Probabilistic Modeling of Plug-in Electric Vehicles Charging Demand and Charging Cost Minimization

One of the main industries contributing to global greenhouse gas (GHG) emissions is transportation industries. Plug-in electric vehicles (PEVs) are an alternative to reduce greenhouse gas emissions. It is crucial to understand how PEV charging affects the distribution grid because as battery technology advances, more and more PEVs are being incorporated into the grid. The study proposes a probabilistic approach to estimate PEV charging demand taking into mobility problems at a public charging station. With regard to the time-of-use (TOU) price from the utility, the intelligent charging scheme taken into consideration here attempts to reduce the cost of energy while satisfying the charge requirements of all PEV customers. Ant Lion Optimization (ALO) and Grey Wolf Optimization (GWO), two metaheuristic optimization techniques, are used to reduce the cost of charging PEVs while maintaining their state-of-charge within a predetermined range.