A Fog Model for Dynamic Load Flow Analysis in Smart Grids

Abstract

In the last 20 years, the amount of energy consumed has grown more than 50% and due to a shortage of energy resources in the future, will not be possible to meet all this demand. The current distribution model transports energy from stations to consumers, but does not consider the use of alternative sources. The smart grids have emerged to allow the inclusion of alternative forms of energy generation in the grid. Yet, to avoid an overload in the system is necessary to calculate the power flow in real time. In this paper, we use Fog Computing as mean to reduce the logical distance between the central distribution and consumption spot. IoT devices in the network edge have more effectiveness and less cost to handle the power flow information. We evaluate the performance of the Newton-Raphson and Gauss-Seidel algorithms with the objective of developing calculations in real time of the load flow problem with the help of fog. Our results have shown that is possible to make a smart grid based on Fog Computing and thus making smart electric networks that react to the environment.