  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.
