The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method

This paper is concerned with the economic generation dispatch problem. It is a well-known fact that practical aspects of power plant equipment, as well as the objectives to be met, may result in a nonconvex, nondifferentiable model that poses difficulties to conventional mathematical programming methods. This paper proposes the use of metaheuristic Teaching-Learning-Based Optimization to overcome such difficulties. This metaheuristic is well known for requiring a few parameters and, most importantly, it does not require the tuning of problem-dependent parameters. The algorithm proposed in this work is parameter-free; that is, the few parameters required by the Teaching-Learning-Based Optimization method are set automatically based on the power system’s data. In addition, the handling of constraints, such as generators’ prohibited zones and the generator-load-loss power balance, is performed in a very efficient way. Simulation results are shown for power systems containing 3 to 40 generation units, and the results provided by the proposed method are shown and discussed based on comparisons with other metaheuristics and a mathematical programming technique.

• Autores: Castro, Carlos; Silva, Fernanda L.
• Tipo de recurso: Article of journal
• Fecha de publicación: 2023
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/13510
• Acceso en línea: https://hdl.handle.net/20.500.12585/13510; https://doi.org/10.32397/tesea.vol4.n1.510
• Palabra clave: Economic Dispatch Problem; Power Generation Optimization; Teaching-Learning-Based Optimization; Metaheuristic Algorithms; Nonconvex Model; Parameter-Free Algorithm; Power System Constraints; Power Systems Simulation
• Rights: openAccess
• License: Carlos Castro, Fernanda L. Silva - 2023