A multi-criteria assessment method for design and dispatch of distributed energy systems considering different energy consumption attributes

Xinyu Ren, Haowen Lei, Yixuan Li, Xiaolong Guo, Zhonghao Chen, Pow-Seng Yap and Zhihua Wang

Energy, 2025, vol. 323, issue C

Abstract: Distributed energy systems (DES), utilizing multi-energy synergies and smart optimization to improve efficiency and drive a low-carbon transition, are crucial to the future of energy development. In this study, the DES, including two different solar energy utilization systems, are constructed. Then, a novel multi-objective gradient-based optimizer-shift-based density estimation algorithm is proposed and used to optimize the model of the hybrid systems for six buildings under different operation strategies. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for the selection of the best configuration scheme. The simulation results show that the comprehensive performance of the system in photovoltaic-solar thermal mode is better than that in photovoltaic-thermal mode under the identical weight mode. Moreover, the optimal operation strategy for the apartment is following hybrid electric-thermal load, and the corresponding comprehensive performance index S is 35.6%. The optimal operation strategy for the hospital, hotel, market, and office is following electric load (FEL) with electric cooling ratio, and the corresponding S is 27.09%, 27.59%, 17.38%, and 15.62%, respectively. The optimal operation strategy for the school is following thermal load (FTL), and the corresponding S is 9.84%. In addition, compared with improving the strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEAD), and multi-objective particle swarm optimization (MOPSO), the proposed algorithm can provide superior optimization results. In the case of the CCHP system for hospital building, the proposed algorithm achieves economic, energy, and environmental performance improvements of 13.10%, 3.89% and 3.32% compared to SPEA2, 14.47%, 3.47% and 3.57% compared to NSGA-II, 1.48%, 5.97% and 4.86% compared to MOEAD, and 0.90%, 0.03% and 0.39% compared to MOPSO.

Keywords: Multi-objective optimization; Distributed energy systems; Shift-based density estimation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:323:y:2025:i:c:s0360544225010643

DOI: 10.1016/j.energy.2025.135422

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