PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems

Ting-Han Fan, Xian Yeow Lee, Yubo Wang
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:21-33, 2022.

Abstract

Reinforcement learning for power distribution systems has so far been studied using customized environments due to the proprietary nature of the power industry. To encourage researchers to benchmark reinforcement learning algorithms, we introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power losses and voltage violations under physical networked constraints. PowerGym provides four distribution systems (13Bus, 34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design variants for various control difficulties. To foster generalization, PowerGym offers a detailed customization guide for users working with their distribution systems. As a demonstration, we examine state-of-the-art reinforcement learning algorithms in PowerGym and validate the environment by studying controller behaviors. The repository is available at https://github.com/siemens/powergym.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-fan22a, title = {PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems}, author = {Fan, Ting-Han and Lee, Xian Yeow and Wang, Yubo}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {21--33}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/fan22a/fan22a.pdf}, url = {https://proceedings.mlr.press/v168/fan22a.html}, abstract = {Reinforcement learning for power distribution systems has so far been studied using customized environments due to the proprietary nature of the power industry. To encourage researchers to benchmark reinforcement learning algorithms, we introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power losses and voltage violations under physical networked constraints. PowerGym provides four distribution systems (13Bus, 34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design variants for various control difficulties. To foster generalization, PowerGym offers a detailed customization guide for users working with their distribution systems. As a demonstration, we examine state-of-the-art reinforcement learning algorithms in PowerGym and validate the environment by studying controller behaviors. The repository is available at https://github.com/siemens/powergym.} }
Endnote
%0 Conference Paper %T PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems %A Ting-Han Fan %A Xian Yeow Lee %A Yubo Wang %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-fan22a %I PMLR %P 21--33 %U https://proceedings.mlr.press/v168/fan22a.html %V 168 %X Reinforcement learning for power distribution systems has so far been studied using customized environments due to the proprietary nature of the power industry. To encourage researchers to benchmark reinforcement learning algorithms, we introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power losses and voltage violations under physical networked constraints. PowerGym provides four distribution systems (13Bus, 34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design variants for various control difficulties. To foster generalization, PowerGym offers a detailed customization guide for users working with their distribution systems. As a demonstration, we examine state-of-the-art reinforcement learning algorithms in PowerGym and validate the environment by studying controller behaviors. The repository is available at https://github.com/siemens/powergym.
APA
Fan, T., Lee, X.Y. & Wang, Y.. (2022). PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:21-33 Available from https://proceedings.mlr.press/v168/fan22a.html.

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