Discrete complex-valued code pathfinder algorithm for wind farm layout optimization problem
•A discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed.•The DCPFA algorithm’s exploration and exploitation abilities were improved.•The DCPFA is applied to solve wind farm layout optimization (WFLO) problem.•The experimental results show that DCPFA found solutions have lower...
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| Published in: | Energy conversion and management. X Vol. 16; p. 100307 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2022
Elsevier |
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| ISSN: | 2590-1745, 2590-1745 |
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| Abstract | •A discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed.•The DCPFA algorithm’s exploration and exploitation abilities were improved.•The DCPFA is applied to solve wind farm layout optimization (WFLO) problem.•The experimental results show that DCPFA found solutions have lower cost values per unit of power.
In wind farm planning, it is necessary to optimize the location of turbines to reduce the influence of eddy currents between each other and generate more energy. This problem is also transformed into a discrete optimization problem, it is named wind farm layout optimization (WFLO) problem, to solve this optimization problem we can obtain the optimal turbine placement scheme. With the complexity of wind condition in WFLO problem increases, it becomes very difficult to solve, so more and more researchers use metaheuristic algorithms to solve this problem. Improve the solution accuracy of the WFLO problem, it will increase renewable energy use and lowering carbon emissions. In order to improve the solution accuracy of WFLO problem, a discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed in this paper, and used to solve the WFLO problem. In DCPFA, the algorithm’s exploration ability was improved, and the algorithm achieves a balance between exploration and exploitation. To test the performance of DCPFA in solving the WFLO problem, two complex wind conditions in wind farms are simulated. Comparing the results with other famous metaheuristic optimization algorithms and recently published literature, the DCPFA found a solution that will have lower cost values per unit of power, such as DCPFA’s solution in wind condition (1) can output 34120(KW), it is bigger than RSA’s 32498(KW) and GA’s 32038(KW), DCPFA’s solution in wind condition (2) can output 19375(KW), it is bigger than SSA’s 17781(KW) and BPSO-TVAV’s 15,796 (KW). More importantly, in the objective function (Cost/KW), DCPFA is also ranked first in two wind condition. According to the experimental results, it can be explained that DCPFA’s effectiveness and robustness in solving WFLO problems. |
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| AbstractList | In wind farm planning, it is necessary to optimize the location of turbines to reduce the influence of eddy currents between each other and generate more energy. This problem is also transformed into a discrete optimization problem, it is named wind farm layout optimization (WFLO) problem, to solve this optimization problem we can obtain the optimal turbine placement scheme. With the complexity of wind condition in WFLO problem increases, it becomes very difficult to solve, so more and more researchers use metaheuristic algorithms to solve this problem. Improve the solution accuracy of the WFLO problem, it will increase renewable energy use and lowering carbon emissions. In order to improve the solution accuracy of WFLO problem, a discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed in this paper, and used to solve the WFLO problem. In DCPFA, the algorithm’s exploration ability was improved, and the algorithm achieves a balance between exploration and exploitation. To test the performance of DCPFA in solving the WFLO problem, two complex wind conditions in wind farms are simulated. Comparing the results with other famous metaheuristic optimization algorithms and recently published literature, the DCPFA found a solution that will have lower cost values per unit of power, such as DCPFA’s solution in wind condition (1) can output 34120(KW), it is bigger than RSA’s 32498(KW) and GA’s 32038(KW), DCPFA’s solution in wind condition (2) can output 19375(KW), it is bigger than SSA’s 17781(KW) and BPSO-TVAV’s 15,796 (KW). More importantly, in the objective function (Cost/KW), DCPFA is also ranked first in two wind condition. According to the experimental results, it can be explained that DCPFA’s effectiveness and robustness in solving WFLO problems. •A discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed.•The DCPFA algorithm’s exploration and exploitation abilities were improved.•The DCPFA is applied to solve wind farm layout optimization (WFLO) problem.•The experimental results show that DCPFA found solutions have lower cost values per unit of power. In wind farm planning, it is necessary to optimize the location of turbines to reduce the influence of eddy currents between each other and generate more energy. This problem is also transformed into a discrete optimization problem, it is named wind farm layout optimization (WFLO) problem, to solve this optimization problem we can obtain the optimal turbine placement scheme. With the complexity of wind condition in WFLO problem increases, it becomes very difficult to solve, so more and more researchers use metaheuristic algorithms to solve this problem. Improve the solution accuracy of the WFLO problem, it will increase renewable energy use and lowering carbon emissions. In order to improve the solution accuracy of WFLO problem, a discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed in this paper, and used to solve the WFLO problem. In DCPFA, the algorithm’s exploration ability was improved, and the algorithm achieves a balance between exploration and exploitation. To test the performance of DCPFA in solving the WFLO problem, two complex wind conditions in wind farms are simulated. Comparing the results with other famous metaheuristic optimization algorithms and recently published literature, the DCPFA found a solution that will have lower cost values per unit of power, such as DCPFA’s solution in wind condition (1) can output 34120(KW), it is bigger than RSA’s 32498(KW) and GA’s 32038(KW), DCPFA’s solution in wind condition (2) can output 19375(KW), it is bigger than SSA’s 17781(KW) and BPSO-TVAV’s 15,796 (KW). More importantly, in the objective function (Cost/KW), DCPFA is also ranked first in two wind condition. According to the experimental results, it can be explained that DCPFA’s effectiveness and robustness in solving WFLO problems. |
| ArticleNumber | 100307 |
| Author | Li, Ning Luo, Qifang Zhou, Yongquan Huang, Huajuan |
| Author_xml | – sequence: 1 givenname: Ning surname: Li fullname: Li, Ning 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: yongquanzhou@126.com organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China – sequence: 3 givenname: Qifang surname: Luo fullname: Luo, Qifang organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China – sequence: 4 givenname: Huajuan surname: Huang fullname: Huang, Huajuan organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China |
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| Keywords | Wind farm layout optimization Pathfinder algorithm Discrete complex-valued code pathfinder algorithm Metaheuristic |
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| Snippet | •A discrete complex-valued code pathfinder algorithm (DCPFA) has been proposed.•The DCPFA algorithm’s exploration and exploitation abilities were improved.•The... In wind farm planning, it is necessary to optimize the location of turbines to reduce the influence of eddy currents between each other and generate more... |
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| SubjectTerms | Discrete complex-valued code pathfinder algorithm Metaheuristic Pathfinder algorithm Wind farm layout optimization |
| Title | Discrete complex-valued code pathfinder algorithm for wind farm layout optimization problem |
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