A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.
CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.
CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
Nweye, Kingsley (author) / Kaspar, Kathryn (author) / Buscemi, Giacomo (author) / Fonseca, Tiago (author) / Pinto, Giuseppe (author) / Ghose, Dipanjan (author) / Duddukuru, Satvik (author) / Pratapa, Pavani (author) / Li, Han (author) / Mohammadi, Javad (author)
Journal of Building Performance Simulation ; 18 ; 17-38
2025-01-02
22 pages
Article (Journal)
Electronic Resource
English
On occupant-centric building performance metrics
| Online Contents | 2017
On occupant-centric building performance metrics
| British Library Online Contents | 2017