Distributed voltage regulation of smart distribution networks: Consensus-based information synchronization and distributed model predictive control scheme
Distributed voltage regulation of smart distribution networks: Consensus-based information synchronization and distributed model predictive control scheme
复制标题
智能配电网分布式调压:基于共识的信息同步和分布式模型预测控制方案
DOI:
10.1016/j.ijepes.2019.03.059
复制
发表时间:
2019-10
影响因子:
5.2
通讯作者:
Shen Feifan
中科院分区:
文献类型:
--
作者:
Guo Yifei;Wu Qiuwei;Gao Houlei;Shen Feifan
This paper proposes a distributed voltage control (DVC) scheme for smart distribution networks with high penetration of inverter-based distributed generators (DGs), aiming to optimally coordinate DG units and on load tap changer (OLTC) transformer to regulate the voltages within the feasible range. The proposed scheme consists of two important parts: (1) distributed information synchronization (DIS) framework and (2) distributed model predictive control (DMPC)-based voltage control scheme. The DIS framework is established based on the consensus protocols to synchronize the specific information about the critical bus voltages and potential OLTC actions. The DMPC-based voltage control scheme is presented, in which each DG unit only exchanges information with its immediate neighbors and solves the local optimal control problem. Two control modes are designed to better deal with different operating conditions. In the normal mode, only the reactive power outputs of DG units are optimized to mitigate the voltage deviations. In the corrective mode, both of the active and reactive power outputs of DG units are optimally controlled to correct the severe voltage deviations. To mitigate the mutual interaction between the DGs and OLTC, the potential actions of OLTC are predicted and considered in the optimization problem of each units. The control performance of the proposed scheme was demonstrated using a real medium-voltage (MV) distribution network with two feeders under both normal and large-disturbance conditions.
登录
查看更多内容
影响因子:
15.9
作者:
M. Behrangrad
通讯作者:
M. Behrangrad
影响因子:
9.6
作者:
Hongbin Sun;Qinglai Guo;Boming Zhang;Ye Guo;Zhengshuo Li;Jianhui Wang
通讯作者:
Hongbin Sun;Qinglai Guo;Boming Zhang;Ye Guo;Zhengshuo Li;Jianhui Wang
影响因子:
9.6
作者:
H. Farag;Ehab F. EZ. l-Saadany;R. Seethapathy
通讯作者:
H. Farag;Ehab F. EZ. l-Saadany;R. Seethapathy
影响因子:
6.6
作者:
Yashodhan P. Agalgaonkar;B. Pal;R. Jabr
通讯作者:
Yashodhan P. Agalgaonkar;B. Pal;R. Jabr
影响因子:
11
作者:
Delong Zhang;Jianlin Li;D. Hui
通讯作者:
Delong Zhang;Jianlin Li;D. Hui