In recent years, there has been significant interest in the development of
machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF).
Although significant progress has been achieved in predicting high-quality
primal solutions, no existing learning-based approach can provide valid dual
bounds for AC-OPF. This paper addresses this gap by training optimization
proxies for a convex relaxation of AC-OPF. Namely, the paper considers a
second-order cone (SOC) relaxation of ACOPF, and proposes a novel dual
architecture that embeds a fast, differentiable (dual) feasibility recovery,
thus providing valid dual bounds. The paper combines this new architecture with
a self-supervised learning scheme, which alleviates the need for costly
training data generation. Extensive numerical experiments on medium- and
large-scale power grids demonstrate the efficiency and scalability of the
proposed methodology.

本研究针对交流最优潮流问题进行了基于机器学习的优化代理方法的探索，通过凸松弛和新型双重架构提供有效的对偶上下界，同时结合自监督学习策略，实现了高效和可扩展性的优化。