用本地数据实时求解电网最优潮流，精度更高、速度更快。
A Data-Driven Real-Time Optimal Power Flow Algorithm Using Local Feedback
- 基于本地反馈设计可学习函数，将优化问题转化为参数调节。
- 在IEEE 37节点测试系统上，追踪误差更小，计算速度优于基准方法。
- 无需复杂梯度计算，适合分布式能源接入的实时调控场景。
分布式能源（DERs）的广泛接入使电网具备更强的波动性和快速控制能力。本文提出一种仅依赖本地测量的实时数据驱动最优潮流算法，用于解决时变交流最优潮流（AC OPF）问题。设计一个可学习函数，以本地反馈为输入，其在特定条件下具有唯一驻点，从而将原问题转化为对函数参数的优化。采用深度神经网络（DNN）参数化该函数，并通过随机原始-对偶更新进行求解，称为训练阶段。同时提出无梯度替代方案，避免非线性潮流模型的繁琐梯度计算。理论上建立了基于DNN通用逼近性质的解追踪误差界。在IEEE 37节点测试馈线上的数值结果表明，该方法能更精确、更快速地跟踪时变最优潮流解，优于现有基准方法。
原文摘要 · Abstract (English)
The increasing penetration of distributed energy resources (DERs) adds variability as well as fast control capabilities to power networks. Dispatching the DERs based on local information to provide real-time optimal network operation is the desideratum. In this paper, we propose a data-driven real-time algorithm that uses only the local measurements to solve time-varying AC optimal power flow (OPF). Specifically, we design a learnable function that takes the local feedback as input in the algorithm. The learnable function, under certain conditions, will result in a unique stationary point of the algorithm, which in turn transfers the OPF problems to be optimized over the parameters of the function. We then develop a stochastic primal-dual update to solve the variant of the OPF problems based on a deep neural network (DNN) parametrization of the learnable function, which is referred to as the training stage. We also design a gradient-free alternative to bypass the cumbersome gradient calculation of the nonlinear power flow model. The OPF solution-tracking error bound is established in the sense of universal approximation of DNN. Numerical results on the IEEE 37-bus test feeder show that the proposed method can track the time-varying OPF solutions with higher accuracy and faster computation compared to benchmark methods.
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