Radial Partitioning with Spectral Penalty Parameter Selection in Distributed Optimization for Power Systems

Abstract

This paper proposes group-based distributed optimization (DO) algorithms on top of intelligent partitioning for the optimal power flow (OPF) problems. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. Spectral parameter selection is introduced for group-based DO as a crucial hyper-parameter selection step in DO. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package. Our numerical results for different power network instances show that our designed algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based DO. Our results confirm the importance of smart partitioning and parameter selection for large-scale optimization problems on networks.