV charging stations, considering target functions, such as total energy loss, voltage unbalance factor (VUF), and center load distance. The results showed that the center load distance resulted in the optimal charging station location in the central area of the system, different from conventional considerations. The results showed that installing the charging station in the center of the load group (case 4) increased the total energy loss and VUF compared to installing it at the root of the load group (case 3) by about 2.1134 and 1.2287%, respectively. However, EVs reduced impacts during periods of system weakness. By controlling charging intervals during off-peak times (case 6), total energy loss and VUF were decreased by 4.7070 and 5.6896%, respectively, which effectively reduced energy demand during peak periods.

Keywords

• center load distance
• energy demand control
• ev charging station
• multi-period power flow
• nsga-ii
• temporal load shifting
• unbalance power flow