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Published October 2018 | Published
Journal Article Open

Distributed plug-and-play optimal generator and load control for power system frequency regulation

Abstract

A distributed control scheme, which can be implemented on generators and controllable loads in a plug-and-play manner, is proposed for power system frequency regulation. The proposed scheme is based on local measurements, local computation, and neighborhood information exchanges over a communication network with an arbitrary (but connected) topology. In the event of a sudden change in generation or load, the proposed scheme can restore the nominal frequency and the reference inter-area power flows, while minimizing the total cost of control for participating generators and loads. Power network stability under the proposed control is proved with a relatively realistic model which includes nonlinear power flow and a generic (potentially nonlinear or high-order) turbine-governor model, and further with first- and second-order turbine-governor models as special cases. In simulations, the proposed control scheme shows a comparable performance to the existing automatic generation control (AGC) when implemented only on the generator side, and demonstrates better dynamic characteristics than AGC when each scheme is implemented on both generators and controllable loads. Simulation results also show robustness of the proposed scheme to communication link failure.

Additional Information

© 2018 The Authors. Published by Elsevier Ltd. Under a Creative Commons license - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Received 20 September 2017; Received in revised form 12 January 2018; Accepted 10 March 2018. This work was supported by NSF through grants CNS 1545096, EPCN 1619352, and CCF 1637598, DTRA through grant HDTRA 1-15-1-0003, ARPAE through the GRID DATA program and the NODES program, and Skoltech through Collaboration Agreement 1075-MRA. The work of E. Mallada was also supported by Johns Hopkins E2SHI Seed Grant, ARO through contract W911NF-17-1-0092, and NSF through grants CNS 1544771, EPCN 1711188, and AMPS 1736448.

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August 19, 2023
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October 18, 2023