An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators

• 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

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.