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Fast technique for unit commitment by genetic algorithm based on unit clustering

机译:基于单元聚类的遗传算法快速单元承诺技术

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The paper presents a new approach to the large-scale unit-commitment problem. To reduce computation time and to satisfy the minimum up/down-time constraint easily, a group of units having analogous characteristics is clustered. Then, this 'clustered compress' problem is solved by means of a genetic algorithm. Besides, problem-oriented powerful tools such as relaxed-pruned ELD, intelligent mutation, shift operator etc. make the proposed approach more effective with respect to both cost and execution time. The proposed algorithm is tested using the reported problem data set. Simulation results for systems of up to 100-unit are compared with previous reported results. Numerical results show an improvement in the solution cost compared with the results obtained from a genetic algorithm with standard operations.
机译:本文提出了一种解决大规模单位承诺问题的新方法。为了减少计算时间并容易地满足最小的上/下时间限制,将具有相似特性的一组单元聚类。然后,通过遗传算法解决这个“簇压缩”问题。此外,面向问题的强大工具(如轻松修剪的ELD,智能变异,移位运算符等)使该方法在成本和执行时间方面更加有效。使用报告的问题数据集对提出的算法进行了测试。将最多100个单元的系统的仿真结果与以前报告的结果进行比较。数值结果表明,与具有标准操作的遗传算法相比,解决方案的成本有所提高。

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