A Robust Multiobjective Optimization Strategy for Power Management in a PV-Integrated G2V/V2G System

This article presents a cost-based robust charging scheme for plug-in electric vehicle (PEV) integrated with the beneficial collaboration of photovoltaic (PV) system. The uncertainty of load/PV power and electricity price is incorporated into the proposed model. To handle the altering feature of the uncertainty resources and achieve a robust charging strategy, the information gap decision theory (IGDT) is utilized. Since the formulated IGDT-based charging model is inherently a multiobjective optimization problem, the nondominated sorting genetic algorithm type 2 (NSGA-II) is used to create the Pareto optimal solutions. To attain the best compromise solution, a fuzzy-oriented selection method is introduced. Moreover, to elucidate more effective constraints for the robust territories (RTs) associated with the uncertainty resources of the proposed model, an evaluation based on the deterministic and nondeterministic operation costs is discussed. To validate the proposed robust charging strategy, a power hardware-in-the-loop experimental setup is developed using OPAL-RT 5700. Furthermore, the effectiveness of the NSGA-II in terms of both robustness and convergence efficiency is validated through comparison with single-objective particle swarm optimization, multiobjective particle swarm optimization and GUROBI solver.