论文标题
学习可证明稳定的本地Volt/var控制器，以进行有效的网络操作
Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
论文作者
论文摘要
本文开发了一个数据驱动的框架，以合成电源分配网络（DNS）中分布式能源（DER）的本地伏特/var控制策略。为了提高通过通用最佳反应功率流（ORPF）问题量化的DN操作效率，我们提出了两阶段的方法。第一阶段涉及学习由ORPF实例确定的最佳操作点的歧管。为了综合本地伏特/VAR控制器，将学习任务分区分为学习本地替代物（每次DER）的最佳流形和电压输入和反应性功率输出。由于这些替代物表征了有效的DN操作点，因此在第二阶段，我们开发了将DN转移到这些操作点的本地控制方案。我们确定替代参数和控制参数的条件，以确保本地代理控制器从全球渐近含义上集体汇合到与当地替代物一致的DN操作点。我们使用神经网络对替代物进行建模并在训练阶段执行确定的条件。 IEEE 37-BUS网络上的AC功率流仿真在经验上加强了在线性化功率流假设下获得的理论稳定性。与普遍的基准方法相比，这些测试进一步突出了最佳改善。
This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution networks (DNs). Aiming to improve DN operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DN operating points, in the second stage, we develop local control schemes that steer the DN to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DN operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.
