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.

训练一种深度学习系统（CANOS）来预测近似最优解（与真实的 AC-OPF 成本相差不超过 1%），同时不牺牲速度，并在包含多达 10,000 个母线的实际电网规模上获得有希望的实证结果。因为 CANOS 是一个图神经网络，它对拓扑变化具有鲁棒性，为更复杂的 OPF 问题的优化铺平了道路，这些问题会改变电网连通性，如机组组合、拓扑优化和受约束的安全性 OPF。

CANOS: 快速可扩展的神经网络 AC-OPF 求解器，抗 N-1 扰动

CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1  Perturbations

Energy markets can provide incentives for undesired behavior of market
participants. Multi-agent Reinforcement learning (MARL) is a promising new
approach to determine the expected behavior of energy market participants.
However, reinforcement learning requires many interactions with the system to
converge, and the power system environment often consists of extensive
computations, e.g., optimal power flow (OPF) calculation for market clearing.
To tackle this complexity, we provide a model of the energy market to a basic
MARL algorithm, in form of a learned OPF approximation and explicit market
rules. The learned OPF surrogate model makes an explicit solving of the OPF
completely unnecessary. Our experiments demonstrate that the model additionally
reduces training time by about one order of magnitude, but at the cost of a
slightly worse approximation of the Nash equilibrium. Potential applications of
our method are market design, more realistic modeling of market participants,
and analysis of manipulative behavior.

通过提供一个学习了的 OPF 近似和显式的市场规则的能源市场模型，以 MARL 算法的形式来确定预期的能源市场参与者的行为，从而减少与系统的交互次数，并减少培训时间，同时稍微糟糕地近似了纳什均衡。