An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators
This paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. TLBO is inspired by philosophy of teaching and learning in the classroom. OPF on the other hand, is one of the most complex probl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720722000595 |
| _version_ | 1811157855030476800 |
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| author | Mohd Herwan Sulaiman Zuriani Mustaffa Muhammad Ikram Mohd Rashid |
| author_facet | Mohd Herwan Sulaiman Zuriani Mustaffa Muhammad Ikram Mohd Rashid |
| author_sort | Mohd Herwan Sulaiman |
| collection | DOAJ |
| description | This paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. TLBO is inspired by philosophy of teaching and learning in the classroom. OPF on the other hand, is one of the most complex problems in power system operation, where in this paper, two objective functions aimed to be minimized by TLBO namely cost minimization and combined cost and emission (CEE) minimization. The effectiveness of proposed TLBO in solving the OPF is tested on modified IEEE-57 bus system that integrated with stochastic wind and solar power generations. To show the effectiveness of the proposed TLBO, several recent algorithms that have been proposed in literature will be utilized and compared. The simulations demonstrate the superiority of TLBO as an effective alternative solution for the OPF problems, where for the cost minimization, TLBO able to obtained 0.16% cost saving per hour compared to the second best algorithm; and for the CEE minimization, TLBO outperformed the second best algorithm by 0.12% cost saving per hour. |
| first_indexed | 2024-04-10T05:14:28Z |
| format | Article |
| id | doaj.art-1168b8c3d529471e9f7bd39ee7706327 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-04-10T05:14:28Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-1168b8c3d529471e9f7bd39ee77063272023-03-09T04:13:49ZengElsevierResults in Control and Optimization2666-72072023-03-0110100187An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generatorsMohd Herwan Sulaiman0Zuriani Mustaffa1Muhammad Ikram Mohd Rashid2Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia; Corresponding author.Faculty of Computing, Universiti Malaysia Pahang, 26600 Pekan Pahang, MalaysiaFaculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, MalaysiaThis paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. TLBO is inspired by philosophy of teaching and learning in the classroom. OPF on the other hand, is one of the most complex problems in power system operation, where in this paper, two objective functions aimed to be minimized by TLBO namely cost minimization and combined cost and emission (CEE) minimization. The effectiveness of proposed TLBO in solving the OPF is tested on modified IEEE-57 bus system that integrated with stochastic wind and solar power generations. To show the effectiveness of the proposed TLBO, several recent algorithms that have been proposed in literature will be utilized and compared. The simulations demonstrate the superiority of TLBO as an effective alternative solution for the OPF problems, where for the cost minimization, TLBO able to obtained 0.16% cost saving per hour compared to the second best algorithm; and for the CEE minimization, TLBO outperformed the second best algorithm by 0.12% cost saving per hour.http://www.sciencedirect.com/science/article/pii/S2666720722000595Cost and emission minimizationsMetaheuristic algorithmsOptimal power flowTeaching–learning based optimizationStochastic power generations |
| spellingShingle | Mohd Herwan Sulaiman Zuriani Mustaffa Muhammad Ikram Mohd Rashid An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators Results in Control and Optimization Cost and emission minimizations Metaheuristic algorithms Optimal power flow Teaching–learning based optimization Stochastic power generations |
| title | An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| title_full | An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| title_fullStr | An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| title_full_unstemmed | An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| title_short | An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| title_sort | application of teaching learning based optimization for solving the optimal power flow problem with stochastic wind and solar power generators |
| topic | Cost and emission minimizations Metaheuristic algorithms Optimal power flow Teaching–learning based optimization Stochastic power generations |
| url | http://www.sciencedirect.com/science/article/pii/S2666720722000595 |
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