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.

通过扩展 CityLearn v1 并利用美国建筑库数据集中的终端使用负荷剖面，CityLearn v2 提供了一个模拟环境，用于创建具有弹性的多主体分布式能源系统和具有动态住户反馈的客观控制的虚拟网格互动社区。本研究详细介绍了 v2 环境设计，并提供了应用示例，利用强化学习来管理电池储能系统的充放电周期、车辆与电网的控制以及热泵功率调制期间的热舒适度。