Integrating green hydrogen production and electrical energy storage in energy communities under uncertainty

This paper addresses the integration of green hydrogen production and electrical energy storage in renewable energy communities. An optimal approach is proposed for sizing an electrolyzer and a battery energy storage system within the community which includes photovoltaic generation and loads. The method tackles key planning challenges by incorporating uncertainty handled through decision theory techniques. Multiple scenarios are defined based on variations in photovoltaic generation, load demand, and electricity price profiles to capture a wide range of operating conditions. The proposed planning model includes scheduling strategies aimed at facilitating the integration of the distributed resources by coordinating their power flows, with the dual objectives of maximizing green hydrogen production and enhancing energy sharing within the community. The scheduling is solved through mixed-integer linear programming, whose combination with decision theory reduces computational effort by exhaustively considering a range of scenarios through their probabilities of occurrence and distinct characteristics. To evaluate the impact of the resource contributions under the community’s self-consumption incentive policy, the paper includes the formulation of shared energy models for three distinct system configurations, each adapted from a general framework to address the specific characteristics of the respective configurations. The results of numerical applications provide evidence of the effectiveness of the proposed procedure and present an analysis of economic viability. The analysis shows that, as the hydrogen selling price increases, the optimal planning procedure leads to increased hydrogen production, which in turn boosts the net economic benefit. The proposed approach provides a flexible decision–support tool for planners and policymakers, enabling tailored insights into optimal system design based on the specific objectives and available information. • Incentive-based energy community design to support green hydrogen production. • Mixed-integer linear programming integrated into decision theory. • Combining decision theory and scenario-based scheduling to manage uncertainty. • Electrolyzer and battery sizing under various community configurations.