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。

CityLearn v2：面向能源灵活、韧性、以居民为中心和低碳的电网互动社区管理

CityLearn v2: Energy-flexible, resilient, occupant-centric, and  carbon-aware management of grid-interactive communities

Residential buildings account for a significant portion (35\%) of the total
electricity consumption in the U.S. as of 2022. As more distributed energy
resources are installed in buildings, their potential to provide flexibility to
the grid increases. To tap into that flexibility provided by buildings,
aggregators or system operators need to quantify and forecast flexibility.
Previous works in this area primarily focused on commercial buildings, with
little work on residential buildings. To address the gap, this paper first
proposes two complementary flexibility metrics (i.e., power and energy
flexibility) and then investigates several mainstream machine learning-based
models for predicting the time-variant and sporadic flexibility of residential
buildings at four-hour and 24-hour forecast horizons. The
long-short-term-memory (LSTM) model achieves the best performance and can
predict power flexibility for up to 24 hours ahead with the average error
around 0.7 kW. However, for energy flexibility, the LSTM model is only
successful for loads with consistent operational patterns throughout the year
and faces challenges when predicting energy flexibility associated with HVAC
systems.

该研究提出了两种互补的灵活性测量指标，并使用主流的基于机器学习的模型预测住宅建筑物在四小时和 24 小时预测时段内的时间变化和间歇性灵活性。长短期记忆（LSTM）模型在功率灵活性方面表现最佳，可以在 24 小时内准确预测功率灵活性，平均误差约为 0.7 千瓦。然而，在能源灵活性方面，LSTM 模型只能成功预测具有一致运行模式的负荷，并且在预测与暖通空调系统相关的能源灵活性时面临挑战。