*Article* **Optimal Design and Analysis of a Hybrid Hydrogen Energy Storage System for an Island-Based Renewable Energy Community**

**Robert Garner and Zahir Dehouche \***

College of Engineering, Design and Physical Sciences, Brunel University London, London UB8 3PH, UK; robert.garner2@brunel.ac.uk

**\*** Correspondence: zahir.dehouche@brunel.ac.uk

**Abstract:** Installations of decentralised renewable energy systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for a decentralised renewable generation and hybrid storage system to create a renewable energy community (REC), developed in Python. The model implements photovoltaic (PV) solar and wind turbines combined with a hybrid battery and regenerative hydrogen fuel cell (RHFC). The electrical service demand was derived using real usage data from a rural island case study location. Cost remuneration was managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective genetic algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design resulted in a levelized cost of electricity (LCOE) of 0.15 EUR/kWh, reducing costs by over 50% compared with typical EU grid power, with a project internal rate of return (IRR) of 10.8%, simple return of 9.6%/year, and return on investment (ROI) of 9 years. The emissions output from grid-only use was reduced by 72% to 69 gCO2*e*/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

**Keywords:** decentralised energy systems; renewable energy community; hydrogen energy storage system; decarbonisation; techno-economic assessment; multi-objective optimisation
