A DAG-based cloud-fog layer architecture for distributed energy management in smart power grids in the presence of PHEVs

被引：9

作者：

Lin, Yubin ^{[1
]}

Cheng, Chenbing ^{[2
]}

Xiao, Fen ^{[3
]}

Alsubhi, Khalid ^{[4
]}

Aljahdali, Hani Moaiteq Abdullah ^{[5
]}

机构：

[1] State Grid Fujian Power Econ Res Inst, Fuzhou 350012, Peoples R China

[2] Minjiang Univ, Coll Phys & Informat Engn, Fozhou 350108, Peoples R China

[3] State Grid Fujian Elect Power Co Ltd, Fuzhou 350003, Peoples R China

[4] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia

[5] King Abdulaziz Univ, Fac Comp & Informat Technol Rabigh, Jeddah, Saudi Arabia

来源：

SUSTAINABLE CITIES AND SOCIETY | 2021年 / 75卷

关键词：

Machine learning; Evolutionary computing; Smart power grid; Plug-in hybrid electric vehicle; DAG-based cloud-fog computing; OPTIMIZATION; MICROGRIDS;

D O I：

10.1016/j.scs.2021.103335

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

In this paper, a new framework based on the directed acyclic graph (DAG) and distributed multi-layer cloud-fog computing to find the optimal energy management of the smart grids, considering high penetration of plug-in hybrid electric vehicles (PHEVs). The presented distributed structure lets neighboring agents make a consensus together. The uncertainties have been modeled according to the Monte Carlo simulations, due to wide usages of diverse renewable energy resources such as photovoltaic panels and wind turbines. Three diverse charging schemes have been considered in the smart grid test system which contains controlled, uncontrolled and smart chargings. The Whale Optimization Algorithm (WOA) has been used to solve the augmented Lagrangian function in each agent. The simulation results are shown that the suggested scheme is effective.

引用

页数：11

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