Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method.