Abstract
This chapter presents a population-based meta-heuristic called teaching-learning-based optimization (TLBO) for the solution of economic scheduling of power generators. The mathematical model of TLBO basically simulates the interaction of the teacher with students in a classroom during the optimization process. To demonstrate the applicability and validity of TLBO, it has been implemented and tested on two different types of complex constrained economic dispatch problems that include test cases of distinct nature. To confirm the superiority and efficacy of TLBO over the existing approaches in terms of optimal search behavior and robustness, the outcomes of simulation results are also compared with other recent reported methods.
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Acknowledgements
The authors acknowledge the financial support provided by AICTE-RPS project File No. 8-36/RIFD/RPS/POLICY-1/2016-17 dated 2.9.2017 and TEQIP III. The authors also thank the director and management of M.I.T.S. Gwalior, India, for providing facilities for carrying out this work.
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Sharma, K., Dubey, H.M., Pandit, M. (2020). Teaching-Learning-Based Optimization for Static and Dynamic Load Dispatch. In: Pandit, M., Dubey, H., Bansal, J. (eds) Nature Inspired Optimization for Electrical Power System. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4004-2_1
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DOI: https://doi.org/10.1007/978-981-15-4004-2_1
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