Dual Conic Proxies for AC Optimal Power Flow

• URL: http://arxiv.org/abs/2310.02969v2
• Date: Tue, 26 Mar 2024 14:00:59 GMT
• Title: Dual Conic Proxies for AC Optimal Power Flow
• Authors: Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck,
• Abstract summary: 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. The paper combines this new architecture with a self-supervised learning scheme, which alleviates the need for costly training data generation.
• Score: 16.02181642119643
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 AC-OPF, and proposes \revision{a novel 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.

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