Deep Reinforcement Learning Based Continuous Volt-Var Optimization in Power Distribution Systems with Renewable Energy Resources

In order to build a low-carbon and environmentally friendly energy system, a large number of distributed generation connected to the grid, which has brought great challenges to the stable operation of the distribution network. The intermittency and volatility of distributed generation lead to repeat...

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Bibliographic Details
Published in2021 IEEE Sustainable Power and Energy Conference (iSPEC) pp. 682 - 686
Main Authors Li, Wenyun, Huang, Wei, Zhu, Tao, Wu, Minghe, Yan, Ziheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.12.2021
Subjects
deep reinforcement learning
distributed generation
Distribution networks
Power distribution
Reactive power
Reinforcement learning
Renewable energy sources
SAC
smart inverter
static var compensator
Switches
Transforms
voltage regulation
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Summary:In order to build a low-carbon and environmentally friendly energy system, a large number of distributed generation connected to the grid, which has brought great challenges to the stable operation of the distribution network. The intermittency and volatility of distributed generation lead to repeated voltage violations in power system. At the same time, the frequent switching of transformer taps and capacitor switches greatly increases the economic cost. This paper introduces the method of deep reinforcement learning to transform the Volt-Var optimize problem into a Markov decision process with continuous action space. The soft actor-critic algorithm is used to train the agent to control the static var compensator and the smart inverter to optimize the reactive power of the distribution network. Comparative studies on IEEE 12-node and 33-node test systems demonstrate the performance of the proposed solution.
DOI:10.1109/iSPEC53008.2021.9735939