Enhancing economic and environmental performance of energy communities: A multi-objective optimization approach with mountain gazelle optimizer

Abstract

This research explores three distinct configurations of energy communities, collectives of local consumers utilizing renewable electrical and thermal energy. The study aims to enhance economic outcomes while addressing climate change and meeting energy demands through advanced strategies. The optimization framework focuses on refining the design, capacity, and efficiency of energy conversion and storage systems, balancing investment and operational costs with greenhouse gas emissions (GhGE) across their lifecycle. Two innovative demand-side management (DSM) strategies are introduced: a downstream pricing-based demand response program (DRP) and an upstream DSM model aligning electricity demand with locally available renewable energy. The study employs a multi-objective modeling approach using the novel mountain gazelle optimizer (MGO), which integrates fuzzy theory and Pareto optimization to minimize costs and emissions. Results demonstrate significant benefits of the proposed DSM strategies. DSM 2 enhances self-consumption rates by approximately 17 % for individual prosumers (IP) and 14–17 % for energy communities, while DSM 1 effectively reduces grid exchanges by 9 % for prosumers and up to 17 % for energy communities. The optimization framework also facilitates a more balanced distribution of generation and demand, alleviating grid stress. These findings underscore the potential of integrated DSM strategies and multi-objective optimization in advancing the performance and sustainability of renewable energy systems, offering diverse advantages in self-consumption and grid interaction.