Optimization of power allocation scheduling and component sizing in battery-assisted electrolyser systems

This paper presents a co-design optimization framework for battery-assisted electrolyser systems aimed at improving the cost-effectiveness of green hydrogen production. As renewable energy sources (RES) increasingly strain electricity grids, decentralized power-to-hydrogen (P2H) production can mitigate grid imbalance while supplying a clean energy carrier, yet its financial viability remains uncertain. The proposed framework integrates models of battery energy storage systems (BESS), electrolysers, RES, and grid connections, and optimizes them on two levels: Mixed-Integer Linear Programming (MILP) determines the operational strategy that maximizes operating profit, while Bayesian Optimization (BO) selects the component sizing that maximizes net present value (NPV) over a continuous three-year horizon. The framework is applied to two real-world cases: a grid-limited 2 MW solar site (case A) and a more balanced solar–wind site (case B). For case A the optimal design pairs a 964 kW electrolyser with a 220 kWh battery, whereas case B favors a 475 kW electrolyser without a battery, showing that battery storage is decisive only when the grid connection is small. At a hydrogen price of €8/kg both systems are near break-even (NPV €−0.1 to €−0.9 million) and become clearly profitable at €16/kg (NPV €3.9–4.3 million); a sensitivity analysis shows they remain firmly loss-making across the €3–5/kg range projected for 2030. The electrolyser operates with an average efficiency of about 80% (HHV), at a levelized cost of hydrogen of about €10/kg. These results quantify how hydrogen price, grid capacity, and component sizing jointly govern the techno-economic feasibility of P2H systems, and provide a transferable framework for their cost-effective design.