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1.	Zhang, Mengfan, et al. (author) Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System 2022 In: 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728193885 Conference paper (peer-reviewed)abstract With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.

Zhang, Mengfan, et al. (author)
Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System
2022
In: 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728193885
Conference paper (peer-reviewed)abstract
- With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.

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