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

Abstract

• New framework based on the directed acyclic graph and distributed multi-layer cloud-fog computing. • Finding a solution for the optimal energy management of the smart grids. • Considering a machine learning algorithm to solve augmented Lagrangian function in each agent. 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.