A novel Koopman-inspired method for the secondary control of microgrids with grid-forming and grid-following sources

Xun Gong and Xiaozhe Wang

Applied Energy, 2023, vol. 333, issue C, No S0306261922018888

Abstract: This paper proposes an online data-driven Koopman-inspired identification and control method for microgrid secondary voltage and frequency control. Unlike typical data-driven methods, the proposed method requires no warm-up training yet with guaranteed bounded-input–bounded-output (BIBO) stability and even asymptotic stability under some mild conditions. The proposed method estimates the Koopman state space model adaptively so as to perform effective secondary voltage and frequency control that can handle microgrid nonlinearity and uncertainty. Case studies in the 4-bus and 13-bus microgrid test systems (with grid-forming and grid-following sources) demonstrate the effectiveness and robustness of the proposed identification and control method subject to the change of operating conditions and large disturbances (e.g., microgrid mode transitions, generation/load variations) even with measurement noises and time delays.

Keywords: Data-driven control; Adaptive Koopman-inspired identification; Microgrid secondary control; Grid-forming; Grid-following; Koopman operator control; Observer Kalman filter identification (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2022.120631

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