Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm
The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constrai...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1293193/full |
| _version_ | 1827798926797832192 |
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| author | Ye Tian Ye Tian Zhangxiang Shi Yajie Zhang Limiao Zhang Haifeng Zhang Xingyi Zhang |
| author_facet | Ye Tian Ye Tian Zhangxiang Shi Yajie Zhang Limiao Zhang Haifeng Zhang Xingyi Zhang |
| author_sort | Ye Tian |
| collection | DOAJ |
| description | The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems. |
| first_indexed | 2024-03-11T19:42:56Z |
| format | Article |
| id | doaj.art-e59a84b39dcf48f3848452dc0e73aa14 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-11T19:42:56Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-e59a84b39dcf48f3848452dc0e73aa142023-10-06T06:52:12ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-10-011110.3389/fenrg.2023.12931931293193Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithmYe Tian0Ye Tian1Zhangxiang Shi2Yajie Zhang3Limiao Zhang4Haifeng Zhang5Xingyi Zhang6Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei, ChinaSchool of Computer Science and Technology, Anhui University, Hefei, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, ChinaSchool of Mathematical Sciences, Anhui University, Hefei, ChinaSchool of Computer Science and Technology, Anhui University, Hefei, ChinaThe optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1293193/fulloptimal power flowconstrained optimizationmany-objective optimizationco-evolutionary algorithmsmetaheuristics |
| spellingShingle | Ye Tian Ye Tian Zhangxiang Shi Yajie Zhang Limiao Zhang Haifeng Zhang Xingyi Zhang Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm Frontiers in Energy Research optimal power flow constrained optimization many-objective optimization co-evolutionary algorithms metaheuristics |
| title | Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm |
| title_full | Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm |
| title_fullStr | Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm |
| title_full_unstemmed | Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm |
| title_short | Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm |
| title_sort | solving optimal power flow problems via a constrained many objective co evolutionary algorithm |
| topic | optimal power flow constrained optimization many-objective optimization co-evolutionary algorithms metaheuristics |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1293193/full |
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