Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm
Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality...
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2024-12-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
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| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1290 |
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| author | Firmansyah Nur Budiman Taufal Hidayat Rudi Uswarman |
| author_facet | Firmansyah Nur Budiman Taufal Hidayat Rudi Uswarman |
| author_sort | Firmansyah Nur Budiman |
| collection | DOAJ |
| description | Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency. |
| format | Article |
| id | doaj-art-2aa3348fbf4e429a9378c0de736ff3e8 |
| institution | DOAJ |
| issn | 2598-3245 2598-3288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj-art-2aa3348fbf4e429a9378c0de736ff3e82025-08-20T02:50:48ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882024-12-018216317810.31961/eltikom.v8i2.12901246Optimal Power Flow using An Optimally Tuned Pattern Search AlgorithmFirmansyah Nur Budiman0Taufal Hidayat1Rudi Uswarman2Universitas Islam Indonesia, IndonesiaKing Abdulaziz University, Saudi ArabiaKing Abdulaziz University, Saudi ArabiaOptimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1290optimal power flowoptimally tuned pattern searchpower system optimization |
| spellingShingle | Firmansyah Nur Budiman Taufal Hidayat Rudi Uswarman Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer optimal power flow optimally tuned pattern search power system optimization |
| title | Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm |
| title_full | Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm |
| title_fullStr | Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm |
| title_full_unstemmed | Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm |
| title_short | Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm |
| title_sort | optimal power flow using an optimally tuned pattern search algorithm |
| topic | optimal power flow optimally tuned pattern search power system optimization |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1290 |
| work_keys_str_mv | AT firmansyahnurbudiman optimalpowerflowusinganoptimallytunedpatternsearchalgorithm AT taufalhidayat optimalpowerflowusinganoptimallytunedpatternsearchalgorithm AT rudiuswarman optimalpowerflowusinganoptimallytunedpatternsearchalgorithm |