Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inverter schedules, guaranteeing feasibility is largely elusive. Rather than training DNNs to imitate already computed optimal power flow (OPF) solutions, this work integrates DNN-based inverter policies into the OPF. The proposed DNNs are trained through two OPF alternatives that confine voltage deviations on the average and as a convex restriction of chance constraints. The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions. This is important when OPF has to be solved for an unobservable feeder. DNN weights are trained via back-propagation and upon differentiating the AC power flow equations assuming the network model is known. Otherwise, a gradient-free variant is put forth. The latter is relevant when inverters are controlled by an aggregator having access only to a power flow solver or a digital twin of the feeder. Numerical tests compare the DNN-based inverter control schemes with the optimal inverter setpoints in terms of optimality and feasibility.

翻译：将可再生能源纳入分配网的离岸外包,除非负荷要求和太阳能发电经常进行远程计量,否则,控制倒流者将受到近似网格条件或其代理人的制约成为关键规格。虽然深神经网络(DNNS)可以学习最佳反向时间表,但保障可行性基本上是难以实现的。这项工作不是训练DNNS模仿已经计算的最佳电流(OPF)解决方案,而是将基于DNNN的逆向等离子政策纳入OPF。提议的DNNPs通过两个OPF替代方案进行培训,这些替代方案将电压偏差限制在平均值,并将其作为对机会限制的一种配置限制。经过培训的DNNNNNN可以由当前网条件的局部、吵闹或代理描述器驱动。当OPFP必须解决不可观测的进料问题时,这一点很重要。DNNNN重量通过回调法培训,假设基于网络模式的AC电流方程式。否则,将推出一个无梯变方。当内反向的电源由具有最佳流或顶级软体测试的硬体测试规则时,只有具有最佳的NUBEVEVA的硬度。