Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
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DOI：
10.1109/pesgm48719.2022.9916967
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Hantao Cui;Yichen Zhang
中科院分区：
文献类型：
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作者：
Hantao Cui;Yichen Zhang
本文介绍了一个通用的、高性能的电力系统强化学习环境ANDES_GYM。该环境利用ANDES的建模和仿真能力以及强化学习(RL)环境OpenAI Gym来实现电力系统强化学习算法的原型和演示。详细描述了所提出的软件工具的体系结构，以提供RL算法的“观察”和“动作”接口。给出了一个基于已有算法训练的RL的负载频率控制算法的快速原型。通过支持ANDES提供的所有电力系统动态模型和OpenAI Gym提供的众多RL算法，所提出的环境具有高度的通用性。
This paper presents andes_gym, a versatile and high-performance reinforcement learning environment for power system studies. The environment leverages the modeling and simulation capability of ANDES and the reinforcement learning (RL) environment OpenAI Gym to enable the prototyping and demonstration of RL algorithms for power systems. The architecture of the proposed software tool is elaborated to provide the “observation” and “action” interfaces for RL algorithms. An example is shown to rapidly prototype a load-frequency control algorithm based on RL trained by available algorithms. The proposed environment is highly generalized by supporting all the power system dynamic models available in ANDES and numerous RL algorithms available for OpenAI Gym.