Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
This work addresses the challenge of decentralized energy management for distribution systems, offering a practical solution for operators, though it is incremental as it builds on existing OPF and machine learning approaches.
The paper tackles the problem of implementing optimal power flow (OPF) in electric networks without extensive communication by learning decentralized control policies for Distributed Energy Resources (DERs) using locally available information, achieving near-optimal performance that closely matches centralized solutions. It applies this method to test feeder networks with real data, providing a framework for Distribution System Operators to efficiently manage DERs.
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 inter-temporal 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.