An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

Adia Khalid,Syed Haider,Sheraz Aslam,Khursheed Aurangzeb,Mahmood Ashraf,Nadeem Javaid

doi:10.3390/en11123500

Abstract

An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.

Highlights

Microgrids are encouraging sources to solve the energy crisis and carbon emission problems around the globe
The evolution of cloud computing brings a big influence on old models of computing
The fog computing model is a powerful supplement of cloud computing

Summary

Introduction

Microgrids are encouraging sources to solve the energy crisis and carbon emission problems around the globe. Microgrids usually consist of renewable energy sources (RESs), i.e., wind power generation, solar panel, tidal, etc. Due to the many advantages of microgrids, many countries have mandates for the utility companies to increase the power generation from renewable sources. In a grid-connected mode, the microgrid is connected with external grids and able to share electricity with them; in an islanded mode, a microgrid is restricted to share energy with the external grid. Nowadays, both academia and industry are focusing on the challenges of microgrids, i.e., integration with the smart grid network, intermittent nature, optimal power flow, etc. Weather (wind speed, temperature and irradiations) can be forecasted by the deep neural network (DNN), artificial neural network (ANN) or any other prediction model

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