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A Self-Adaptive Differential Artificial Bee Colony for Combined Economic Emission Dispatch of Power System
Ilmi N.
Icocseti 2025 International Conference on Computer Sciences Engineering and Technology Innovation Proceeding
Abstract
The energy sector heavily relies on fossil-fueled thermal power plants, significantly contributing to operational costs and greenhouse gas emissions. Optimizing these costs while reducing emissions remains a critical challenge in modern energy management. Dual-objective optimization methods are often complex due to their conflicting nature. This paper transforms the dual-objectives into a single-objective framework called Combined Economic Emission Dispatch (CEED) to address this issue. This study presents the self-adaptive differential artificial bee colony (sdABC) algorithm as a novel approach to addressing the CEED problem in power systems. The sdABC algorithm enhances the classical artificial bee colony (ABC) method by integrating differential evolution (DE) strategies and self-adaptive mechanisms. These advancements enable the algorithm to adapt dynamically to complex optimization landscapes, thereby ensuring accelerated convergence and improving the efficiency of solution space exploration. Simulations on the IEEE 30-bus test system were conducted to assess the effectiveness of the proposed algorithm, considering transmission losses and operational constraints. The analysis confirms that the sdABC algorithm outperforms the classical ABC algorithm by achieving lower generation costs and reduced emissions.