As significant amounts of renewable distributed generation (RDG) are installed in the power grid, it becomes increasingly important to plan RDG integration to maximize the utilization of renewable energy and mitigate unintended consequences, such as phase unbalance. One of the biggest challenges in RDG integration planning is the lack of sufficient information to characterize uncertainty (e.g., load and renewable output). In this paper, we propose a two-stage data-driven distributionally robust optimization model (O-DDSP) for the optimal placement of RDG resources, with both load and generation uncertainties described by a data-driven ambiguity set that both enables more flexibility than stochastic optimization (SO) and allows less conservative solutions than robust optimization (RO). The objective is to minimize the total cost of RDG installation plus the total operational cost on the planning horizon. Furthermore, we introduce a tight approximation of O-DDSP based on principal component analysis (leading to a model called P-DDSP), which reduces the original problem size by keeping the most valuable data in the ambiguity set. The performance of O-DDSP and P-DDSP is compared with SO and RO on the IEEE 33-bus radial network with a real data set, where we show that P-DDSP significantly speeds up the solution procedure, especially when the problem size increases. Indeed, as compared to SO and RO, which become computationally impractical for solving problems with large sample sizes, our proposed P-DDSP can use large samples to increase solution accuracy without increasing the solution time. Finally, extensive numerical experiments demonstrate that optimal RDG planning decisions lead to significant savings as well as increased renewable penetration.

中文翻译：

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配电系统中可再生分布式发电的数据驱动规划

随着大量可再生分布式发电 (RDG) 安装在电网中，规划 RDG 整合以最大限度地利用可再生能源并减轻意外后果（例如相位不平衡）变得越来越重要。RDG 整合规划的最大挑战之一是缺乏足够的信息来表征不确定性（例如，负载和可再生能源输出）。在本文中，我们提出了一个两阶段数据驱动的分布鲁棒优化模型（O-DDSP），用于 RDG 资源的最佳放置，负载和发电的不确定性由数据驱动的模糊集描述，这两者都比随机优化 (SO) 并允许比稳健优化 (RO) 更少保守的解决方案。目标是最大限度地减少 RDG 安装的总成本加上规划范围内的总运营成本。此外，我们引入了基于主成分分析的 O-DDSP 的紧密近似（导致称为 P-DDSP 的模型），它通过将最有价值的数据保留在歧义集中来减少原始问题的规模。将 O-DDSP 和 P-DDSP 的性能与 IEEE 33 总线径向网络上的 SO 和 RO 的性能进行比较，并使用真实数据集，我们表明 P-DDSP 显着加快了求解过程，尤其是当问题规模增加时. 事实上，与 SO 和 RO 相比，在解决大样本问题时在计算上变得不切实际，我们提出的 P-DDSP 可以使用大样本来提高求解精度，而不会增加求解时间。最后，