Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents
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Abstract (arXiv)
Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centralized and decentralized reinforcement learning agents that can automatically adjust grid controllers for regulating transmission line flows at normal condition and under contingencies. The power grid flow control problem is formulated as Markov Decision Process (MDP). At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits. If line overloading issues remain unresolved, stage two is used to train decentralized SAC agent via load throw-over at local substations. The effectiveness of the proposed approach is verified on a series of actual planning cases used for operating the power grid of SGCC Zhejiang Electric Power Company.
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5 of 15 references.
References · 5
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 27
- 2020Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulationcited by 19
- 2018Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Drivingcited by 18
- 2018Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learningcited by 8
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