Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely. In the simplest setting, OPF determines how much power to generate in order to minimize costs while meeting demand for power and satisfying physical and operational constraints. In even the simplest case, power grid operators use approximations of the AC-OPF problem because solving the exact problem is prohibitively slow with state-of-the-art solvers. These approximations sacrifice accuracy and operational feasibility in favor of speed. This trade-off leads to costly "uplift payments" and increased carbon emissions, especially for large power grids. In the present work, we train a deep learning system (CANOS) to predict near-optimal solutions (within 1% of the true AC-OPF cost) without compromising speed (running in as little as 33--65 ms). Importantly, CANOS scales to realistic grid sizes with promising empirical results on grids containing as many as 10,000 buses. Finally, because CANOS is a Graph Neural Network, it is robust to changes in topology. We show that CANOS is accurate across N-1 topological perturbations of a base grid typically used in security-constrained analysis. This paves the way for more efficient optimization of more complex OPF problems which alter grid connectivity such as unit commitment, topology optimization and security-constrained OPF.

中文翻译：

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CANOS：快速且可扩展的神经 AC-OPF 求解器，对 N-1 扰动具有鲁棒性

最优潮流（OPF）是指一系列相关的优化问题，其目标是高效、安全地运行电力系统。在最简单的设置中，OPF 确定生成多少电力，以最大限度地降低成本，同时满足电力需求并满足物理和操作限制。即使在最简单的情况下，电网运营商也会使用 AC-OPF 问题的近似值，因为使用最先进的求解器求解精确问题的速度非常慢。这些近似牺牲了准确性和操作可行性以换取速度。这种权衡导致成本高昂的“附加费”和碳排放增加，特别是对于大型电网而言。在目前的工作中，我们训练了一个深度学习系统（CANOS）来预测接近最优的解决方案（在真实 AC-OPF 成本的 1% 以内），而不影响速度（运行时间仅需 33--65 毫秒）。重要的是，CANOS 可扩展到实际的网格大小，并在包含多达 10,000 辆公交车的网格上获得有希望的实证结果。最后，由于 CANOS 是一个图神经网络，因此它对拓扑变化具有鲁棒性。我们证明 CANOS 在安全约束分析中通常使用的基本网格的 N-1 拓扑扰动中是准确的。这为更有效地优化更复杂的 OPF 问题铺平了道路，这些问题会改变电网连接性，例如机组组合、拓扑优化和安全约束的 OPF。