AI 中文总结
针对配电网优化中传统方法的不足，提出GridRAG框架，通过将场景特征与最优解嵌入联合空间，检索相似历史场景，并用扩散模块细化解，实现跨场景通用且加速求解，相比基线有显著优势。
AI 中文摘要
由不确定的分布式能源资源（DER）配置文件驱动的快速变化的运行场景，使得传统的配电网优化方法要么计算成本高昂，要么通用性差。本文介绍了GridRAG，这是一个开创性的检索增强框架，将优化转变为“检索-细化”范式。GridRAG首先将场景特征和最优解嵌入到联合表示空间中以确保语义一致性。基于混合语义信息，从预先构建的数据库中检索相似的历史场景。然后集成一个SDEdit风格的扩散模块，通过对近可行流形上的条件分布进行建模来细化检索到的解。这一过程有效地将检索到的解拉到近最优吸引盆中，为最终求解器提供高质量的热启动。在四个标准拓扑结构上的三个优化任务中得到验证，GridRAG展示了卓越的跨场景通用性，与现有的基于学习和基于模型的基线相比，求解时间加快了数倍。我们的代码可在此https URL获取。
英文摘要
Rapidly shifting operational scenarios driven by uncertain Distributed Energy Resource (DER) profiles render conventional distribution network optimization methods either computationally expensive or poorly generalizable. This paper introduces GridRAG, a pioneering retrieval-augmented framework that transforms optimization into a ``retrieve-and-refine'' paradigm. GridRAG first embeds scenario features and optimal solutions into a joint representation space to ensure semantic consistency. Based on the hybrid semantic information, the similar historical scenarios are then retrieved from a pre-constructed database. Then an SDEdit-style diffusion module is integrated to refine retrieved solutions by modeling the conditional distribution over near-feasible manifolds. This process effectively pulls retrieved solutions into near-optimal attraction basins, providing a high-quality warm-start for the final solver. Validated on three optimization tasks across four standard topologies, GridRAG demonstrates superior cross-scenario generalization and a multi-fold speedup in solution time compared to existing learning-based and model-based baselines. Our code is available at https://github.com/YuxuanCEE/GridRAG.
