CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
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arXiv
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| Format: | Preprint |
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2024
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| author | Nweye, Kingsley Kaspar, Kathryn Buscemi, Giacomo Fonseca, Tiago Pinto, Giuseppe Ghose, Dipanjan Duddukuru, Satvik Pratapa, Pavani Li, Han Mohammadi, Javad Ferreira, Luis Lino Hong, Tianzhen Ouf, Mohamed Capozzoli, Alfonso Nagy, Zoltan |
| author_facet | Nweye, Kingsley Kaspar, Kathryn Buscemi, Giacomo Fonseca, Tiago Pinto, Giuseppe Ghose, Dipanjan Duddukuru, Satvik Pratapa, Pavani Li, Han Mohammadi, Javad Ferreira, Luis Lino Hong, Tianzhen Ouf, Mohamed Capozzoli, Alfonso Nagy, Zoltan |
| contents | As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03848 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities Nweye, Kingsley Kaspar, Kathryn Buscemi, Giacomo Fonseca, Tiago Pinto, Giuseppe Ghose, Dipanjan Duddukuru, Satvik Pratapa, Pavani Li, Han Mohammadi, Javad Ferreira, Luis Lino Hong, Tianzhen Ouf, Mohamed Capozzoli, Alfonso Nagy, Zoltan Machine Learning Computers and Society Systems and Control As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation. |
| title | CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities |
| topic | Machine Learning Computers and Society Systems and Control |
| url | https://arxiv.org/abs/2405.03848 |