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Electrical Engineering and Systems Science > Systems and Control

arXiv:2007.05474 (eess)
[Submitted on 10 Jul 2020]

Title:A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems

Authors:Ognjen Stanojev, Ognjen Kundacina, Uros Markovic, Evangelos Vrettos, Petros Aristidou, Gabriela Hug
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Abstract:The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2007.05474 [eess.SY]
  (or arXiv:2007.05474v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2007.05474

Submission history

From: Ognjen Stanojev [view email]
[v1] Fri, 10 Jul 2020 16:46:39 UTC (3,615 KB)
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