Multi-Objective Optimisation of a Spanish Energy Community Powered by Renewable Energy-Based Electric Vehicle Charging Facilities

The Energy Community (EC) can play an essential role in collective energy sharing and management by maximising on-site energy use among its members (i.e., consumers, producers, and prosumers). ECs can play a pivotal role in maximising emission reductions in the transport sector; thus, they need to expand the deployment of Electric Vehicle (EV) charging facilities (EVCFs) powered by renewable energy sources (RESs). Therefore, the main aim of this study is to optimise energy consumption costs and maximise the RESs utilisation for household loads and EV charging (EVC) loads in a Spanish EC through load scheduling. To improve the performance of the Energy Management System (EMS) and EVC operations, minimise the EC’s energy consumption cost, and maximise the RESs utilisation, the metaheuristic Particle Swarm Optimisation algorithm is employed to solve the multi-objective optimisation problem. Moreover, two strategies are applied and compared in this study to describe the EVC load variability, including machine-learning algorithms and statistical methods. By comparing these strategies, the accuracy and reliability of load estimation and prediction can be improved. This study analyses six scenarios: Scenario 1: grid-estimated load, which can be considered grid-to-vehicle (G2V); Scenario 2: grid-predicted load; Scenario 3: grid-RES-estimated load; Scenario 4: grid-RES-predicted load; Scenario 5: grid-RES-estimated load-scheduling; and Scenario 6: grid-RES-predicted load-scheduling. By developing and examining this integrated, two-step optimisation framework, the study demonstrates the significant economic and environmental benefits of EVCF supported by RESs. Overall, the results demonstrate that centralised EVCF powered by RESs can offer sustainable, cost-effective charging options for EC members, increase EV adoption, and assist in fulfilling the European Union’s greenhouse gas reduction targets.