用对话式AI让居民与能源调度方双向沟通，提升参与感和响应效率。
Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
- 通过双层智能体架构实现能源调度方与用户间的自然语言双向交互。
- 系统交互耗时低于12秒，支持用户主动反馈偏好变化。
- 开源设计便于复现，适合关注智能电网与人机协同的研究者。
居民需求响应依赖持续的用户参与，但现有协调机制要么完全自动化，要么仅提供单向调度指令和价格提醒，难以支持用户做出知情决策。本文提出对话式需求响应（Conversational Demand Response, CDR），通过代理型AI实现调度方与用户之间的双向自然语言交互。采用两层多智能体架构：调度代理发出灵活性请求，用户侧家庭能源管理系统（HEMS）调用基于优化的工具评估可执行性与成本效益。CDR还支持用户主动向上游传递偏好变更。概念验证评估显示，交互过程在12秒内完成。该架构展示了代理型AI如何弥合调度方与用户之间的协调鸿沟，在保持自动化可扩展性的同时，保障透明度、可解释性和用户自主权，从而促进长期用户参与。所有系统组件，包括智能体提示、编排逻辑和仿真接口，均已开源，以支持可复现性与后续开发。
原文摘要 · Abstract (English)
Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one-way dispatch signals and price alerts that offer little possibility for informed decision-making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI. A two-tier multi-agent architecture is developed in which an aggregator agent dispatches flexibility requests and a prosumer Home Energy Management System (HEMS) assesses deliverability and cost-benefit by calling an optimization-based tool. CDR also enables prosumer-initiated upstream communication, where changes in preferences can reach the aggregator directly. Proof-of-concept evaluation shows that interactions complete in under 12 seconds. The architecture illustrates how agentic AI can bridge the aggregator-prosumer coordination gap, providing the scalability of automated DR while preserving the transparency, explainability, and user agency necessary for sustained prosumer participation. All system components, including agent prompts, orchestration logic, and simulation interfaces, are released as open source to enable reproducibility and further development.
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