Balancing trade-offs in Nordic smart local energy systems: multi-objective and multi-criteria optimization for cost, emissions, and self-sufficiency

Smart local energy systems (SLESs) can advance the transition towards sustainable energy. However, their multi-sector and multi-stakeholder composition demands advanced planning approaches that balance factors such as cost, emissions, and self-sufficiency. This study investigates multi-dimensional planning of an SLES located in Finland using multi-objective optimization, multi-criteria decision analysis, and rolling horizon simulation. The analysis integrates direct and embodied greenhouse gas emissions, operational performance simulation, and multi-year optimization data (2020–2024). The resulting Pareto-optimal solutions show significant potential for cost and emission reductions and self-sufficiency gains within the SLES. Compared to the baseline system, levelized cost and carbon intensity decreased by 14 % and 68 %, respectively, while self-sufficiency increased by 64 %. Furthermore, a compromise solution selected using TOPSIS and hybrid criteria weights, based in part on expert input, demonstrated 85 % reduced carbon intensity and 88 % increased self-sufficiency, albeit with 20 % higher cost. The results indicate that optimizing SLESs for emissions and self-sufficiency, in addition to cost, can reveal highly cost-effective improvements in environmental performance and energy autonomy. Notably, the best investment profile can be found in a solution optimized for both cost and emissions. The findings are relevant for regions with cold climates and high shares of renewable energy.