CityLearn v3：面向可再生能源社区真实控制研究的可配置仿真与评估框架
CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities
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中文总结 AI 辅助
本文提出CityLearn v3，一个可配置的仿真与评估框架，用于在参与者变化、设备故障等现实条件下研究可再生能源社区的控制，通过区分请求与执行动作及服务感知指标，揭示聚合性能背后的服务失败与约束问题。
中文摘要 AI 辅助
可再生能源社区（RECs）协调建筑、光伏发电、电池、电动汽车和柔性负荷。控制器研究常常简化参与者的变化、设备可用性、服务截止时间和数据质量，因此较低的成本或峰值需求可能掩盖未完成的服务或不可行的功率请求。本文提出了CityLearn v3，一个在此类条件下进行REC控制研究的可配置仿真与评估框架。它在单一仿真环境中表示变化的成员和资产、柔性负荷截止时间、需求响应请求、本地能源共享以及数据或设备故障。建筑和相位的功率限制约束可控请求，而声明的时间步长保持功率与能量核算的一致性。该框架记录控制器输入，并区分请求的动作与应用于仿真设备的动作。参考控制器、服务与约束感知的性能指标以及轨迹导出支持社区内部和社区之间的比较。软件检查和应用程序示例考察了服务交付、电气约束、结算和变化场景；一个合成的高频轨迹回放说明了聚合如何在不改变年能量的情况下掩盖短时峰值。总之，这些记录允许将聚合性能与服务失败、动作缩减和参与者级结果一起解读。
英文摘要
Renewable energy communities (RECs) coordinate buildings, photovoltaic generation, batteries, electric vehicles and flexible loads. Controller studies often simplify changing participation, equipment availability, service deadlines and data quality, so lower cost or peak demand can conceal missed services or infeasible power requests. This paper presents CityLearn v3, a configurable simulation and evaluation framework for REC control studies under these conditions. It represents changing members and assets, flexible-load deadlines, demand-response requests, local energy sharing, and data or equipment failures within one simulation environment. Building and phase power limits constrain controllable requests, while a declared timestep preserves consistent power-to-energy accounting. The framework records controller inputs and distinguishes requested actions from those applied to the simulated equipment. Reference controllers, service- and constraint-aware performance indicators, and trajectory exports support comparisons within and across communities. Software checks and application examples examine service delivery, electrical constraints, settlement and changing scenarios; a synthetic high-frequency trace replay illustrates how aggregation can conceal short peaks without changing annual energy. Together, these records allow aggregate performance to be interpreted alongside service failures, action reductions and participant-level outcomes.
发表机构
- INESC TEC（INESC TEC（系统与计算机工程、技术与科学研究所））
- Polytechnic of Porto - School of Engineering（波尔图理工学院工程学院）
- FEUP - Faculty of Engineering, University of Porto（波尔图大学工程学院）
- Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。
