Given their intermittency, distributed energy resources (DERs) have been commissioned with regulating voltages at fast timescales. Although the IEEE 1547 standard specifies the shape of Volt/VAR control rules, it is not clear how to optimally customize them per DER. Optimal rule design (ORD) is a challenging problem as Volt/VAR rules introduce nonlinear dynamics, require bilinear optimization models, and lurk trade-offs between stability and steady-state performance. To tackle ORD, we develop a deep neural network (DNN) that serves as a digital twin of Volt/VAR dynamics. The DNN takes grid conditions as inputs, uses rule parameters as weights, and computes equilibrium voltages as outputs. Thanks to this genuine design, ORD is reformulated as a deep learning task using grid scenarios as training data and aiming at driving the predicted variables being the equilibrium voltages close to unity. The learning task is solved by modifying efficient deep-learning routines to enforce constraints on rule parameters. In the course of DNN-based ORD, we also review and expand on stability conditions and convergence rates for Volt/VAR rules on single-/multi-phase feeders. To benchmark the optimality and runtime of DNN-based ORD, we also devise a novel mixed-integer nonlinear program formulation. Numerical tests showcase the merits of DNN-based ORD.
翻译:由于其间歇性,分散能源资源(DERs)被委托管理快速时标的电压。尽管IEEE 1547标准规定了伏尔特/VAR控制规则的形状,但尚不清楚如何最佳定制它们。最佳规则设计(ORD)是一个具有挑战性的问题,因为伏尔特/VAR规则引入非线性动态,需要双线优化模型,以及稳定与稳定状态业绩之间的倾斜。为了应对ORD,我们开发了一个深层的神经网络(DNNN),作为伏尔特/VAR动态的数码双胞胎。DNNNN将电网条件作为投入,将规则参数用作重量,并将均衡电源电压作为产出。由于这一真正的设计,ORD被重新拟订为深层次学习任务,利用电网情景作为培训数据,目的是将预测的变量作为接近一致的平衡电流。学习任务通过修改高效的深层次学习常规来实施规则参数限制。在基于DORD的非N-D的NV-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Restal-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Rest-Rest-NUD.我们审查和扩大标准/BD-Rent-BD-Rest-RD-BD-BD-BD-BD-BD-BD-BD-BD-RD-BD-S-S-S-S-S-S-BD-BD-BD-BD-BD-BD-S-S-S-BD-BD-BD-S-S-S-S-S-S-S-S-R-S-S-S-S-S-S-S-S-S-S-BD-S-S-BD-BD-BD-BD-BD-S-S-S-S-S-S-S-BD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-