Grey Wolf Optimizer for solving single objective functions optimal power flow
In power system, the optimal power flow (OPF) is one of the main significant issues that should be solved to achieve the optimal operation, planning, and energy management. Many stochastic optimization algorithms have been suggested to solve OPF problems. Grey wolf optimizer (GWO) is one of the meta...
Saved in:
| Published in | Proceedings (IEEE International Engineering Management Conference) pp. 1 - 5 |
|---|---|
| Main Authors | , , |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
23.03.2023
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2159-3604 |
| DOI | 10.1109/ATEE58038.2023.10108149 |
Cover
| Abstract | In power system, the optimal power flow (OPF) is one of the main significant issues that should be solved to achieve the optimal operation, planning, and energy management. Many stochastic optimization algorithms have been suggested to solve OPF problems. Grey wolf optimizer (GWO) is one of the metaheuristic techniques that have been recently applied in power systems. The main aim of this paper is to find optimal objective functions such as fuel cost of generators, emissions, active power losses, and voltage deviation at load bus. The control variables must be setting to obtain optimal objective function are active power of generators (except the swing bus generator), voltage magnitude at load bus, sources VAR compensations that are connected to transmission lines to compensate the reactive power on the network and tap changer setting at the transformers. To prove the superiority and efficiency of GWO in power system applications, IEEE 57-bus test power is the network that has been applied to it. The comparison results of objective functions confirmed the superiority of GWO in providing better solutions than the well-known methods reported in the literature. |
|---|---|
| AbstractList | In power system, the optimal power flow (OPF) is one of the main significant issues that should be solved to achieve the optimal operation, planning, and energy management. Many stochastic optimization algorithms have been suggested to solve OPF problems. Grey wolf optimizer (GWO) is one of the metaheuristic techniques that have been recently applied in power systems. The main aim of this paper is to find optimal objective functions such as fuel cost of generators, emissions, active power losses, and voltage deviation at load bus. The control variables must be setting to obtain optimal objective function are active power of generators (except the swing bus generator), voltage magnitude at load bus, sources VAR compensations that are connected to transmission lines to compensate the reactive power on the network and tap changer setting at the transformers. To prove the superiority and efficiency of GWO in power system applications, IEEE 57-bus test power is the network that has been applied to it. The comparison results of objective functions confirmed the superiority of GWO in providing better solutions than the well-known methods reported in the literature. |
| Author | Al-Kaabi, Murtadha Dumbrava, Virgil Eremia, Mircea |
| Author_xml | – sequence: 1 givenname: Murtadha surname: Al-Kaabi fullname: Al-Kaabi, Murtadha email: mmsk.1986s@gmail.com organization: University POLITEHNICA of Bucharest,Doctoral School of Energy Engineering,Romania – sequence: 2 givenname: Virgil surname: Dumbrava fullname: Dumbrava, Virgil email: virgil.dumbrava@upb.ro organization: University POLITEHNICA of Bucharest,Doctoral School of Energy Engineering,Romania – sequence: 3 givenname: Mircea surname: Eremia fullname: Eremia, Mircea email: eremia1@yahoo.com organization: University POLITEHNICA of Bucharest,Doctoral School of Energy Engineering,Romania |
