CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
CoRR(2024)
摘要
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.
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