A Decomposition Method for Solving Sensitivity-Based Distributed Optimal Power Flow

TL;DR

This paper presents a novel distributed optimization method for large-scale power systems that uses sensitivity-based formulations and ADMM, achieving significantly faster computation speeds than traditional methods.

Contribution

Introduces a sensitivity-based decomposition method for distributed optimal power flow using ADMM, with a new distributed sensitivity computation approach that enhances scalability and efficiency.

Findings

14-times faster computation speed compared to phase-angle formulation

Effective scalability demonstrated on large test systems

Reduced data sharing while satisfying global constraints

Abstract

Efficiently solving large-scale optimal power flow (OPF) problems is challenging due to the high dimensionality and interconnectivity of modern power systems. Decomposition methods offer a promising solution via partitioning large problems into smaller subproblems that can be solved in parallel, often with local information. These approaches reduce computational burden and improve flexibility by allowing agents to manage their local models. This article introduces a decomposition method that enables a distributed solution to OPF problems. The proposed method solves OPF problems with a sensitivity-based formulation using the alternating direction method of multipliers (ADMM) algorithm. We also propose a distributed method to compute system-wide sensitivities without sharing local parameters. This approach facilitates scalable optimization while satisfying global constraints and limiting data sharing. We demonstrate the effectiveness of the proposed approach using a large set of test systems and compare its performance against existing decomposition methods. The results show that the proposed method significantly outperforms the typical phase-angle formulation with a 14-times faster computation speed on average.