A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network
This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10028981/ |
| _version_ | 1811172247085252608 |
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| author | Kaushik Paul Pampa Sinha Yassine Bouteraa Pawe Skruch Saleh Mobayen |
| author_facet | Kaushik Paul Pampa Sinha Yassine Bouteraa Pawe Skruch Saleh Mobayen |
| author_sort | Kaushik Paul |
| collection | DOAJ |
| description | This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods. |
| first_indexed | 2024-04-10T17:26:42Z |
| format | Article |
| id | doaj.art-1cc0aa250d1f4c569780f14a343d5905 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T17:26:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1cc0aa250d1f4c569780f14a343d59052023-02-04T00:00:20ZengIEEEIEEE Access2169-35362023-01-0111102881030710.1109/ACCESS.2023.324067810028981A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission NetworkKaushik Paul0https://orcid.org/0000-0002-4022-0038Pampa Sinha1Yassine Bouteraa2Pawe Skruch3https://orcid.org/0000-0002-8290-8375Saleh Mobayen4https://orcid.org/0000-0002-5676-1875Department of Electrical Engineering, BIT Sindri, Dhanbad, IndiaSchool of Electrical Engineering, KIIT University, Bhubaneswar, IndiaCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Automatic Control and Robotics, AGH University of Science and Technology, Kraków, PolandGraduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou, TaiwanThis research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.https://ieeexplore.ieee.org/document/10028981/Manta ray forge optimizationmeta-heuristic techniqueoptimal power flowoptimizationpower reschedulingsensitivity analysis |
| spellingShingle | Kaushik Paul Pampa Sinha Yassine Bouteraa Pawe Skruch Saleh Mobayen A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network IEEE Access Manta ray forge optimization meta-heuristic technique optimal power flow optimization power rescheduling sensitivity analysis |
| title | A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network |
| title_full | A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network |
| title_fullStr | A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network |
| title_full_unstemmed | A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network |
| title_short | A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network |
| title_sort | novel improved manta ray foraging optimization approach for mitigating power system congestion in transmission network |
| topic | Manta ray forge optimization meta-heuristic technique optimal power flow optimization power rescheduling sensitivity analysis |
| url | https://ieeexplore.ieee.org/document/10028981/ |
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