A novel hybrid adaptive strategy for real-time dispatch and scheduling in renewable-integrated EV charging stations
Hao Guo, QIU Peng-Fei, Zhang Jiahui, Bai Xiang, Yan Laiqing, Zia Ullah
Abstract
Paper presents a novel Hybrid Adaptive Dispatch and Scheduling (HADS) framework for grid-connected electric vehicle (EV) charging stations integrated with photovoltaic (PV) generation and energy storage systems (ESS). Unlike existing studies that treat power dispatch and EV scheduling as separate tasks or rely on heuristic methods, the proposed HADS model employs a Mixed-Integer Linear Programming (MILP) approach to co-optimally dispatch real-time energy and charge EVs within a unified decision-making framework. The model enables intelligent coordination among renewable energy sources, storage dynamics, and dynamic grid pricing while ensuring operational feasibility and scalability. By dynamically aligning EV charging loads with solar availability and ESS state of charge, HADS effectively reduces grid stress, minimizes operating costs, and enhances system efficiency. A realistic case study in Taiyuan, China, is conducted using actual solar irradiance and EV demand profiles. Simulation results demonstrate that HADS reduces grid energy consumption by 60%, energy costs by 54.6%, and CO₂ emissions by 50% compared to a base case that relies solely on the grid. The model also maintains zero energy not served (ENS) under normal conditions and exhibits strong resilience under grid constraints. With renewable energy penetration exceeding 56%, the results validate the practicality, scalability, and environmental value of the HADS framework. Its deterministic MILP structure also lays a strong foundation for future integration with predictive or learning-based strategies in adaptive EV infrastructure planning.