Application of Mixed Strategist Dynamics and Grid‑Oriented Optimization for Evaluation of Plug‑in Electric Vehicle Load Scheduling - Kandipalli, Tejasri; Vaisakh, Kanchapogu (New Energy Exploitation and Application, 2026)
This paper presents an enhanced and comprehensive framework for optimal scheduling of Plug-in Electric Vehicle (PEV) charging by integrating Mixed Strategist Dynamics (MSD) with Forward Dynamic Programming (FDP) to achieve both user-level fairness and grid-oriented optimization. The MSD mechanism generates probabilistic charging strategies that distribute demand across time slots while incorporating equity-based payoff functions to prevent synchronized charging peaks. Building on these probabilistic schedules, an FDP-based deterministic refinement layer is introduced to ensure accurate State-of-Charge (SoC) fulfilment, minimize operating cost, and satisfy grid operational constraints. To ensure technical feasibility, the proposed hybrid MSD–FDP approach is validated on the IEEE 34-bus radial distribution system using Backward/Forward-Sweep (BFS) power-flow analysis. A voltage-penalty cost component is incorporated to restrict bus-voltage deviations within 0.955–1.05 p.u. and to prevent transformer overloading under high EV penetration. The model also integrates Vehicle-to-Grid (V2G) capability, enabling controlled discharging during peak-load conditions to support voltage recovery and improve feeder stability. Simulation results demonstrate that the proposed hybrid framework achieves substantial improvements over MSD-only scheduling and uncoordinated charging. Peak-load demand is reduced by approximately 27%, and the minimum bus voltage is improved from 0.91 p.u. to 0.958 p.u. Additionally, fairness among EVs is significantly enhanced with entropy values averaging 1.77–1.79, indicating balanced access to charging resources. The findings confirm that coordinated charging with V2G support can effectively transform EV fleets into flexible distributed energy assets while ensuring cost efficiency, technical reliability, and scalability for real-world smart-grid applications.