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.

翻译：随着越来越多的分布式能源资源成为需求侧基础设施的一部分，量化其在社区尺度上提供的能源柔性至关重要，尤其是为了解地理、气候和居住者行为差异对其有效性的影响，以及确定加速其实际应用的最佳控制策略。CityLearn提供了一个基准测试环境，用于评估简单和先进的分布式能源资源控制算法，包括基于规则的控制、模型预测控制和强化学习控制。本文介绍的CityLearn v2在CityLearn v1的基础上进行了扩展，通过提供一个利用美国建筑存量终端用能负荷数据集创建虚拟电网交互社区的仿真环境，实现具有动态居住者反馈的弹性多智能体分布式能源资源和目标控制。本研究详细阐述了v2环境的设计，并提供了应用实例，这些实例利用强化学习来管理电池储能系统的充放电周期、车网互联控制以及热泵功率调制过程中的热舒适性。