Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/24626
Title: A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems
Authors: Stanojev, Ognjen 
Kundacina, Ognjen 
Markovic, Uros 
Vrettos, Evangelos 
Aristidou, Petros 
Hug, Gabriela 
Major Field of Science: Engineering and Technology
Field Category: Mechanical Engineering
Keywords: Frequency control;Low-inertia systems;Reinforcement learning;Voltage source converter
Issue Date: Apr-2021
Source: 52nd North American Power Symposium, NAPS, 2021, 11-13 April, Tempe, AZ, USA
Conference: North American Power Symposium 
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.
URI: https://hdl.handle.net/20.500.14279/24626
ISBN: 9781728181929
DOI: 10.1109/NAPS50074.2021.9449821
Rights: © IEEE
Type: Conference Papers
Affiliation : University of Zurich 
University of Novi Sad 
Swissgrid AG 
Cyprus University of Technology 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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