Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part II: Case Studies
Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part II: Case Studies
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DOI：
10.1109/tpwrs.2018.2878380
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发表时间：
2018-04
影响因子：
6.6
通讯作者：
Yi Guo;K. Baker;E. Dall’Anese;Zechun Hu;T. Summers
中科院分区：
文献类型：
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作者：
Yi Guo;K. Baker;E. Dall’Anese;Zechun Hu;T. Summers
这是基于数据的分布鲁棒随机最优潮流的两部分文件的第二部分。一般的问题公式和方法已在第一部分（Y）中介绍。Guo，K.贝克，E. Dall'Anese，Z. Hu和T.H.李文生，“基于数据的分布式鲁棒随机最优潮流-第一部分：方法学”，电力系统工程学报，2018年）。在这里，我们提出了广泛的数值实验在配电和输电网络，以说明所提出的方法的有效性和灵活性，平衡效率，约束违反风险，和样本外的性能。在配电侧，该方法使用本地能量存储装置减轻由于高光伏穿透而引起的过电压。在传输侧，该方法降低了由于使用可控发电机的备用策略的高风力穿透而导致的$N-1$安全线路流约束风险。在这两种情况下，基于数据的分布式鲁棒模型预测控制算法显式地利用预测误差训练数据集，这些数据集可以在线更新。数值结果说明了固有的运营成本之间的权衡，违反约束的风险，和样本外的性能，系统运营商提供系统的技术，以平衡这些目标。
This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow. The general problem formulation and methodology have been presented in Part I (Y. Guo, K. Baker, E. Dall’Anese, Z. Hu, and T.H. Summers, “Data-based distributionally robust stochastic optimal power flow—Part I: Methodologies,” IEEE Trans. Power Syst., 2018.). Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance. On the distribution side, the method mitigates overvoltages due to high photovoltaic penetration using local energy storage devices. On the transmission side, the method reduces $N-1$ security line flow constraint risks due to high wind penetration using reserve policies for controllable generators. In both cases, the data-based distributionally robust model-predictive control algorithm explicitly utilizes forecast error training datasets, which can be updated online. The numerical results illustrate inherent tradeoffs between the operational costs, risks of constraints violations, and out-of-sample performance, offering systematic techniques for system operators to balance these objectives.