Data-Driven Decentralized Optimal Power Flow
Data-Driven Decentralized Optimal Power Flow
复制标题
数据驱动的分散式最优潮流
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Tomlin
中科院分区：
文献类型：
--
作者：
Roel Dobbe;O. Sondermeijer;David Fridovich;D. Arnold;Duncan S. Callaway;C. Tomlin
在电网中实施最优潮流（OPF）方法来进行电压和潮流调节通常被认为需要通信。我们考虑了具有多个可控分布式能源（DER）的配电系统，并提出了一种数据驱动的方法来学习每个DER的控制策略，以便仅从本地可用信息重构和模拟集中式OPF问题的解决方案。总的来说，所有本地控制器都与集中式OPF解决方案紧密匹配，提供接近最优的性能并满足系统约束。速率扭曲框架有助于分析所得到的完全分散控制策略对OPF解决方案的重构效果。我们的方法提供了一个自然的扩展，以决定DER应该与哪些总线通信，以改进其单个策略的重建。利用实际负荷和分布式发电机的数据，将该方法应用于单相和三相测试馈线网络。它为配电系统运营商提供了一个框架，以有效地规划和操作分布式配电系统对主动配电网络的贡献。
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework facilitates the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. Our methodology provides a natural extension to decide what buses a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to active distribution networks.
影响因子：
6.6
作者：
Weixuan Lin;E. Bitar
通讯作者：
Weixuan Lin;E. Bitar