Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm

Abstract Electric Vehicles (EVs) are seen to have some negative impacts on microgrid performance, such as diminishing power quality and efficiency and increasing power losses, voltage variations and even customer energy prices. This paper proposes a new method for evaluating the effect of integrating a large number of EVs on a power system and their impact on the network voltage profile via injecting reactive power into highly-loaded buses. A multi-objective optimization problem is developed to obtain the optimal siting and sizing of charging stations and renewable energy sources (RES). The optimization problem focuses on reducing power losses, improving voltage stability of the system and reducing charging costs of EVs. In order to increase the network load factor some coefficients are introduced. Such coefficients, which depend on wind speed, solar irradiance and hourly peak demand ratio in the load characteristic of day-ahead, help aggregators to charge their EVs in off-peak hours. Differential Evolution (DE) algorithm is used for solving the optimization problem. The performance of the proposed method is evaluated for 69-bus and 94-bus microgrids.