Multi-Objective Grey Wolf Algorithm for Dynamic Economic Environmental Dispatch Considering Electric Vehicles

Abstract

Aiming at the multi-objective optimization problem of dynamic economic emission dispatch (DEED) considering plug-in electric vehicles (PEVs), this paper proposes an improved multi-objective grey wolf optimizer (IMGWO) to simultaneously minimize power generation costs and pollutant emissions. Traditional economic dispatch requires a trade-off between economic benefits and environmental sustainability. However, these two objectives are inherently conflicting, rendering single-objective optimization strategies ineffective for practical DEED scenarios. In this study, PEVs are integrated into the DEED framework to implement systematic charging and discharging scheduling, which realizes peak shaving and valley filling and further improves the operational quality of the power grid. The incorporation of PEVs also renders the DEED problem more nonlinear, non-convex, and non-smooth. To address these complex characteristics, the proposed IMGWO adopts modified logistic chaotic mapping for population initialization, combined with a nonlinear convergence factor and dynamic weight strategy. These improvements effectively enhance the global search ability and convergence accuracy while preventing premature convergence. Furthermore, the Pareto optimal solution set is utilized to resolve conflicts among multiple optimization objectives. Comparative experiments on standard benchmark test cases verify that the proposed IMGWO outperforms four existing advanced algorithms in comprehensive evaluation indicators, demonstrating its excellent effectiveness and robustness. Finally, the proposed algorithm is applied to three classic PEV-integrated DEED cases. The results confirm that the IMGWO can achieve superior scheduling performance and verify the positive effect of PEV participation in improving grid dispatching quality and unit operation efficiency.