Optimal planning of multi-type renewable energy resources and electric vehicle charging stations under resilient charging tariff using Gorilla Troops optimizer

Currently, there is highly concentrated attention given to microgrid energy management, particularly due to the significant increase in the adoption of renewable energy resources (RERs) such as photovoltaic units and wind turbines, as well as the growing reliance on electric vehicles (EVs) to reduce environmental pollution. Considering uncertainties, which are associated with solar irradiance and wind speed, can increase the ability to achieve proper energy management. In addition to that, considering the smart grid's role in the electricity market as a price maker and the unpredictable behavior of EVs, these considerations aim to minimize costs and maximize profits for microgrids. The main contribution of this paper is studying the impact of increasing the charging tariff of EVs in the presence of different types of RERs on charging behavior, maximizing the profit of the microgrid, and reducing the total energy purchased from the wholesale market. For this purpose, a metaheuristic-based optimization problem called Gorilla Troops optimizer (GTO) is employed to optimally allocate the RERs and the EV charging stations. The results demonstrate that increasing the charging tariff without RERs or with only one source of RERs results in the charging of EVs following an uncontrollable strategy. Hence, the cost of purchasing energy from the utility increases. On the other hand, compared to the base case, integrating EV charging stations with multi- type RERs resulted in the profit increasing by 61.92 % and the total energy purchased from the wholesale market decreasing by 24.98 %. Moreover, the waiting time at the charging station will be reduced shortly; the annual installment of multi- type RERs decreased by 31.25 % compared with considering only one source of RERs, and the charging strategy of EVs followed the controllable charging strategy.