Economic-Environmental Stochastic Scheduling for Optimal Smart V2X Coordinated with Integrated Demand Response Program

This study presents a centralized stochastic scheduling framework for a smart energy management system that integrates hydrogen storage, thermal and electrical storage, renewable energy sources, electric vehicle stations with bidirectional V2X functionality, and demand response programs. The objective is to minimize operational costs and pollutant emissions under variable energy prices and uncertain renewable generation profiles. A multi-objective mixed-integer linear programming model is developed and solved using the MO-NSGAII algorithm, which considers uncertainties in electricity prices, wind, and solar output. The framework is applied to a 24-hour scheduling horizon for system comprising 20 EV parking lots, a 60-kW wind turbine, a 160 m² solar array (32 kW capacity), combined heat and power units, boilers, and hydrogen-based fuel cells. Simulation results show that coordinated V2X and demand response shift 18% of load from peak to off-peak periods, increase storage utilization by 22% (with a maximum BESS charge of 95 kW), and reduce peak demand by 7.59%. Case 2 showed a 34.1% reduction in operational expenses compared to the baseline and a drop in emissions to 11,280 kg. Also, the total system cost stayed close to 640 USD. It is confirmed by these results that smart energy hubs powered by hydrogen storage, coordinated V2X operations, and demand response are both cost-effective and eco-friendly.