Decentralized Stability-Constrained Optimal Power Flow for Inverter-Based Power Systems

TL;DR

This paper introduces a decentralized stability-constrained optimal power flow framework for inverter-based power systems, using algebraic stability criteria that are computationally efficient and economically interpretable.

Contribution

It presents the first decentralized stability-constrained OPF method incorporating algebraic stability criteria suitable for optimization in inverter-based systems.

Findings

Decentralized stability constraints can be integrated into OPF using local voltage differences.

Stability constraints can have positive shadow prices when reactive power costs are included.

Binding stability constraints may occur with active-power-only objectives but have zero shadow prices if other constraints are inactive.

Abstract

Future inverter-dominated power systems feature higher variability and more stressed operating conditions, which motivates the consideration of stability in operational settings. Existing approaches to stability-constrained OPF often rely on eigenvalue calculation, global model information, or dynamic evaluation inside optimization formulation, which are computationally intensive and difficult to scale. This paper proposes the first decentralized stability-constrained OPF framework for inverter-based power systems. The key novelty lies in the incorporation of a class of algebraic decentralized small-signal stability criteria that admits tractable representations in steady-state variables and is therefore suitable for optimization. The decentralized stability condition is based on local voltage differences and enables clear theoretical and practical economic interpretation of the stability contribution from each inverter. We define a Nodal Stability Shadow Price (NSSP) for each inverter, and characterize the role of these stability constraints through their associated shadow prices, enabling a nodal interpretation of their economic impacts. It is proved that under active-power-only objectives in lossless networks, binding stability constraints may occur but will admit zero shadow prices if all other operational constraints are inactive. Most importantly, we reveal the importance of considering the opportunity cost of reactive power for inverter-based resources (IBRs) that have limited capacity. When reactive power costs are considered, stability constraints can carry strictly positive shadow prices and admit meaningful economic impacts.