Economic Dispatch of 500 kV Java-Bali Power System using Hybrid Particle Swarm-Ant Colony Optimization Method

H. Suyono, E. Subekti, Hery Purnomo, Tri Nurwati, R. Hasanah
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引用次数: 3

Abstract

The rapid growth of population and economic development of a country requires an adequate support of electrical power supply management. The interconnected system of generation plants with appropriate economic dispatch is purposed to achieve certain goal. This paper describes the use of a hybrid method between the Particle Swarm Optimization (PSO) method and Ant Colony Optimization (ACO) method to be implemented for economic dispatch of the 500kV Java-Bali power system. It aims to divide the generation loading among the whole thermal power plants in the system and to look for the best combination which gives the most economical generation cost. The search for solutions using this hybrid method is determined by the Gbest’s particle distribution and the ability of ants to find the best solution, which is called BestAnt. In this study, the evaluation process was carried out using 60 iterations for the 30-bus network and the 500kV Java-Bali power network based on the available data. The optimization results show that the generation cost being optimized using the hybrid method is lower than when using the PSO method, even if it is still higher than when using the ACO method. However, the hybrid method offers the best achievement in terms of computation speed being compared to both the PSO and ACO methods.
基于粒子群-蚁群混合优化方法的500kv爪哇-巴厘电力系统经济调度
一个国家人口的快速增长和经济的快速发展,需要电力供应管理的充分支持。合理经济调度的发电机组互联系统是为了实现一定的目标。本文介绍了将粒子群算法(PSO)与蚁群算法(ACO)相结合的方法应用于500kV爪哇-巴厘电力系统的经济调度。其目的是对系统中各火电厂的发电负荷进行划分,寻找发电成本最经济的最佳组合。使用这种混合方法寻找解决方案取决于Gbest的粒子分布和蚂蚁找到最佳解决方案的能力,即BestAnt。在本研究中,基于现有数据,对30母线网络和500kV Java-Bali电网进行了60次迭代的评估过程。优化结果表明,混合方法优化后的发电成本虽然仍高于蚁群算法,但低于粒子群算法优化后的发电成本。然而,与粒子群算法和蚁群算法相比,混合算法在计算速度方面取得了最好的成绩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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