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 decentralised renewable
generation and hybrid storage system to create a Renewable Energy Community (REC), developed
in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and
Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage
data from a rural island case study location. Cost remuneration is 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 results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with
typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years.
Emissions output from grid only use is reduced by 72% to 69 gCO2e/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.