Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems

TL;DR

This paper investigates how to partition power systems effectively to enhance the convergence speed of distributed optimization algorithms for optimal power flow problems, using graph clustering and tie-line analysis.

Contribution

It introduces a partitioning method based on tie-line characteristics and graph clustering to improve convergence in distributed OPF solutions.

Findings

Proper partitioning accelerates convergence.

Improper partitioning increases iteration count.

Graph-based clustering aids in optimal partition selection.

Abstract

The convergence performance of distributed optimization algorithms is of significant importance to solve optimal power flow (OPF) in a distributed fashion. In this paper, we aim to provide some insights on how to partition a power system to achieve a high convergence rate of distributed algorithms for the solution of an OPF problem. We analyzed several features of the power network to find a set of suitable partitions with the aim of convergence performance improvement. We model the grid as a graph and decompose it based on the edge betweenness graph clustering. This technique provides several partitions. To find an effective partitioning, we merge the partitions obtained by clustering technique and analyze them based on characteristics of tie-lines connecting neighboring partitions. The main goal is to find the best set of partitions with respect to the convergence speed. We deploy analytical target cascading (ATC) method to distributedly solve optimization subproblems. We test the proposed algorithm on the IEEE 118-bus system. The results show that the algorithm converges faster with a proper partitioning, whereas improper partitioning leads to a large number of iterations