Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI

TL;DR

Conversational Demand Response is introduced, a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI, and the architecture illustrates how agentic AI can bridge the aggregator-prosumer coordination gap.

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.

Figures (2)

• Figure 1: Bidirectional communication in CDR.
• Figure 2: Illustration of the dual-solve procedure for a representative household day. (a) Cost-optimized self-consumption baseline. (b) Schedule with DR commitment.