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Optimal location-allocation of storage devices and renewable-based DG in distribution systems

机译：配电系统中存储设备和基于可再生能源的DG的最佳位置分配

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摘要

This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.
机译：本文提出了一种混合整数圆锥规划（MICP）模型，以在径向配电系统的多级规划范围内找到分布式发电机（DG）的最佳类型，大小和位置。拟议的计划框架着重于风力涡轮机，光伏面板，燃气轮机和能量存储设备（ESD）的最佳选址和规模。本质上，可再生能源和电力需求存在不确定性。为了处理决策中的这种概率情况，将MICP模型扩展为两阶段随机规划模型。为了获得更多实际结果，使用年度历史数据来生成方案。为了便于处理，k-means聚类技术用于减少场景数量，同时保持不确定数据之间的相关性。由于凸性，建议的MICP模型保证找到全局最优解。为了显示该模型的潜力和性能，对69总线径向分布系统在不同条件下进行了深入研究，并进行了灵敏度分析。结果和比较证明其有效性和实用性。

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