a framework for stochastic risk-averse decision making in hydrogen-powered intelligent electric vehicles parking management with carbon and green certificate considerations

Energy management in intelligent electric parking lots (ipl) plays a crucial role in achieving technical and environmental goals by utilizing renewable energy sources (res) and hydrogen storage systems (hss). this article proposes a framework for risk-averse decision-making in hydrogen-powered smart parking management, considering carbon considerations and green certifications. given the uncertainty in input parameters such as solar radiation, temperature, wind speed, and ipl load, a probabilistic model is developed using a combination of two-point estimation method and information gap decision theory (igdt). furthermore, a combined optimization method, differential honey badger algorithm (dhba), is employed to optimize operational costs, including energy procurement from the grid, electric vehicle (ev) charging costs in smart parking lots, and costs associated with green certifications and carbon emissions, as the main objectives of the optimization problem. the main idea of this article is for a typical ipl comprising a hydrogen storage system (hss) consisting of a fuel cell, electrolyzer, and hydrogen storage tank, alongside load demand alongside res. additionally, alongside energy management, demand response (dr) management has also been optimized. simulation results achieve all technical and economic objectives with the presence of renewable energy sources and electric vehicles, resulting in a 15.5% increase in profit. furthermore, considering uncertainty leads to a 9.6% decrease in profit compared to the absence of these sources. moreover, considering green certifications and carbon emissions results in a significant reduction in pollution emissions.