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Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning

Beyer, Kirstin and Beckmann, Robert and Geißendörfer, Stefan and von Maydell, Karsten and Agert, Carsten (2021) Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning. Energies, 14 (7), p. 1991. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en14071991. ISSN 1996-1073.

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Official URL: http://dx.doi.org/10.3390/en14071991


The increasing penetration of the power grid with renewable distributed generation causes significant voltage fluctuations. Providing reactive power helps balancing the voltage in the grid. This paper proposes a novel adaptive volt-var control algorithm on the basis of Deep reinforcement learning. The learning agent is an online-learning deep deterministic policy gradient that is applicable under real-time conditions in smart inverters for reactive power management. The algorithm only uses input data from the grid connection point of the inverter itself; thus, no additional communication devices are needed and it can be applied individually to any inverter in the grid. The proposed volt-var control is successfully simulated at various grid connection points in a 21-bus low-voltage distribution test feeder. The resulting voltage behavior is analyzed and a systematic voltage reduction is observed both in a static grid environment and a dynamic environment. The proposed algorithm enables flexible adaption to changing environments through continuous exploration during the learning process and, thus, contributes to a decentralized, automated voltage control in future power grids.

Item URL in elib:https://elib.dlr.de/142116/
Document Type:Article
Title:Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Beyer, KirstinUNSPECIFIEDhttps://orcid.org/0000-0002-3733-5790UNSPECIFIED
Beckmann, RobertUNSPECIFIEDhttps://orcid.org/0000-0001-9331-8170UNSPECIFIED
Geißendörfer, StefanUNSPECIFIEDhttps://orcid.org/0000-0002-7496-8191UNSPECIFIED
von Maydell, KarstenUNSPECIFIEDhttps://orcid.org/0000-0003-0966-5810UNSPECIFIED
Agert, CarstenUNSPECIFIEDhttps://orcid.org/0000-0003-4733-5257UNSPECIFIED
Date:3 April 2021
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:p. 1991
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:deep reinforcement learning; low-voltage grid; reactive power; smart inverter; voltage control; volt-var-optimization
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Geißendörfer, Dr. Stefan
Deposited On:06 May 2021 09:06
Last Modified:24 May 2022 23:47

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