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Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units

机译:混沌人工免疫方法在利用热力单元经济分配电能中的应用

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摘要

The economic dispatch problem (EDP) is an optimization problem useful in power systems operation. The objective of the EDP of electric power generation, whose characteristics are complex and highly non-linear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying system constraints. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. As special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used as optimization technique in EDPs. Based on the chaos theory, this paper discusses the design and validation of an optimization procedure based on a chaotic artificial immune network approach based on Zaslavsky’s map. The optimization approach based on chaotic artificial immune network is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results and comparisons show that the chaotic artificial immune network approach is competitive in performance with other optimization approaches presented in literature and is also an attractive tool to be used on applications in the power systems field.
机译:经济调度问题(EDP)是在电力系统运行中有用的优化问题。具有复杂性和高度非线性的发电EDP的目标是调度承诺的发电机组输出,以便以最小的运行成本满足所需的负载需求,同时满足系统约束。近年来,作为传统数学方法的替代方法,现代启发式优化技术由于能够在EDP中找到几乎全局的最优解而备受关注。作为避免陷入局部极小值的特殊机制,混沌序列的遍历性已被用作EDP中的优化技术。基于混沌理论,本文讨论了基于Zaslavsky映射的基于混沌人工免疫网络方法的优化程序的设计和验证。基于混沌人工免疫网络的优化方法已针对包含13个热力单元的测试系统进行了验证,该热力单元的增量燃料成本函数考虑了阀点负载效应。仿真结果和比较结果表明,混沌人工免疫网络方法在性能上与文献中介绍的其他优化方法相比具有竞争优势,并且是在电力系统领域中使用的有吸引力的工具。

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