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首页> 外文期刊>Quality Control, Transactions >An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty
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An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty

机译：一种提高向日葵优化算法的蒙特卡罗模拟，用于考虑风电不确定性的径向分布系统效率改进

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摘要

All over the world, the operators of the power distribution networks (DNs) are still looking for improving the efficiency of their networks. The performance of DNs and lifetime of its component have been significantly affected by its capability of varying their topologies with accurate load gathering via smart grid functions. This paper investigates making use of the smart DNs features and proposes a model of handling the capability of re-allocating the capacitors integrating with configuring the DNs topology. Using the developed formulation, the efficiency of DNs can be improved not only by minimizing the operational costs related to the network losses but also by optimizing the investment costs associated with capacitor re-allocations. Also, various load patterns are employed in the developed formulation to imitate the daily load variations over a year. The improved sunflower optimization algorithm (ISFOA) is proposed in this paper to get the optimal solution of the presented problem. The standard IEEE 33-node feeder and practical 84-node system of Taiwan Power Company (TPC) are the considered test systems. Besides, the uncertainties due to a distributed generation of wind power are investigated via Monte Carlo simulation involved with the proposed ISFOA. Furthermore, to verify the ability of ISFOA to obtain better solutions compared with different recent optimizers, a statistical comparison is carried out based on a large scale 118-node distribution systems. The simulation results reveal that significant technical and economic benefits are obtained by applying the proposed algorithm with higher superiority and effectiveness.
机译：在世界各地，配电网络（DNS）的运营商仍在寻找提高其网络的效率。其组件的DNS和寿命的性能受到通过其改变其拓扑的能力，通过智能电网功能的准确负载收集的能力来显着影响。本文调查了利用智能DNS功能，并提出了处理重新分配与配置DNS拓扑集成的电容器的能力的模型。使用开发的配方，不仅可以通过最小化与网络损耗相关的操作成本，而且还可以通过优化与电容器重新分配相关的投资成本来提高DNS的效率。此外，在开发的配方中采用各种负载模式以模仿一年的日常负荷变化。在本文中提出了改进的向日葵优化算法（ISFOA）以获得所呈现的问题的最佳解决方案。台湾电力公司（TPC）的标准IEEE 33节点馈线和实用的84节点系统是考虑的测试系统。此外，通过与所提出的ISFOA涉及的蒙特卡罗模拟来研究引起的风力发电引起的不确定性。此外，为了验证ISFOA获得更好的解决方案的能力与不同的最近优化器相比，基于大规模118节点分配系统进行统计比较。仿真结果表明，通过应用具有更高优势和有效性的提出算法来获得显着的技术和经济效益。

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