A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids

Researchers propose a quantum-assisted distributed AI framework for optimizing microgrid operations that combines renewable energy sources with storage and demand-response systems. The system uses quantum and classical solvers to solve dispatch problems within strict deadlines, achieving optimal results with 97.83% renewable utilization and zero missed deadlines in testing.

This research addresses a critical infrastructure challenge: real-time optimization of microgrids that integrate volatile renewable energy sources with conventional generation, storage, and controllable loads. The quantum-assisted approach is significant because microgrid dispatch is computationally complex—solving it repeatedly under hard time constraints requires balancing solution quality against execution speed, a problem where hybrid quantum-classical methods show promise.

The work extends beyond traditional optimization by framing solver selection itself as an agentic decision problem. BDIx agents learn solver latency patterns and commit to strategies that guarantee deadline compliance, incorporating machine learning into the optimization pipeline. The belief-shaped storage valuation mechanism represents a more sophisticated approach than myopic per-slot optimization, embedding temporal market signals into energy pricing decisions.

The experimental results demonstrate practical viability: zero deadline violations, exact optimal dispatch in every slot, daily costs matching theoretical lower bounds, and near-total renewable penetration. The 4.5% cost increase when deactivating the storage valuation mechanism quantifies the value of intertemporal optimization. However, evaluation occurs in simulation with statevector QAOA rather than actual quantum hardware, leaving questions about real-world quantum processor performance and error rates.

For the energy sector and quantum computing fields, this work validates hybrid quantum-classical approaches for critical infrastructure. It suggests microgrids represent an early-stage quantum advantage use case where problem complexity and tight latency constraints favor quantum acceleration. The framework's scalability to larger grids and its performance on error-prone quantum processors remain open questions for future deployment.

• →Quantum-assisted hybrid solver approach achieves optimal microgrid dispatch while guaranteeing deadline compliance through intelligent solver selection
• →Framework achieves 97.83% renewable energy utilization with 146.24 EUR daily cost matching exact theoretical lower bound in 24-hour simulation
• →Agent-based belief system learns solver latencies and commits deliberation strategies, integrating machine learning into optimization pipeline
• →Storage valuation mechanism improves daily costs by 4.5% by incorporating intertemporal price signals instead of myopic slot-by-slot optimization
• →Evaluation uses statevector simulation; real quantum hardware performance and scalability to larger grids remain to be tested