The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more computationally efficient approach to address that challenge via the analytical deep learning-based surrogate model. The key idea is to resort to the deep learning for developing a computationally cheap surrogate model to replace the original time-consuming differential-algebraic constraints related to TTC. However, the deep learning-based surrogate model introduces implicit rules that are difficult to handle in the optimization process. To this end, we derive the Jacobian and Hessian matrices of the implicit surrogate models and finally transfer them into an analytical formulation that can be easily solved by the interior point method. Surrogate modeling and problem reformulation allow us to achieve significantly improved computational efficiency and the yielded solutions can be used for operational planning. Numerical results carried out on the modified IEEE 39-bus system demonstrate the effectiveness of the proposed method in dealing with com-plicated TTC constraints while balancing the computational efficiency and accuracy.

中文翻译：

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基于分析深度学习的具有动态 TTC 约束的运营规划代理模型

风电渗透率的增加导致关键走廊的更多运行变化，传统耗时的瞬态稳定性约束总传输能力（TTC）运行规划无法满足实时监控需求。本文开发了一种计算效率更高的方法，通过基于深度学习的分析代理模型来应对这一挑战。关键思想是借助深度学习来开发计算成本低廉的替代模型，以取代与 TTC 相关的原始耗时的微分代数约束。然而，基于深度学习的代理模型引入了在优化过程中难以处理的隐式规则。为此，我们推导出隐式代理模型的 Jacobian 矩阵和 Hessian 矩阵，最后将它们转换成一个解析公式，可以很容易地通过内点法求解。代理建模和问题重构使我们能够显着提高计算效率，并且生成的解决方案可用于运营规划。在修改后的 IEEE 39 总线系统上进行的数值结果证明了所提出的方法在处理复杂的 TTC 约束同时平衡计算效率和准确性方面的有效性。