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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...

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Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Muhammad Ikram, Mohd Rashid
Format: Article
Language:English
Published: Elsevier B.V. 2023
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/37622/1/An%20application%20of%20teaching%E2%80%93learning-based%20optimization%20for%20solving%20the%20optimal%20power%20flow%20problem%20with%20stochastic.pdf
http://umpir.ump.edu.my/id/eprint/37622/
https://doi.org/10.1016/j.rico.2022.100187
https://doi.org/10.1016/j.rico.2022.100187
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spelling my.ump.umpir.376222023-07-13T03:36:15Z http://umpir.ump.edu.my/id/eprint/37622/ An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators Mohd Herwan, Sulaiman Zuriani, Mustaffa Muhammad Ikram, Mohd Rashid T Technology (General) TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier B.V. 2023-03 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/37622/1/An%20application%20of%20teaching%E2%80%93learning-based%20optimization%20for%20solving%20the%20optimal%20power%20flow%20problem%20with%20stochastic.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa and Muhammad Ikram, Mohd Rashid (2023) 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, 10 (100187). pp. 1-13. ISSN 2666-7207. (Published) https://doi.org/10.1016/j.rico.2022.100187 https://doi.org/10.1016/j.rico.2022.100187
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Article
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
title 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_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_sort application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators
publisher Elsevier B.V.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/37622/1/An%20application%20of%20teaching%E2%80%93learning-based%20optimization%20for%20solving%20the%20optimal%20power%20flow%20problem%20with%20stochastic.pdf
http://umpir.ump.edu.my/id/eprint/37622/
https://doi.org/10.1016/j.rico.2022.100187
https://doi.org/10.1016/j.rico.2022.100187
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score 13.211869

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