Summary of Citylearn V2: Energy-flexible, Resilient, Occupant-centric, and Carbon-aware Management Of Grid-interactive Communities, by Kingsley Nweye et al.
CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
by Kingsley Nweye, Kathryn Kaspar, Giacomo Buscemi, Tiago Fonseca, Giuseppe Pinto, Dipanjan Ghose, Satvik Duddukuru, Pavani Pratapa, Han Li, Javad Mohammadi, Luis Lino Ferreira, Tianzhen Hong, Mohamed Ouf, Alfonso Capozzoli, Zoltan Nagy
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is presented for benchmarking distributed energy resource control algorithms on a community scale. CityLearn v2 extends previous work by providing a simulation environment that leverages real-world data to create virtual grid-interactive communities. This allows for the evaluation of different control strategies, including reinforcement learning, to manage various applications such as battery energy storage system charging/discharging cycles and thermal comfort during heat pump power modulation. The paper details the design of CityLearn v2 and provides examples of its application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Distributed energy resources are important for a sustainable future. This research helps us understand how they work together in a community to provide flexibility, which is critical for making sure we have enough energy when we need it. A new tool called CityLearn v2 is developed to test different control strategies that can be used to manage these resources. This tool uses real-world data to create virtual communities and helps us figure out how to make the most of our energy storage systems, vehicles, and heating systems. |
Keywords
» Artificial intelligence » Reinforcement learning
