Evaluating the role of green hydrogen in managing BEV-Induced peak loads: a comparative techno-economic and environmental assessment with BESS in Italy

• BEV-driven peak loads in Italy by 2030 were modeled under Fit for 55 targets. • Hydrogen and BESS were assessed for techno-economic and environmental impacts. • Hydrogen required 520–630 tons/day production for evening peak balancing. • BESS showed lower LCOE (€115–135/MWh) than hydrogen (€160–210/MWh). • Monte Carlo analysis confirmed hydrogen competitiveness only under ideal costs. The rapid growth of battery electric vehicles (BEVs) is expected to substantially intensify short-duration peak electricity demand, creating new challenges for grid operation and decarbonization. This study contributes new system-level evidence on the relative economic and environmental suitability of large-scale energy storage technologies for BEV-driven peak load mitigation, with a specific focus on the Italian electricity system by 2030. The key novelty lies in the integrated assessment of BEV-induced peak demand, optimized storage sizing, and uncertainty-aware techno-economic and environmental comparison of Battery Energy Storage Systems (BESS) and green hydrogen under realistic market conditions. Hourly BEV charging demand is simulated based on EU Fit for 55 targets and national mobility data and superimposed on historical Italian load profiles to quantify additional peak requirements. A formal capacity optimization model is applied to determine minimum-cost storage configurations. Economic performance is evaluated using levelized cost metrics derived from a discounted cash-flow framework, while lifecycle CO 2 emissions are assessed using established inventory datasets. Uncertainty in key drivers, including capital costs, efficiencies, electricity prices, financing conditions, and BEV penetration, is explicitly captured through Monte Carlo simulations. To enhance the interpretability of high-dimensional uncertainty results, a neural network is trained as a surrogate model on Monte Carlo-generated input–output datasets. The neural network does not replace probabilistic analysis but provides smoothed response surfaces that reveal nonlinear cost sensitivities and interaction effects across scenarios. Results show that BEV charging could increase evening peak demand by 3.2–5.8 GW, requiring 13.5–16 GWh of BESS capacity or substantially larger hydrogen infrastructure due to conversion losses. BESS exhibits lower levelized costs (€115–135/MWh) and lower lifecycle emissions per delivered kilowatt-hour compared to green hydrogen (€160–210/MWh) in most scenarios, outperforming hydrogen in over 80% of simulations. The findings demonstrate that BESS is currently the most effective solution for short-duration BEV-driven peak shaving, while green hydrogen is better suited for long-duration and seasonal storage applications.