Efficient optimization of static economic load dispatch in electrical power systems using the teaching–learning based optimization algorithm

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Chinese explanation / 中文解读

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Original abstract

Introduction. Power grids are considered one of the most critical energy infrastructures in modern societies, and their economic exploitation plays an important role in reducing the costs of generating electrical energy and increasing the efficiency of generation systems. In the meantime, power generation plants are responsible for providing the required power to the grid, and the optimal distribution of power among them has a direct impact on the final cost of energy generation. For this reason, the economic load dispatch (ELD) problem has been raised as one of the fundamental issues in the optimal operation of power systems. Problem. The static economic load dispatch problem is defined with the aim of determining the amount of power generated by each generator unit in such a way that the total cost of energy generation is minimized, while all operational constraints of the power system, including power balance constraints, transmission network losses, generator production constraints, power rate of change constraints, and prohibited areas, are met. The presence of features such as nonlinear cost function, valve-point effect and nonconvex search space makes solving this problem with classical mathematical methods face serious challenges. Goal. To develop an efficient method for solving the ELD problem and to achieve an optimal production schedule for power system generators with minimum production cost. Methodology. In this study, the metaheuristic algorithm teaching–learning based optimization (TLBO) has been used to solve the ELD problem. The performance evaluation of the algorithm has been carried out on a standard 6-unit power system. Results. The optimization results show that the TLBO algorithm is able to provide an optimal production schedule by observing all system constraints, in which the total production cost reaches $15452.06. To evaluate the performance quality, the results of TLBO were compared with seven well-known metaheuristic algorithms, and the simulation results showed that TLBO provided the best performance by achieving first rank in terms of objective function value, average cost, and performance stability. Scientific novelty. The innovation of this research lies in the effective application of the TLBO algorithm to solve the ELD problem by considering a complete set of operational constraints and providing a comprehensive comparative analysis with several metaheuristic algorithms. Practical value. The findings of this study indicate that the TLBO algorithm can be used as an efficient, stable, and reliable method for solving operation optimization problems in power systems and help reduce the cost of energy generation and increase the economic efficiency of power grids. References 29, tables 4, figures 2.

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