Multi-area economic dispatch using an improved stochastic fractal search algorithm
Multi-area economic dispatch using an improved stochastic fractal search algorithm
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DOI：
10.1016/j.energy.2018.10.065
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发表时间：
2019
期刊：
影响因子：
9
通讯作者：
Jian Lin;Zhou-Jing Wang
中科院分区：
文献类型：
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作者：
Jian Lin;Zhou-Jing Wang
多区域经济调度具有高度非凸性和非线性的特点，是电力系统运行中的一个重要问题。本文提出了一种改进的随机分形搜索（ISFS），以解决MAED问题考虑区域负荷需求，联络线的限制和各种操作约束。为了平衡探索和开发，ISFS引入了一种基于反对的学习方法来进行种群初始化和世代跳跃。通过与差分进化策略相结合，提出了一种混合扩散过程，并将其作为局部搜索技术，以提高算法的开发能力。此外，一种新的修复为基础的惩罚方法，并纳入ISFS找到可行的解决方案更有效。ISFS的有效性和鲁棒性的几个测试系统组成的16-120发电机组进行了评估。计算结果表明，所提出的ISFS计划的国家的最先进的算法的优越性。
Multi-area economic dispatch (MAED) characterized by high non-convexity and non-linearity is an important issue in power system operation. This paper presents an improved stochastic fractal search (ISFS) to solve the MAED problem considering the area load demands, the tie-line limits and various operating constraints. To balance exploration and exploitation, the ISFS introduces an opposition-based learning method for population initialization as well as for generation jumping. By combining with the differential evolution strategy, a hybrid diffusion process is developed and used as the local search technique to enhance the exploitation ability. Furthermore, a novel repair-based penalty approach is presented and incorporated into the ISFS to find feasible solutions more efficiently. The effectiveness and robustness of the ISFS is evaluated on several test systems consisting of 16–120 generating units. Computational results demonstrate the superiority of the proposed ISFS scheme over the state-of-the-art algorithms.