Date of Publication :4th March 2024

Abstract:This paper presents a comprehensive review of the application of metaheuristic techniques to address economic emission dispatch (EED) problems in power systems incorporating renewable energy sources (RES). The EED problem involves optimizing the power generation schedule of thermal units while considering the economic cost and emissions. With the increasing integration of renewable energy sources, the EED problem becomes more complex due to the intermittent and uncertain nature of these sources. Traditional optimization methods may struggle to provide efficient solutions for such dynamic and uncertain systems. To overcome these challenges, researchers have turned to metaheuristic techniques. Metaheuristic algorithms, such as genetic algorithms, particle swarm optimization, simulated annealing, etc. have shown promising results in solving complex optimization problems. These algorithms are well-suited for handling the non-linear and non-convex nature of EED problems that incorporate RES. In addition, these techniques provide near-optimal solutions while considering the economic cost and emissions. They offer flexibility, scalability, and robustness in handling the complexities introduced by RES integration. The findings of this review contribute to the understanding and advancement of metaheuristic techniques in the field of EED with renewable energy sources. The research highlights the potential of these techniques to support the development of sustainable and efficient energy management strategies, facilitating the integration of renewable energy sources into power systems.

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