Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
arXiv:2603.06217v1 Announce Type: new Abstract: 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 aggregat
arXiv:2603.06217v1 Announce Type: new Abstract: 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.
Executive Summary
This paper introduces Conversational Demand Response (CDR), a novel coordination mechanism that leverages agentic AI to enable bidirectional natural language interactions between aggregators and prosumers in residential demand response. Moving beyond traditional one-way dispatch or price alerts, CDR introduces a two-tier multi-agent architecture that allows aggregator agents to dispatch flexibility requests and prosumer HEMS to evaluate deliverability via optimization-based tools. Crucially, the mechanism supports prosumer-initiated upstream communication, enhancing user agency and transparency. The proof-of-concept evaluation demonstrates rapid interaction completion within 12 seconds, offering a scalable solution that balances automation with user input. The open-source release of all components further supports reproducibility and innovation. Overall, CDR represents a significant step toward reconciling scalability with user empowerment in demand response.
Key Points
- ▸ Introduction of bidirectional natural language via agentic AI
- ▸ Two-tier multi-agent architecture for aggregator-prosumer coordination
- ▸ Support for prosumer-initiated communication and rapid interaction speed
Merits
Innovation in Coordination Mechanism
CDR uniquely integrates agentic AI to enable bidirectional communication, offering a scalable yet transparent alternative to existing one-way DR systems.
User Empowerment
By enabling prosumer-initiated upstream communication, the model preserves user agency and increases transparency, critical for sustaining participation.
Feasibility Demonstration
The proof-of-concept evaluation confirms operational viability within realistic timeframes (under 12 seconds).
Demerits
Deployment Complexity
Integrating agentic AI into existing DR infrastructure may require additional training, adaptation, or interface adjustments.
Scalability Concerns
While promising, the system’s scalability under high-volume, concurrent interactions remains empirically unproven beyond initial tests.
Expert Commentary
The paper represents a thoughtfully conceived solution to a longstanding coordination problem in demand response. The integration of agentic AI as an intermediary between aggregators and prosumers is particularly compelling, as it bridges the gap between automated grid optimization and human-centric decision-making. Unlike prior approaches that treated prosumers as passive recipients of signals, CDR elevates them to active participants in the coordination process. The open-source release is a significant contribution to the field, fostering reproducibility and accelerating adoption. Moreover, the demonstration of sub-12-second interaction latency validates the feasibility of real-time, conversational coordination. While the authors acknowledge scalability and deployment challenges, their candid acknowledgment of these issues enhances credibility. This work signals a shift toward more participatory, explainable, and sustainable energy systems—a trend likely to influence future research in distributed energy resources and user-centric grid management.
Recommendations
- ✓ Utilities should pilot CDR in select residential clusters to assess impact on engagement metrics and DR participation rates.
- ✓ Academic and industry researchers should extend CDR’s framework to include multi-agent learning or adaptive preference modeling to enhance dynamic responsiveness.