论文标题
使用深度学习的逆变器的伏特/var控制规则的最佳设计
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
论文作者
论文摘要
分布网格受到分布式能源（DERS）的可变功率注射引起的快速电压波动的挑战。为了调节电压，IEEE标准1547建议根据分段摩正/VAR控制规则，每个DER注入反应能力。尽管该标准表明默认形状，但可以定制每个总线的规则。当Volt/var规则引入非线性动力学以及潜在的稳定性和稳态电压配置文件之间的折衷方案时，最佳规则设计（ORD）的这项任务具有挑战性。 ORD作为混合企业非线性程序（MINLP）配制，但与问题大小相关。为了更有效的解决方案，我们将ORD重新制定为一个深度学习问题。这个想法是设计模拟Volt/var动力学的DNN。 DNN将网格方案作为输入，规则参数作为权重，并输出平衡电压。可以通过训练DNN来找到最佳规则参数，以便在各种情况下接近统一。 DNN仅用于优化规则，并且从未在现场使用。在处理ORD时，我们还审查并扩展了单相和多相馈线的伏特/VAR动力学的稳定性条件和收敛速率。测试通过对其MINLP对应物进行基准测试，以展示基于DNN的ORD的优点。
Distribution grids are challenged by rapid voltage fluctuations induced by variable power injections from distributed energy resources (DERs). To regulate voltage, the IEEE Standard 1547 recommends each DER inject reactive power according to piecewise-affine Volt/VAR control rules. Although the standard suggests a default shape, the rule can be customized per bus. This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles. ORD is formulated as a mixed-integer nonlinear program (MINLP), but scales unfavorably with the problem size. Towards a more efficient solution, we reformulate ORD as a deep learning problem. The idea is to design a DNN that emulates Volt/VAR dynamics. The DNN takes grid scenarios as inputs, rule parameters as weights, and outputs equilibrium voltages. Optimal rule parameters can be found by training the DNN so its output approaches unity for various scenarios. The DNN is only used to optimize rules and is never employed in the field. While dealing with ORD, we also review and expand on stability conditions and convergence rates for Volt/VAR dynamics on single- and multi-phase feeders. Tests showcase the merit of DNN-based ORD by benchmarking it against its MINLP counterpart.
