Multi-objective optimization of regional energy systems with exergy efficiency and user satisfaction dynamics

The evolving energy landscape is increasingly integrating diverse energy sources, electricity, gas, heat, and cooling, reflecting a strategic shift driven by smart technologies and rising renewable adoption. However, the variability of renewable supply requires enhanced flexibility in demand-side management. This study presents a novel approach to optimizing regional integrated energy systems through a two-layer closed-loop model that incorporates exergy efficiency and user satisfaction dynamics. The model addresses the limitations of traditional energy systems, which often operate within the constraints of singular energy resources and fail to fully integrate renewable energies. The proposed model optimizes energy production, conversion, transmission, and consumption by using a multi-objective framework that includes economic, environmental, and exergy efficiency considerations. The proposed optimization approach significantly improves the performance of integrated energy systems. The energy efficiency is enhanced by 8.36 %, while exergy efficiency shows a notable increase of 1.61 %. Emissions are reduced by approximately 16.3 %, demonstrating the environmental benefits of the model. Though operational costs rise slightly, the trade-off favors sustainability with substantial gains in energy and environmental outcomes. The modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm outperforms traditional methods like NSGA-II and Standard PSO, achieving a higher Hypervolume value, indicating better convergence and solution diversity. This makes MOPSO a robust tool for solving multi-objective optimization problems in energy management.