Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
This addresses the coordination gap in demand response for prosumers by enabling scalable automation while preserving transparency and user agency, though it appears incremental as it builds on existing multi-agent and optimization methods.
The paper tackles the problem of coordinating residential demand response by introducing Conversational Demand Response (CDR), a bidirectional natural language interaction mechanism between aggregators and prosumers using agentic AI, with proof-of-concept evaluation showing interactions complete in under 12 seconds.
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.