A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF formulations consider simple control policies such as affine policies that are mathematically simple and resemble many policies used in current practice. Motivated by the efficacy of machine learning (ML) algorithms and the potential benefits of general control policies for cost and constraint enforcement, we put forth a deep neural network (DNN)-based policy that predicts the generator dispatch decisions in real time in response to uncertainty. The weights of the DNN are learnt using stochastic primal-dual updates that solve the SOPF without the need for prior generation of training labels and can explicitly account for the feasibility constraints in the SOPF. The advantages of the DNN policy over simpler policies and their efficacy in enforcing safety limits and producing near optimal solutions are demonstrated in the context of a chance constrained formulation on a number of test cases.

翻译：现代电网安全和最佳运作面临的一个突出挑战是由于负荷和可再生能源的不确定性日益增大,因此对现代电网的安全和最佳运作构成了一项突出的挑战。托盘式最佳电流(SOPF)配方提供了一种机制,通过计算在不确定情况下维持可行性的发送决定和控制政策来处理这些不确定性。大多数SOPF配方考虑简单的控制政策,例如数学上简单且与目前做法中采用的许多政策相似的线性政策。受机器学习算法的功效和一般控制政策对成本和限制执行的潜在好处的驱动,我们提出了一项深层神经网络(DNNN)政策,根据不确定性实时预测发电机发送决定。DNN的权重是利用随机的原始性更新来学习的,这种更新无需事先生成培训标签即可解决SOPFF,可以明确说明SPF的可行性限制。DNN政策优于较简化的政策,以及它们在执行安全限制和产生近乎最佳的解决办法方面的效力,在对一些试验案例进行机会有限的情况下得到证明。