Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl, Hans Auer

TL;DR
This paper presents Conversational Demand Response (CDR), a novel bidirectional natural language interaction system between aggregators and prosumers, enabling transparent, explainable, and scalable residential demand response coordination through agentic AI.
Contribution
It introduces a two-tier multi-agent architecture for real-time, conversational demand response, combining automation with user agency and transparency, and provides an open-source implementation.
Findings
Interactions complete in under 12 seconds.
Demonstrates scalable, transparent prosumer-aggregator communication.
Enables prosumer-initiated upstream communication.
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…
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Taxonomy
TopicsSmart Grid Energy Management · Multi-Agent Systems and Negotiation · Transportation and Mobility Innovations
