Scheduling optimization of a regionally integrated energy system based on an improved multi-objective particle swarm algorithm
With the transformation of the global energy structure, the regional integrated energy system (RIES) has become one of the research hotspots due to the characteristics of energy complementarity and multi-energy coupling. However, in actual operation, the integrated district energy system suffers fro...
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| Vydáno v: | Energy reports Ročník 14; s. 3888 - 3904 |
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| Jazyk: | angličtina |
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01.12.2025
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| ISSN: | 2352-4847, 2352-4847 |
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| Abstract | With the transformation of the global energy structure, the regional integrated energy system (RIES) has become one of the research hotspots due to the characteristics of energy complementarity and multi-energy coupling. However, in actual operation, the integrated district energy system suffers from a problem of reduced scheduling accuracy when coping with multi-energy coupling. To solve this problem, this paper proposes an improved particle swarm algorithm based on the neighborhood particle diversity learning mechanism to optimize the scheduling model. In the optimization process, the neighboring particle diversity learning mechanism and the adaptive strategy based on the gradient change of the iteration number are used to update the particle velocity component, the spiral contraction strategy incorporated into the whale optimization algorithm is used to update the position component, and the crowded distances of particles in the mesh are computed to maintain the size of the Pareto solution set; based on which, the hierarchical analysis of the superiority and inferiority of the solution distances method is applied to the Pareto solution set for decision making to get the optimal scheduling result. The study results show that the proposed method has significant advantages over the traditional particle swarm method in terms of economic cost, energy utilization efficiency, and system coordination, and the performance is improved by 1.43 % and 1.75 % in heating and non-heating periods, respectively. Finally, this study provides theoretical support for the optimal scheduling of regional integrated energy systems.
•Speed component update mechanism based on neighboring particle diversity learning.•Learning factor update strategy based on gradient changes in the number of iterations.•Position component update mechanism based on spiral contraction strategy. |
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| AbstractList | With the transformation of the global energy structure, the regional integrated energy system (RIES) has become one of the research hotspots due to the characteristics of energy complementarity and multi-energy coupling. However, in actual operation, the integrated district energy system suffers from a problem of reduced scheduling accuracy when coping with multi-energy coupling. To solve this problem, this paper proposes an improved particle swarm algorithm based on the neighborhood particle diversity learning mechanism to optimize the scheduling model. In the optimization process, the neighboring particle diversity learning mechanism and the adaptive strategy based on the gradient change of the iteration number are used to update the particle velocity component, the spiral contraction strategy incorporated into the whale optimization algorithm is used to update the position component, and the crowded distances of particles in the mesh are computed to maintain the size of the Pareto solution set; based on which, the hierarchical analysis of the superiority and inferiority of the solution distances method is applied to the Pareto solution set for decision making to get the optimal scheduling result. The study results show that the proposed method has significant advantages over the traditional particle swarm method in terms of economic cost, energy utilization efficiency, and system coordination, and the performance is improved by 1.43 % and 1.75 % in heating and non-heating periods, respectively. Finally, this study provides theoretical support for the optimal scheduling of regional integrated energy systems.
•Speed component update mechanism based on neighboring particle diversity learning.•Learning factor update strategy based on gradient changes in the number of iterations.•Position component update mechanism based on spiral contraction strategy. |
| Author | Zhang, Xiaoming Ding, Baozhou Yang, Peihong Sun, Zhangzhuoyu Cheng, Haojie |
| Author_xml | – sequence: 1 givenname: Haojie orcidid: 0009-0003-0709-8821 surname: Cheng fullname: Cheng, Haojie email: 2024023177@stu.imust.edu.cn – sequence: 2 givenname: Xiaoming surname: Zhang fullname: Zhang, Xiaoming email: 2020911@imust.edu.cn – sequence: 3 givenname: Peihong surname: Yang fullname: Yang, Peihong email: yangpeihong1025@imust.edu.cn – sequence: 4 givenname: Baozhou surname: Ding fullname: Ding, Baozhou email: 2024022432@stu.imust.edu.cn – sequence: 5 givenname: Zhangzhuoyu surname: Sun fullname: Sun, Zhangzhuoyu email: 2024022427@stu.imust.edu.cn |
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| Keywords | Improved multi-objective particle swarm algorithm Regional integrated energy systems (RIES) Hierarchical analysis Scheduling optimization |
| Language | English |
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