Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
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DOI：
10.1109/tsg.2019.2935711
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发表时间：
2018-06
影响因子：
9.6
通讯作者：
Roel Dobbe;O. Sondermeijer;David Fridovich-Keil;D. Arnold;Duncan S. Callaway;C. Tomlin
中科院分区：
文献类型：
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作者：
Roel Dobbe;O. Sondermeijer;David Fridovich-Keil;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广泛的通信。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。速率失真框架能够分析得到的完全分散控制策略对OPF解决方案的重构效果。该方法提供了一个自然的扩展，以决定DER应该与哪些节点通信，以改进其单个策略的重建。该方法应用于单相和三相测试馈线网络，使用来自实际负载和分布式发电机的数据，重点关注不表现出跨时间依赖性的der。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电源的贡献，以实现配电网络中的分布式能源服务。
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive 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 enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes 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, focusing on DERs that do not exhibit intertemporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.