Multi-Objective Energy Management of Renewable Energy Communities and Smart Grids

Abstract Escalating electrical energy demand and the integration of distributed renewable energy resources (DRERs) necessitate innovative solutions, such as renewable energy communities (RECs) and smart grids (SGs). Managing DRER variability and daily load profiles poses significant energy management challenges. Advancements in SGs, including demand response (DR) and DRER integration, offer clear solutions. This study employs a multi-objective optimization approach to manage energy procurement and consumption in DRER-integrated RECs and SGs, aiming to reduce energy costs, enhance responsiveness, lower emissions, and improve comfort. An artificial neural network (ANN) model handles DRER’s uncertainty. At the same time, the novel multi-objective ant nest algorithm (MOANA) identifies optimal solutions for energy cost, comfort index (CI), carbon emissions, peak-to-average ratio (PAR), and responsiveness index (RI). Results show MOANA with DR outperforms the non-DR model in optimizing cost, emissions, RI, and CI, supporting reliable, sustainable, and efficient energy management in RECs and SGs.