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

Energies

Stefan Geißendörfer¹  Kirstin Beyer¹  Karstenvon Maydell¹  Robert Beckmann¹  Carsten Agert¹ 

[1] German Aerospace Center (DLR)—Institute for Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany;

关键词: deep reinforcement learning;    low-voltage grid;    reactive power;    smart inverter;    voltage control;    volt-var-optimization;   

DOI  :  10.3390/en14071991

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown