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Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
IEEE Transactions on Smart Grid ( IF 9.6 ) Pub Date : 2019-08-15 , DOI: 10.1109/tsg.2019.2935711
Roel Dobbe , Oscar Sondermeijer , David Fridovich-Keil , Daniel Arnold , Duncan Callaway , Claire Tomlin

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.

中文翻译:

迈向分布式能源服务:通过机器学习分散最佳功率流

通常认为,在电网中执行最佳功率流(OPF)方法以执行电压和功率流调节需要大量的交流。我们考虑具有多个可控分布式能源(DER)的配电系统,并提出一种数据驱动的方法来学习每个DER的控制策略,以仅从本地可用信息中重建和模仿集中式OPF问题的解决方案。总而言之,所有本地控制器都与集中式OPF解决方案紧密匹配,从而提供了近乎最佳的性能并满足了系统约束。速率失真框架可以分析所得的完全分散控制策略能够重构OPF解决方案的能力。该方法提供了自然的扩展,可以确定DER应该与哪些节点通信以改善其单个策略的重建。该方法适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,重点是不表现出时间依赖性的DER。它为配电系统运营商提供了一个框架,可有效规划和运营DER的贡献,以实现配电网络中的分布式能源服务。
更新日期:2020-04-22
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