Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm
Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. Howeve...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.13113 |
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| _version_ | 1841546544888676352 |
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| author | Noor Habib Khan Yong Wang Salman Habib Raheela Jamal Muhammad Majid Gulzar S. M. Muyeen Mohamed Ebeed |
| author_facet | Noor Habib Khan Yong Wang Salman Habib Raheela Jamal Muhammad Majid Gulzar S. M. Muyeen Mohamed Ebeed |
| author_sort | Noor Habib Khan |
| collection | DOAJ |
| description | Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u. |
| format | Article |
| id | doaj-art-effa279ec44040a89fdbade21c9efea7 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-effa279ec44040a89fdbade21c9efea72025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142672269310.1049/rpg2.13113Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithmNoor Habib Khan0Yong Wang1Salman Habib2Raheela Jamal3Muhammad Majid Gulzar4S. M. Muyeen5Mohamed Ebeed6Department of New Energy North China Electric Power University Beijing ChinaDepartment of New Energy North China Electric Power University Beijing ChinaDepartment of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi ArabiaDepartment of New Energy North China Electric Power University Beijing ChinaDepartment of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi ArabiaDepartment of Electrical Engineering Qatar University Doha QatarElectrical Department Faculty of Electrical Engineering Sohag University Sohag EgyptAbstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.https://doi.org/10.1049/rpg2.13113optimisationpower controlrenewable energy sources |
| spellingShingle | Noor Habib Khan Yong Wang Salman Habib Raheela Jamal Muhammad Majid Gulzar S. M. Muyeen Mohamed Ebeed Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm IET Renewable Power Generation optimisation power control renewable energy sources |
| title | Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm |
| title_full | Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm |
| title_fullStr | Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm |
| title_full_unstemmed | Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm |
| title_short | Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm |
| title_sort | stochastic optimal power flow framework with incorporation of wind turbines and solar pvs using improved liver cancer algorithm |
| topic | optimisation power control renewable energy sources |
| url | https://doi.org/10.1049/rpg2.13113 |
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