Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response

The increased number of Electric Vehicles (EVs) in smart grids has highlighted the need for fast charging stations to provide services in the minimum time duration. This duration for charging at the station can be regarded as a challenge due to increasing the grid load. To mitigate the effects of these disadvantages, Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) can be employed. Thus, the present paper aims to design a fast-charging station while considering parameters such as the solar panel capacity, storage systems, wind turbine, Demand Response (DR) program, and stochastic model of RESs. Accordingly, two models of wind power plant ownership are proposed for the station and the grid. The correct estimation of the wind-generated power can reduce the uncertainties in programming; thus, a forecasting method based on the fuzzy-neural network and improved Particle Swarm Optimization (PSO) algorithm with time-varying coefficients is proposed. In the first ownership, based on the forecast wind-generated power, the station signs a contract with the grid, and by using EV, RES, and ESS load management, try to reduce the imbalance and costs. In the second ownership, the wind power plant is at the service of the grid and the station owner makes revenues by servicing the grid. The objective function of the problem is based on the current net value over a 10-year time horizon, including the costs of performance and maintenance. The findings revealed that when the charging station uses load management, it increases profitability and reduces the initial capital investment in an acceptable manner. In the first and second ownerships, the total 10-year cost in the presence of Demand Response (DR) is reduced by 17.85% and 3.31, respectively. Based on the findings, the initial capital cost for supplying internal loads and providing flexible services to the grid is slightly higher in the second than the first ownership. The simulation results also indicate that the proposed hybrid algorithm forecasts wind speed changes with proper precision.