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

Kirstin Beyer, Robert Beckmann, Stefan Geißendörfer, Karsten von Maydell and Carsten Agert
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Kirstin Beyer: German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Robert Beckmann: German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Stefan Geißendörfer: German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Karsten von Maydell: German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Carsten Agert: German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany

Energies, 2021, vol. 14, issue 7, 1-11

Abstract: 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.

Keywords: deep reinforcement learning; low-voltage grid; reactive power; smart inverter; voltage control; volt-var-optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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