Hierarchical Distributed Voltage Regulation in Networked Autonomous Grids
Hierarchical Distributed Voltage Regulation in Networked Autonomous Grids
复制标题
网络化自治电网中的分层分布式电压调节
DOI：
10.23919/acc.2019.8814670
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Lijun Chen
中科院分区：
文献类型：
--
作者：
Xinyang Zhou;Zhiyuan Liu;Wenbo Wang;Changhong Zhao;F. Ding;Lijun Chen
提出了一种求解最优潮流(OPF)的新算法，其目标是以最小成本调度可控分布式能源(DER)进行电压调节。该算法利用基于网络自治网格(AGs)的信息结构，对大型配电网具有前所未有的可扩展性。具体地说，每个AG是具有树形拓扑的大型配电网络的子树。每个AG的拓扑和线路参数只有负责与该AG内的DER进行通信和调度的区域协调器(RC)才知道。其中每个AG被视为节点的简化网络由中央协调器(CC)管理，该中央协调器仅知道简化网络的拓扑和线路参数，并且与所有RC通信。我们共同探索这种信息结构和线性化配电潮流(LinDistFlow)模型的结构，以推导出求解最优潮流的原始-对偶梯度算法的分层分布式实现。与原始-对偶算法的集中协调实现相比，所提出的实现显著降低了计算负担。在4,521个节点的测试馈线上的数值结果表明，该分层分布式算法的收敛速度比集中式协调的原始-对偶算法提高了10倍以上。
We propose a novel algorithm to solve optimal power flow (OPF) that aims at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The proposed algorithm features unprecedented scalability to large distribution networks by utilizing an information structure based on networked autonomous grids (AGs). Specifically, each AG is a subtree of a large distribution network that has a tree topology. The topology and line parameters of each AG are known only to a regional coordinator (RC) that is responsible for communicating with and dispatching the DERs within this AG. The reduced network, where each AG is treated as a node, is managed by a central coordinator (CC), which knows the topology and line parameters of the reduced network only and communicates with all the RCs. We jointly explore this information structure and the structure of the linearized distribution power flow (LinDistFlow) model to derive a hierarchical, distributed implementation of the primal-dual gradient algorithm that solves the OPF. The proposed implementation significantly reduces the computation burden compared to the centrally coordinated implementation of the primal-dual algorithm. Numerical results on a 4,521-node test feeder show that the proposed hierarchical distributed algorithm can achieve an improvement of more than tenfold in the speed of convergence compared to the centrally coordinated primal-dual algorithm.
影响因子：
6.8
作者：
Xinyang Zhou;Masoud Farivar;Zhiyuan Liu;Lijun Chen;S. Low
通讯作者：
Xinyang Zhou;Masoud Farivar;Zhiyuan Liu;Lijun Chen;S. Low