Real-time Decision Making for Power System via Imitation Learning and Reinforcement Learning

Guo, Lei; Guo, Jun; Zhang, Yong; Guo, Wanshu; Xue, Yusheng; Wang, Lipeng

doi:10.1109/icpsasia55496.2022.9949821

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…[8] proposes an optimal power flow method based on Lagrangian deep reinforcement learning for real-time optimization of power grid control. [9] implements an online AC OPF by combining reinforcement learning and imitation learning. Imitation learning is a kind of supervised learning.…”

Section: Of 11mentioning

confidence: 99%

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Zhao¹,

Wu²,

He³

et al. 2023

Preprint

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Modern power system integrates more and more new energy and use a large number of power electronic equipment. This makes it face more challenges in online optimization and real-time control. Deep reinforcement learning(DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. In this paper, we explore DRL based online combination optimization method of grid section for large complex power system. In our method, to improve the convergence speed of the model, we propose to discretize the output action of the unit and simplify the action space. We also design a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain the stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, we propose to use the annealing optimization algorithm to make the granularity of the unit output finer. We verify our method on IEEE 118-bus system. The experimental results show that our model has fast convergence speed and better performance, and can obtain stable solutions.

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Section: Of 11mentioning

confidence: 99%

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Zhao¹,

Wu²,

He³

et al. 2023

Preprint

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show abstract

“…Yan and Xu (2020) proposed an optimal power flow method based on Lagrangian deep reinforcement learning for real-time optimization of power grid control. Guo et al (2022) implemented online AC-OPF by combining reinforcement learning and imitation learning. Imitation learning is introduced to improve the learning efficiency of agents in reinforcement learning by learning from expert experience.…”

mentioning

confidence: 99%

Combination optimization method of grid sections based on deep reinforcement learning with accelerated convergence speed

Zhao,

Wu,

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

A modern power system integrates more and more new energy and uses a large number of power electronic equipment, which makes it face more challenges in online optimization and real-time control. Deep reinforcement learning (DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. This paper explores a DRL-based online combination optimization method of grid sections for a large complex power system. In order to improve the convergence speed of the model, it proposes to discretize the output action of the unit and simplify the action space. It also designs a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain a stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, this paper proposes to use the annealing optimization algorithm to make the granularity of the unit output finer. The proposed method in this paper has been verified on an IEEE 118-bus system. The experimental results show that it has fast convergence speed and better performance and can obtain stable solutions.

show abstract

Real-time Decision Making for Power System via Imitation Learning and Reinforcement Learning

Cited by 2 publications

References 7 publications

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Combination Optimization Method of Grid Section Based on Deep Reinforcement Learning with Accelerated Convergence Speed

Combination optimization method of grid sections based on deep reinforcement learning with accelerated convergence speed

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