Scenario-Based Predictive Optimization of PV-BESS Systems for Enhanced V2G Flexibility Under Uncertainties
Abstract
Smart charging of electric-vehicle (EV) fleets must balance energy cost, transformer/feeder power limits, user satisfaction, and the operational value of on-site resources such as rooftop PV and battery energy storage systems (BESS). This work presents a scenario-based model predictive control (SB-SMPC) framework for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) coordination that minimizes the net operating cost while satisfying the system constraints. The controller explicitly models stochasticity in base load, PV generation, and electricity prices via sampled scenarios, and it also integrates demand charge cost for distribution grid services. EV service quality is guaranteed through departure energy targets, connection-time policies, and a minimum state of charge (SoC) floor. BESS dynamics, round-trip efficiency, terminal SoC targets, and battery degradation costs are included to capture battery storage economics. This study compares V2G operations with and without BESS across daily horizons. Results show that SB-SMPC systematically limits transformer import, curtails PV only when economically justified, and shifts charging to low-price periods while meeting EV energy requirements; enabling V2G further reduces net costs when energy export cost and demand charges are favorable. Comparative results (with/without BESS) reveal that BESS helps to reduce net electricity cost around 4% and grid peaks around 10% as compared to without BESS installation. Imposing high demand charges further cuts the peaks about 11%. The sensitivity analysis further confirmed the robustness of the proposed framework under varying load, PV, and price conditions.