用量子辅助智能体框架，实时优化混合可再生能源微电网调度。
A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids
- 用量子-经典混合求解器+智能体协商，动态选择最快解法以满足调度截止时间。
- 24小时仿真中零超时、成本达理论最低值146.24欧元，可再生能源利用率97.83%。
- 引入储能跨时段价值评估机制，使调度更具前瞻性，避免短期最优陷阱。
实时协调包含波动性可再生能源、可调度单元、储能和可削减负荷的混合微电网，需在严格控制截止时间内反复求解组合优化与联盟形成问题。本文提出一种量子辅助的分布式人工智能（DAI）框架：每个控制周期的调度问题由扩展信念-欲望-意图（BDIx）智能体建模为无约束二次二值优化（QUBO）问题，并通过量子、量子启发及经典求解器组合求解。协调智能体将求解器选择作为首要决策行为，基于对求解器延迟的预判，承诺能在截止时间内完成的求解方案。此外，引入信念驱动的储能估值机制，使储能智能体以未来峰价折价定价，将跨时段信息注入原本仅考虑单周期的优化中。在含光伏、风能、电池、发电机组和需求响应的联网微电网上进行24小时仿真，使用状态矢量模拟执行量子近似优化算法（QAOA），并与禁忌搜索、模拟退火、二进制粒子群优化、贪婪下降和穷举法逐周期对比。所有决策周期均未超时，承诺调度在每周期达到精确最优，日总成本为146.24欧元，等于精确下界，可再生能源利用率达97.83%，无未满足电量。关闭储能估值机制后，日成本升至152.75欧元，增加4.5%。
原文摘要 · Abstract (English)
The real-time orchestration of microgrids that combine fluctuating renewable sources, dispatchable units, storage and curtailable consumers requires the repeated solution of combinatorial dispatch and coalition formation problems under hard control deadlines. In this paper, a quantum-assisted agentic distributed artificial intelligence (DAI) framework is proposed in which the dispatch problem of each control slot is formulated as a quadratic unconstrained binary optimization (QUBO) problem by Belief-Desire-Intention extended (BDIx) agents and is solved by a portfolio of quantum, quantum-inspired and classical solvers. Solver selection is elevated to a first-class agentic deliberation action of the coordinator agent. Learned beliefs about solver latencies are maintained and the solver intention that is expected to satisfy the prevailing deliberation deadline is committed in each slot. In addition, a belief-shaped storage valuation mechanism is introduced through which the storage agent prices its energy at a discounted future-peak value, injecting intertemporal information into the otherwise myopic per-slot optimization. The framework is evaluated on a 24-hour simulation of a grid-connected microgrid with photovoltaic, wind, battery, genset and demand-response assets, with the Quantum Approximate Optimization Algorithm (QAOA) executed by statevector simulation and benchmarked per slot against tabu search, simulated annealing, binary particle swarm optimization, greedy descent and exhaustive enumeration. Zero deliberation deadlines are missed, the committed dispatch attains the exact optimum on every slot and the realized daily cost of 146.24 EUR equals the exact lower bound, with 97.83 percent renewable utilization and zero unserved energy. When the storage valuation mechanism is deactivated, the daily cost is increased to 152.75 EUR, a 4.5 percent increase.
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