Enhanced Energy Cost Minimization for Plug-In Electric Vehicles with Grid Stability Constraints and Uncertain Charging Demands
This paper proposes a coordinated energy management framework for plug-in electric vehicle (EV) charging and discharging that minimizes operational cost while preserving grid stability under uncertain user behavior. The uncertainty of charging demand is represented through stochastic initial state-of-charge (SOC) levels, which capture the variability of EV energy requirements upon arrival. Based on this uncertainty representation, an optimization model incorporating battery dynamics, time-of-use (TOU) pricing, vehicle-to-grid capability, mobility constraints, and peak demand limits is formulated. To solve the resulting nonlinear optimization problem, the Grey Wolf Optimization (GWO) algorithm is employed and benchmarked against Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and Particle Swarm Optimization (PSO) Algorithm. Simulation studies conducted on a fleet of EVs over a 24-hour scheduling horizon demonstrate that the proposed framework maintains SOC within the safe operating range while ensuring that all vehicles satisfy the departure SOC target.