Sparsity-Promoting Optimal Wide-Area Control of Power Networks

TL;DR

This paper introduces a sparsity-promoting optimal control method for power networks that identifies minimal communication structures while maintaining near-optimal performance and robustness.

Contribution

It combines sparsity-promoting regularization with slow coherency objectives to design efficient wide-area control architectures in power systems.

Findings

Achieves near-optimal control performance with minimal communication links.

Demonstrates robustness margins comparable to centralized control.

Identifies sparse control architectures effectively using the proposed method.

Abstract

Inter-area oscillations in bulk power systems are typically poorly controllable by means of local decentralized control. Recent research efforts have been aimed at developing wide- area control strategies that involve communication of remote signals. In conventional wide-area control, the control structure is fixed a priori typically based on modal criteria. In contrast, here we employ the recently-introduced paradigm of sparsity- promoting optimal control to simultaneously identify the optimal control structure and optimize the closed-loop performance. To induce a sparse control architecture, we regularize the standard quadratic performance index with an l1-penalty on the feedback matrix. The quadratic objective functions are inspired by the classic slow coherency theory and are aimed at imitating homogeneous networks without inter-area oscillations. We use the New England power grid model to demonstrate that the proposed combination of the sparsity-promoting control design with the slow coherency objectives performs almost as well as the optimal centralized control while only making use of a single wide-area communication link. In addition to this nominal performance, we also demonstrate that our control strategy yields favorable robustness margins and that it can be used to identify a sparse control architecture for control design via alternative means.