Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

Hou Shengren, Edgar Mauricio Salazar , Pedro P. Vergara , Peter Palensky

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems’ operational cost and technical constraints (e.g, generation-demand power balance) DRL algorithms must consider a trade-off when designing the reward function. This trade-off introduces extra hyperparameters that impact the DRL algorithms’ performance and capability of providing feasible solutions. In this paper, a performance comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are presented. We aim to provide a fair comparison of these DRL algorithms for energy systems optimal scheduling problems. Results show DRL algorithms’ capability of providing in real-time good-quality solutions, even in unseen operational scenarios, when compared with a mathematical programming model of the energy system optimal scheduling problem. Nevertheless, in the case of large peak consumption, these algorithms failed to provide feasible solutions, which can impede their practical implementation.
Original languageEnglish
Title of host publicationProceedings of 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022
Place of PublicationDanvers
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-6654-8032-1
ISBN (Print)978-1-6654-8033-8
DOIs
Publication statusPublished - 2022
Event2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) - Novi Sad, Serbia
Duration: 10 Oct 202212 Oct 2022

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe
Volume2022-October

Conference

Conference2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
Country/TerritorySerbia
CityNovi Sad
Period10/10/2212/10/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Energy management
  • Machine learning
  • Deep learning
  • Reinforcement learning

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