EnergyBridge：家庭能源管理、用户参与度与电网灵活性基准测试
EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility
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中文总结 AI 辅助
本研究提出EnergyBridge基准框架，结合特定区域EnergyPlus环境与LLM用户模拟器，在含584组人类角色扮演判断的实验中，实现了更高授权率、更低能耗与更可靠容量承诺，推动以人为本的电网灵活性研究。
中文摘要 AI 辅助
住宅虚拟电厂（VPP）可通过调整家庭需求为电网提供灵活性，但只有在居民授权计划并兑现承诺响应时，物理灵活性才能成为可靠容量。现有基准测试仅评估控制效果，却忽略了特定事件的授权环节。本文提出EnergyBridge，这一连接容量上报、家庭授权与物理执行的基准及智能体框架。它结合了针对天津和柏林的特定区域EnergyPlus环境，以及基于大语言模型（LLM）的用户参与度模拟器。在584个与人物角色、事件匹配的人类角色扮演判断中，该LLM用户参与度模拟器保持了方法排序的一致性，平均接受误差为5.3点。在传统控制器和智能体基线方法中，EnergyBridge在两个区域均实现了最高的模拟授权率、最低的事件窗口能耗，以及最可靠的容量承诺。本文公开了人类数据和代码，以支持可复现的以人为本的电网灵活性研究：this https URL。
英文摘要
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.
