Online Voltage Control for Unbalanced Distribution Networks Using Projected Newton Method
Online Voltage Control for Unbalanced Distribution Networks Using Projected Newton Method
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DOI：
10.1109/tpwrs.2022.3144246
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发表时间：
2022-01
影响因子：
6.6
通讯作者：
Rui Cheng;Zhaoyu Wang;Yifei Guo;Qianzhi Zhang
中科院分区：
文献类型：
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作者：
Rui Cheng;Zhaoyu Wang;Yifei Guo;Qianzhi Zhang
针对不平衡配电网，提出了一种基于投影牛顿法（PNM）的分布式能源（DER）在线电压控制策略。最佳电压/无功控制（VVC）问题被制定为一个优化程序，其目标是通过协调DER的无功输出来维持整个网络的电压分布。为了克服传统的基于梯度的方法收敛速度慢，基于PNM的解决算法来解决这个VVC问题。它利用一个非对角对称正定矩阵，从目标的Hessian矩阵，以缩放梯度，因此可以预期在这种牛顿算法的快速收敛性能。此外，利用电压测量的瞬时反馈，基于PNM的VVC的在线实现进一步设计，以处理快速系统变化。在这种基于PNM的在线VVC方案中，每个总线代理将瞬时电压测量值传送到中央代理，并且中央代理将DER的VAr输出命令传送回每个总线代理。PNM的快速收敛性能使其具有更强的真实的实时跟踪系统变化的能力。最后，数值算例验证了该方法的有效性、优越性和可扩展性。
This paper proposes an online voltage control strategy of distributed energy resources (DERs), based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal Volt/VAr control (VVC) problem is formulated as an optimization program, with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based solution algorithm is developed to solve this VVC problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm. Moreover, taking advantage of the instantaneous feedback of voltage measurements, the online implementation of the PNM-based VVC is further designed to deal with fast system variations. In this online PNM-based VVC scheme, each bus agent communicates the instantaneous voltage measurements to the central agent, and the central agent communicates the VAr output commands of DERs back to each bus agent. The fast convergence performance of PNM results in a stronger capability to track the system variations in real time. Finally, numerical case studies are performed to validate the effectiveness, superiority, and scalability of the proposed method.