构建了评估家庭能源管理参与度与电网灵活性的基准框架。
EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility
- 结合仿真环境与大模型模拟用户授权行为
- 在两地实测中实现最高授权率与最低用电波动
- 适合关注人机协同电网调度的研究者使用
居民虚拟电厂可通过调整家庭用电需求提供电网灵活性，但物理灵活性仅在居民授权并实际响应时才成为可靠容量。现有基准仅评估控制策略，忽略事件相关的授权过程。本文提出EnergyBridge，一个融合容量上报、用户授权与物理执行的基准与代理框架。该框架结合天津和柏林地区特异的EnergyPlus仿真环境，以及基于大模型的用户参与模拟器。在584组与真人角色扮演匹配的人类判断测试中，大模型模拟器在方法排序上保持5.3分的平均绝对接受误差。相比传统控制器与代理基线，EnergyBridge在两地均实现了最高的模拟授权率、最低的事件窗口用电量，以及最可靠的容量承诺。我们开源了人类数据与代码，支持可复现的人本电网灵活性研究：https://github.com/Agentic-Intelligence-Lab/EnergyBridge。
原文摘要 · Abstract (English)
Residential virtual power plants (VPPs) can provide grid flexibility by shifting household demand, but physical flexibility becomes dependable capacity only when residents authorize a plan and the promised response is delivered. Existing benchmarks evaluate control but omit event-specific authorization. We present EnergyBridge, a benchmark and agent framework connecting capacity reporting, household authorization, and physical execution. It combines region-specific EnergyPlus environments for Tianjin and Berlin with an LLM-based User Participation Simulator. Against 584 persona- and event-matched human role-play judgments, the LLM-based User Participation Simulator preserves method ordering with a 5.3-point mean absolute acceptance error. Across conventional controllers and agent baselines, EnergyBridge achieves the highest simulated authorization, lowest event-window energy, and the most reliable capacity commitment in both regions. We release human data and codes for reproducible human-centered grid-flexibility research: https://github.com/Agentic-Intelligence-Lab/EnergyBridge.
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