A Bio-Inspired Optimization Approach for Low-Carbon Dispatch in EV-Integrated Virtual Power Plants

Abstract

With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs.