发表机构
EPB Quantum; IonQ Inc.（EPB量子; IonQ公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出量子增强深度强化学习框架用于配电网重构，通过对比不同Q网络架构，发现量子增强Q网络能以更少参数获得更高奖励，为组合电力系统优化提供新方向。
AI 中文摘要
现代智能电网生成越来越丰富的运行数据，并部署比以往更多的远程控制开关资产，这为数据驱动的配电网重构（DNR）创造了新的机遇。DNR优化电网拓扑以提高效率、减少功率损耗并维持运行约束，但寻找最优开关配置仍然是一个计算上要求很高的组合问题。在本文中，我们提出一个用于静态DNR的深度强化学习（DRL）框架，以系统性地研究Q网络架构如何影响解的质量和学习性能。我们在受控的DRL框架内评估多种架构，仅改变Q网络以隔离每种架构选择的贡献。我们的结果表明，量子增强的Q网络可以用更少的参数获得比经典对应网络更高的奖励，这表明此类架构是组合电力系统优化的一个有前景的方向。
英文摘要
Modern smart grids generate increasingly rich operational data and deploy greater numbers of remotely controlled switching assets than ever before, creating new opportunities for data-driven distribution network reconfiguration (DNR). DNR optimizes grid topology to improve efficiency, reduce power losses, and maintain operational constraints, but finding optimal switch configurations remains a computationally demanding combinatorial problem. In this paper, we propose a deep reinforcement learning (DRL) framework for static DNR to systematically investigate how the Q-network architecture affects both solution quality and learning performance. We evaluate multiple architectures within a controlled DRL framework, varying only the Q-network to isolate the contribution of each architectural choice. Our results show that quantum-enhanced Q-networks can achieve higher rewards with fewer parameters than their classical counterpart, suggesting that such architectures are a promising direction for combinatorial power systems optimization.
Comments14 pages, 8 figures
