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<p>We present a pure linear cutting-plane relaxation approach for rapidly proving tight and accurate lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF) and its multi-period extension with ramping constraints. Our method leverages outer-envelope linear cuts for well-known second-order cone relaxations for ACOPF together with modern cut management techniques and reformulations to attain numerical stability. These techniques prove effective on a broad family of ACOPF instances, including the largest ones publicly available, quickly and robustly yielding tight bounds. Additionally, we consider the (frequent) case where an ACOPF instance is handled following a small or moderate change in problem data, e.g., load changes and generator or branch shut-offs. We provide significant computational evidence, on single and multi-period ACOPF instances, that the cuts computed on the prior instance provide very good lower bounds when warm-starting our algorithm on the perturbed instance.</p>

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Accurate linear cutting-plane relaxations for ACOPF

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Daniel Bienstock,
  • Matías Villagra

摘要

We present a pure linear cutting-plane relaxation approach for rapidly proving tight and accurate lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF) and its multi-period extension with ramping constraints. Our method leverages outer-envelope linear cuts for well-known second-order cone relaxations for ACOPF together with modern cut management techniques and reformulations to attain numerical stability. These techniques prove effective on a broad family of ACOPF instances, including the largest ones publicly available, quickly and robustly yielding tight bounds. Additionally, we consider the (frequent) case where an ACOPF instance is handled following a small or moderate change in problem data, e.g., load changes and generator or branch shut-offs. We provide significant computational evidence, on single and multi-period ACOPF instances, that the cuts computed on the prior instance provide very good lower bounds when warm-starting our algorithm on the perturbed instance.