A bi-Level collaborative optimization strategy for power quality in distribution networks based on fuzzy triple black hole multi-objective optimization algorithm
With the large-scale integration of renewable energy units and electric vehicles (EVs) into distribution networks, enhancing the power quality of these networks has emerged as a critical issue requiring immediate attention. Meanwhile, existing solution methods are inadequate for meeting the multi-ob...
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| Veröffentlicht in: | Renewable energy focus Jg. 56; S. 100760 |
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| Sprache: | Englisch |
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01.03.2026
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| ISSN: | 1755-0084 |
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| Abstract | With the large-scale integration of renewable energy units and electric vehicles (EVs) into distribution networks, enhancing the power quality of these networks has emerged as a critical issue requiring immediate attention. Meanwhile, existing solution methods are inadequate for meeting the multi-objective optimization needs of distribution networks. This study establishes a bi-level collaborative optimization strategy for improving power quality in distribution networks. Specifically, the upper planning tier aims to minimize comprehensive costs through multi-component collaborative planning. The lower operational tier, based on the comprehensive performance evaluation decision model (CPEDM), conducts coordinated scheduling of multiple components by considering both economic benefits and power quality indicators. Furthermore, a fuzzy triple black hole multi-objective optimization algorithm (MOFTBH), which boasts high solution quality, uncertainty handling capabilities, and high adaptability, is developed and employed to solve the bi-level collaborative model. The study focuses on the IEEE-33 system as the research subject, leveraging the MOFTBH for analysis. Simulation results indicate that the optimization strategy presented in this study improves economic benefits and power quality by 45.43% and 19.90%, respectively, compared to the case without any optimization. Specifically, indices such as voltage deviation, voltage fluctuation, and harmonic distortion have improved by 39.01% , 127.45% and 113.14% , MOFTBH demonstrates a 30% faster Pareto front convergence rate compared to benchmark algorithms, with a 25% improvement in solution set uniformity. Under equivalent iteration counts, the objective function values show an optimization range of 18.7%–23.4%. This planning model aims to provide intelligent and green strategies for future smart grid construction and facilitate the commercial expansion of distribution network operators. |
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| AbstractList | With the large-scale integration of renewable energy units and electric vehicles (EVs) into distribution networks, enhancing the power quality of these networks has emerged as a critical issue requiring immediate attention. Meanwhile, existing solution methods are inadequate for meeting the multi-objective optimization needs of distribution networks. This study establishes a bi-level collaborative optimization strategy for improving power quality in distribution networks. Specifically, the upper planning tier aims to minimize comprehensive costs through multi-component collaborative planning. The lower operational tier, based on the comprehensive performance evaluation decision model (CPEDM), conducts coordinated scheduling of multiple components by considering both economic benefits and power quality indicators. Furthermore, a fuzzy triple black hole multi-objective optimization algorithm (MOFTBH), which boasts high solution quality, uncertainty handling capabilities, and high adaptability, is developed and employed to solve the bi-level collaborative model. The study focuses on the IEEE-33 system as the research subject, leveraging the MOFTBH for analysis. Simulation results indicate that the optimization strategy presented in this study improves economic benefits and power quality by 45.43% and 19.90%, respectively, compared to the case without any optimization. Specifically, indices such as voltage deviation, voltage fluctuation, and harmonic distortion have improved by 39.01% , 127.45% and 113.14% , MOFTBH demonstrates a 30% faster Pareto front convergence rate compared to benchmark algorithms, with a 25% improvement in solution set uniformity. Under equivalent iteration counts, the objective function values show an optimization range of 18.7%–23.4%. This planning model aims to provide intelligent and green strategies for future smart grid construction and facilitate the commercial expansion of distribution network operators. |
| ArticleNumber | 100760 |
| Author | Xu, Jiajing Yang, Peng Wu, Chilv Zhong, Rui Guo, Lingjun Wang, Zhicong Yang, Xiaohui Tu, Zekai |
| Author_xml | – sequence: 1 givenname: Xiaohui surname: Yang fullname: Yang, Xiaohui – sequence: 2 givenname: Jiajing surname: Xu fullname: Xu, Jiajing email: 490580735@qq.com – sequence: 3 givenname: Chilv surname: Wu fullname: Wu, Chilv – sequence: 4 givenname: Lingjun surname: Guo fullname: Guo, Lingjun – sequence: 5 givenname: Zhicong surname: Wang fullname: Wang, Zhicong – sequence: 6 givenname: Rui surname: Zhong fullname: Zhong, Rui – sequence: 7 givenname: Zekai surname: Tu fullname: Tu, Zekai – sequence: 8 givenname: Peng surname: Yang fullname: Yang, Peng |
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| Keywords | Multi-component collaborative governance Power quality Bi-level optimization of distribution networks Fuzzy triple black hole multi-objective optimization algorithm Fuzzy logic controller Comprehensive performance evaluation decision model |
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| SubjectTerms | Bi-level optimization of distribution networks Comprehensive performance evaluation decision model Fuzzy logic controller Fuzzy triple black hole multi-objective optimization algorithm Multi-component collaborative governance Power quality |
| Title | A bi-Level collaborative optimization strategy for power quality in distribution networks based on fuzzy triple black hole multi-objective optimization algorithm |
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