Real-world implementation of reinforcement learning based energy coordination for a cluster of households
- Author
- Gargya Gokhale (UGent) , Niels Tiben, Marie-Sophie Verwee (UGent) , Manu Lahariya, Bert Claessens (UGent) and Chris Develder (UGent)
- Organization
- Abstract
- Given its substantial contribution of 40% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set of residential buildings, to provide grid supporting services, that eventually should include ancillary services. In particular, we present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our RL approach relies solely on observed data from individual households and does not require any explicit building models or simulators, making it practical to implement and easy to scale. We show the feasibility of our proposed RL-based coordination strategy in a real-world setting. In a 4-week case study, we demonstrate a hierarchical control system, relying on an RL-based ranking system to select which households to activate flex assets from, and a real-time PI control-based power dispatch mechanism to control the selected assets. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.
- Keywords
- Advantage function, Coordination, Building Cluster, Reinforcement Learning, Demand Response
Downloads
-
8417.pdf
- full text (Accepted manuscript)
- |
- open access
- |
- |
- 734.23 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HEMK5BHVMG3P1YAW9Y1B5H0Y
- MLA
- Gokhale, Gargya, et al. “Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households.” PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, Association for Computing Machinery (ACM), 2023, pp. 347–51, doi:10.1145/3600100.3625681.
- APA
- Gokhale, G., Tiben, N., Verwee, M.-S., Lahariya, M., Claessens, B., & Develder, C. (2023). Real-world implementation of reinforcement learning based energy coordination for a cluster of households. PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, 347–351. https://doi.org/10.1145/3600100.3625681
- Chicago author-date
- Gokhale, Gargya, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, and Chris Develder. 2023. “Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households.” In PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, 347–51. New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3600100.3625681.
- Chicago author-date (all authors)
- Gokhale, Gargya, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, and Chris Develder. 2023. “Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households.” In PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, 347–351. New York: Association for Computing Machinery (ACM). doi:10.1145/3600100.3625681.
- Vancouver
- 1.Gokhale G, Tiben N, Verwee M-S, Lahariya M, Claessens B, Develder C. Real-world implementation of reinforcement learning based energy coordination for a cluster of households. In: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023. New York: Association for Computing Machinery (ACM); 2023. p. 347–51.
- IEEE
- [1]G. Gokhale, N. Tiben, M.-S. Verwee, M. Lahariya, B. Claessens, and C. Develder, “Real-world implementation of reinforcement learning based energy coordination for a cluster of households,” in PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, Istanbul, Turkey, 2023, pp. 347–351.
@inproceedings{01HEMK5BHVMG3P1YAW9Y1B5H0Y,
abstract = {{Given its substantial contribution of 40% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set of residential buildings, to provide grid supporting services, that eventually should include ancillary services. In particular, we present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our RL approach relies solely on observed data from individual households and does not require any explicit building models or simulators, making it practical to implement and easy to scale. We show the feasibility of our proposed RL-based coordination strategy in a real-world setting. In a 4-week case study, we demonstrate a hierarchical control system, relying on an RL-based ranking system to select which households to activate flex assets from, and a real-time PI control-based power dispatch mechanism to control the selected assets. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.}},
author = {{Gokhale, Gargya and Tiben, Niels and Verwee, Marie-Sophie and Lahariya, Manu and Claessens, Bert and Develder, Chris}},
booktitle = {{PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023}},
isbn = {{9798400702303}},
keywords = {{Advantage function,Coordination,Building Cluster,Reinforcement Learning,Demand Response}},
language = {{eng}},
location = {{Istanbul, Turkey}},
pages = {{347--351}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{Real-world implementation of reinforcement learning based energy coordination for a cluster of households}},
url = {{http://doi.org/10.1145/3600100.3625681}},
year = {{2023}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: