Holistic planning framework for multi-energy microgrids: A multi-objective perspective on system optimization

Multi-energy microgrids provide a sustainable and efficient solution for meeting energy demand across sectors. However, determining the optimal capacities of distributed energy resources (DERs) is challenging due to renewable generation variability and the intricate coupling of energy systems. This study developed a multi-objective planning framework to balance system costs, renewable energy integration, and curtailment reduction in cold-climate regions like Norway. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), which enables decision-makers to select the best configuration based on predefined criteria. Sensitivity analysis revealed a 70 % threshold for renewable energy share, beyond which curtailment and system costs increase significantly. Wind energy systems significantly affect renewable energy share and curtailment, while the role of energy storage and heat pumps in mitigating curtailment was also analyzed. The post-optimization analysis incorporated relative grid size (RGS) as an additional criterion to study the impact of electrical and heating system interdependence on system planning. The results emphasize the importance of optimizing relative grid size for improved system resilience under input variability, with RGS = 1.11 achieving the most balanced performance across objectives and performance metrics. The potential for extending the developed model to other climatic regions and networks is also discussed. • Multi-objective planning for a multi-energy microgrid optimized for cold climates. • Optimized the system for cost, renewable energy share and energy curtailment. • The optimization problem was solved using a combination of NSGA-II and TOPSIS. • The effect of capacity configurations on design objectives was thoroughly explored. • Impact of electrical and heating grid sizes on capacity configurations was analyzed.