Optimization of Reconfigurable Islanded Microgrids using Random Forest Classifier

Nestor Vazquez, Manou Rosenberg, Tat Kei Chau, Xinan Zhang, T. Fernando, Herbert Ho Ching Iu
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引用次数: 1

Abstract

In this paper, a classifier is developed as an approach to find the optimal configuration of islanded microgrids. In islanded microgrids with high penetration of renewable sources, the power generation may be intermittent and unpredictable. Moreover, even when forecast information is available, the non-dispatchable nature of these generation units further limits the control variables needed to formulate and address an optimization problem. In this regard, reconfigurable microgrids allow controlled changes in the grid topology to redirect and redistribute the power flow, in order to optimize and/or improve the system resiliency. In these scenarios, the optimization variables are the binary status (closed/open) of the controllable switches, which makes the problem particularly suitable to be addressed by decision classification trees. In this study, the optimization objective is power loss minimization, subject to the system constraints of power flow and supply/demand balance. Initially, a decision tree classifier is introduced and tested on a simple 9bus islanded system, to identify and categorize different generation and loading level profiles of the system and learn from them the optimal configurations. After that, a random forest classifier is designed as an ensemble of decision trees with enhanced capabilities. A time-series learning component is also implemented to boost the time-related learning characteristics of the classifier, such as trend and seasonality, which are inherent to the power generation levels of renewable energy sources. The proposed random forest classifier is tested on the modified IEEE 33bus islanded microgrid test system. Simulation results show the random forest classifier, when sufficiently trained, is able to find the optimal configuration of the microgrid to any new generation and loading profile.
基于随机森林分类器的可重构孤岛微电网优化
本文开发了一种分类器来寻找孤岛微电网的最优配置。在可再生能源普及率高的孤岛微电网中,发电可能是间歇性的和不可预测的。此外,即使有预测信息,这些发电机组的不可调度特性也进一步限制了制定和解决优化问题所需的控制变量。在这方面,可重构微电网允许电网拓扑结构的可控变化来重新定向和重新分配功率流,以优化和/或提高系统的弹性。在这些场景中,优化变量是可控开关的二进制状态(关闭/打开),这使得问题特别适合用决策分类树来解决。本研究的优化目标是在潮流和供需平衡的约束下,使系统的功率损耗最小化。首先,介绍了决策树分类器,并在一个简单的9总线孤岛系统上进行了测试,以识别和分类系统的不同发电和负载水平概况,并从中学习最优配置。然后,将随机森林分类器设计为具有增强功能的决策树集合。还实现了一个时间序列学习组件,以增强分类器的时间相关学习特征,例如趋势和季节性,这些特征是可再生能源发电水平固有的。在改进的IEEE 33bus孤岛微电网测试系统上对所提出的随机森林分类器进行了测试。仿真结果表明,随机森林分类器在经过充分训练后,能够找到适合任何新一代和负荷的最优微电网配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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