Locating Electric Vehicle Solar Charging and Discharging Stations Using Multi-Objective Genetic Algorithm and Fuzzy Decision Making

The mass production of combustion engines has caused numerous problems such as air pollution, the limitation of fossil fuel reserves and inconsistent prices. Therefore, much attention has been paid for research and design of electric vehicles (EVs) in developed countries, among which charging and discharging stations are of great significance in order to the promote EVs. This paper provides insights into the optimization of locating EVs charging and discharging stations equipped with solar panels. To this end, we determine the solar power generation capacity as well as the optimum charging/discharging time for EVs using the combination of multi-objective non-dominated genetic algorithm (NSGA-II) and fuzzy decision maker. In order to reasonably simulate the actual system conditions, increasing the applicability of results, we include the uncertainty of network consuming load, along with uncertainty of power generation using solar panels as well as the uncertainty of those EVs that return to the station considerably before the full discharge. These uncertainties are taken into account using the non-sequential Monte Carlo probabilistic method.