Optimal stochastic power flow using enhanced multi-objective mayfly algorithm
For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the the...
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| Published in: | Heliyon Vol. 10; no. 5; p. e26427 |
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| Language: | English |
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Elsevier Ltd
15.03.2024
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| ISSN: | 2405-8440, 2405-8440 |
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| Abstract | For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2–4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results. |
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| AbstractList | For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2–4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results. For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results. |
| ArticleNumber | e26427 |
| Author | Luo, Qifang Zhu, Jianjun Zhou, Yongquan Huang, Huajuan Wei, Yuanfei |
| Author_xml | – sequence: 1 givenname: Jianjun surname: Zhu fullname: Zhu, Jianjun organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China – sequence: 2 givenname: Yongquan orcidid: 0000-0003-4404-952X surname: Zhou fullname: Zhou, Yongquan email: zhouyongquan@gxun.edu.cn organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China – sequence: 3 givenname: Yuanfei surname: Wei fullname: Wei, Yuanfei email: rayweiyuanfei@163.com organization: Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia – sequence: 4 givenname: Qifang surname: Luo fullname: Luo, Qifang organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China – sequence: 5 givenname: Huajuan surname: Huang fullname: Huang, Huajuan organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China |
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| Keywords | Multi-objective mayfly algorithm Stochastic power flow Metaheuristic Renewable energy |
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| SubjectTerms | algorithms electric potential difference energy costs Ephemeroptera Metaheuristic Multi-objective mayfly algorithm prediction Renewable energy solar energy Stochastic power flow Weibull statistics wind |
| Title | Optimal stochastic power flow using enhanced multi-objective mayfly algorithm |
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