| BookMark | eNo1kEFLw0AUhFdRsNb8A8H9A6nv7ctu8o6lxCpUeql4LNnNRlJitiSxpf56U9TLzFy-YZhbcdWG1gvxgDBDBH6cb_JcZ0DZTIGiGQJChglfiIhTzkgDETKZSzFRqDkmA8mNiPp-BwDIzMbQRLwuO3-S76Gp5Ho_1J_1t-9kFTrZh-ZQtx-yH6XxMtidd0N98LL6ascQ2l6GM1A0ch-OZ6gJxztxXRVN76M_n4q3p3yzeI5X6-XLYr6Ka0QeYofKpKUtHXEBbBOjKjDaKees1i4ltBZTKDJtWHmHmSWdakW-QoCySBxNxf1vb-293-67cUZ32v4_QD8iK1KN |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ATEE58038.2023.10108149 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798350331936 |
| EISSN | 2159-3604 |
| EndPage | 5 |
| ExternalDocumentID | 10108149 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IM 6IN AAJGR AAWTH ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i119t-c1267dbdc39a09b462f065c2ccb55c731bb170a85692ec18b357523ef100da4c3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 27 02:19:46 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i119t-c1267dbdc39a09b462f065c2ccb55c731bb170a85692ec18b357523ef100da4c3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10108149 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-March-23 |
| PublicationDateYYYYMMDD | 2023-03-23 |
| PublicationDate_xml | – month: 03 year: 2023 text: 2023-March-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (IEEE International Engineering Management Conference) |
| PublicationTitleAbbrev | ATEE |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0001999663 |
| Score | 1.8294688 |
| Snippet | In power system, the optimal power flow (OPF) is one of the main significant issues that should be solved to achieve the optimal operation, planning, and... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Active power losses (APL) Costs Emission (Em) Fuels Generators Linear programming Metaheuristics Reactive power Tap changers Total fuel Cost (TFC) Voltage deviation (VD) |
| Title | Grey Wolf Optimizer for solving single objective functions optimal power flow |
| URI | https://ieeexplore.ieee.org/document/10108149 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3LSgMxFA22K1cqVnyThdsZJ89pliItRWh10WJ3JU-ojp1Spoj9em9mWkVBcBcGAiE3k3Nyc04uQjeE5kZxIxOv4bjKHaOJdsEkTgojgpSa2-hGHo7kYMIfpmK6NavXXhjvfS0-82ls1nf5rrTrmCqDP5wAgnHVQq28Kxuz1ndCpabubKvhIpm6vRv3eqKbsajgoizd9f5RR6WGkf4BGu0G0KhHXtN1ZVK7-fU2479HeIg63449_PSFRUdozy-O0RDO_x_4uSwCfoSd4W2-8SsMJBXDeot5BBzzBIXHpXlptj0cUa5eiLiMHXSBl7GKGg5F-d5Bk35vfD9ItuUTkjkhqkosoTJ3xlmmdKYMlzQA37DUWiOEzRkxhuSZ7gqpqLekaxhQN8p8IFnmIEbsBLUX5cKfIswJ8AQF4G655wB6WgRAfuuBbjltZDhDnTgXs2XzQsZsNw3nf3y_QPsxJFHLRdklalertb8CcK_MdR3UT83CpDg |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3LSgMxFA1aF7pSseLbLNzOOHlOsxRprdpWFy12V_IaUMdOKS1Cv96bmdaiILgbAoGQm-Sc3DknF6ErQlOjuJGR13Bd5Y7RSLvMRE4KIzIpNbfBjdztyfaAPwzFcGlWL70w3vtSfObj8Fn-y3eFnYdUGexwAgjG1SbaEpxzUdm11imVkryzpYqLJOr6pt9sikbCgoaLsnjV_0cllRJIWruotxpCpR95j-czE9vFr9cZ_z3GPVRfe_bw8zca7aMNPz5A3TuIFH4p8gw_wdnw8brwUww0FcOKC5kEHDIFuceFeasOPhxwrlyKuAgddI4noY4azvLis44GrWb_th0tCyhEr4SoWWQJlakzzjKlE2W4pBkwDkutNULYlBFjSJrohpCKeksahgF5o8xnJEkcRIkdotq4GPsjhDkBpqAA3i33HGBPiwyw33ogXE4bmR2jepiL0aR6I2O0moaTP9ov0Xa73-2MOve9x1O0E8ITlF2UnaHabDr35wD1M3NRBvgLmUGnhQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+%28IEEE+International+Engineering+Management+Conference%29&rft.atitle=Grey+Wolf+Optimizer+for+solving+single+objective+functions+optimal+power+flow&rft.au=Al-Kaabi%2C+Murtadha&rft.au=Dumbrava%2C+Virgil&rft.au=Eremia%2C+Mircea&rft.date=2023-03-23&rft.pub=IEEE&rft.eissn=2159-3604&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FATEE58038.2023.10108149&rft.externalDocID=10108149 |