Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

TL;DR

This paper introduces a new decomposable DC power flow formulation using sparsified injection shift factors, enabling scalable and efficient decentralized optimization for large power systems with security constraints.

Contribution

It presents a novel sparse, block-diagonal DC power flow model that improves scalability and efficiency in decentralized power system optimization.

Findings

Successfully solved large-scale problems with over 6,500 buses.

Demonstrated reliable convergence and numerical stability.

Enhanced suitability for decentralized security-constrained unit commitment.

Abstract

One of the most complicating factors in decentralized optimization for power systems is the modeling of power flow equations. Existing formulations for DC power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. In this work, we present a novel DC power flow formulation, based on sparsified injection shift factors, which has a decomposable block-diagonal structure, scales well for large systems, and can efficiently handle N-1 security requirements. Benchmarks on Multi-Zonal Security-Constrained Unit Commitment problems show that the proposed formulation can reliably and efficiently solve instances with up to 6,515 buses, with no convergence or numerical issues.