Cross-layer Design for Real-Time Grid Operation: Estimation, Optimization and Power Flow

Picallo, Miguel; Liao-McPherson, Dominic; Bolognani, Silverio; Dörfler, Florian

doi:10.48550/arxiv.2109.13842

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“…Remark 2: The sensitivity learning approach ( 9) is independent of the method used to update the input u, since it only requires the increment ∆u and the measured ∆y. Hence, it is not only compatible with the projected-gradient-based OFO in (10), but can be combined with linearly simplified AC-OPF as (4), or other OFO approaches, e.g., primal-dual methods [6], [7], quadratic programming [20], [21], which may have other desirable properties, like strict constraint satisfaction or a faster convergence.…”

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confidence: 99%

Adaptive Real-Time Grid Operation via Online Feedback Optimization with Sensitivity Estimation

Picallo¹,

Ortmann²,

Bolognani³

et al. 2021

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In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input-output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a time-varying AC Optimal Power Flow (AC-OPF). Instead of relying on a full grid model, e.g., grid admittance matrix, OFO only requires the steady-state sensitivity relating a change in the controllable inputs, e.g., power injections set-points, to a change in the measured outputs, e.g., voltage magnitudes. Since an inaccurate sensitivity may lead to a model-mismatch and jeopardize the performance, we propose a recursive least-squares estimation that enables OFO to learn the sensitivity from measurements during real-time operation, turning OFO into a model-free approach. We analytically certify the convergence of the proposed OFO with sensitivity estimation, and validate its performance on a simulation using the IEEE 123-bus test feeder, and comparing it against a state-of-the-art OFO with constant sensitivity.

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