Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints
Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints
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DOI：
10.1016/j.enconman.2007.08.012
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发表时间：
2008-04
影响因子：
10.4
通讯作者：
S. Pothiya;I. Ngamroo;W. Kongprawechnon
中科院分区：
文献类型：
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作者：
S. Pothiya;I. Ngamroo;W. Kongprawechnon
本文提出了一种基于多重禁忌搜索算法（MTS）的新优化技术，用于解决带发电机约束的动态经济调度（ED）问题。在约束动态ED问题中，考虑了负荷需求和旋转备用容量以及发电机的一些实际运行约束，例如斜坡率限制和禁止运行区。 MTS算法引入了额外的机制，例如初始化、自适应搜索、多重搜索、交叉和重新启动过程。为了显示其效率，MTS 算法被应用于解决 6 和 15 台电力系统的约束动态 ED 问题。将 MTS 算法获得的结果与传统方法（例如模拟退火（SA）、遗传算法（GA）、禁忌搜索（TS）算法和粒子群优化（PSO））获得的结果进行比较。实验结果表明，与传统方法相比，所提出的 MTS 算法能够有效地获得更高质量的解决方案，并且计算时间更少。
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches.