Saman Zahoor

Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond

Internet of Things (IoT) is an emerging concept, which aims to connect billions of devices with each other. The IoT devices sense, collect, and transmit important information from their surroundings. This exchange of very large amount of information amongst billions of devices creates a massive energy need. Green IoT envisions the concept of reducing the energy consumption of IoT devices and making the environment safe. Inspired by achieving a sustainable environment for IoT, we first give the overview of green IoT and the challenges that are faced due to excessive usage of energy hungry IoT devices. We then discuss and evaluate the strategies that can be used to minimize the energy consumption in IoT, such as designing energy efficient datacenters, energy efficient transmission of data from sensors, and design of energy efficient policies. Moreover, we critically analyze the green IoT strategies and propose five principles that can be adopted to achieve green IoT. Finally, we consider a case study of very important aspect of IoT, i.e., smart phones and we provide an easy and concise view for improving the current practices to make the IoT greener for the world in 2020 and beyond.

Energy Efficient Integration of Renewable Energy Sources in Smart Grid

With the emergence of smart grid (SG), the residents have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this regard, we design energy management control unit (EMCU) based on genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind driven optimization (WDO) to schedule appliances in presence of objective function, constraints, control parameters, and comparatively evaluate the performance. For energy pricing, real time pricing (RTP) plus inclined block rate (IBR) is used. RESs integration to SG is a challenge due stochastic nature of RE. In this paper, two techniques are addressed to handle the stochastic nature of RE. First one is energy storage system (ESS) which smooths out variation in RE generation. Second one is the trading/cooperation of excess generation to neighboring consumers. The simulation results show that WDO perform more efficiently than unscheduled in terms of reduction in: electricity cost, the tradeoff between electricity cost and waiting time, and peak to average ratio (PAR). Moreover, incorporation of RESs into SG design increase the revenue and reduce carbon emission.

Home Energy Management System Using Ant Colony Optimization Technique in Microgrid

From previous years, the research on usage of renewable energy sources (RES), specially photo voltaic (PV) arrays. This paper is based on home energy management system (HEMS). We propose a grid connected microgrid to fulfill the load demand of residential area. We have consider fifteen homes with six appliance for each home, the appliances are taken as the base load. For bill calculation, real time pricing (RTP) tariff is used. Ant colony optimization (ACO) is used for the scheduling of appliances. To fulfill the load demand; Wind turbine (WT), PV, micro turbine (MT), fuel cell (FC) and diesel generator (DG) are used. Energy storage devices are used with generators to store excessive energy. Also, we propose penalty and incentive (PI) mechanism to reduce the overall cost. Objectives of the paper are cost and peak to average ratio (PAR). The simulation results show better performance with our optimization technique rather than without any technique.

Fog Computing Based Energy Management System Model for Smart Buildings

In this article, a three layered architecture is proposed for smart buildings. A fog based infrastructure is designed and deployed on the edge of network, where fog processes the private data collected through the smart meters and stores the public data on cloud. Further, end user has facility to schedule and control the home appliances by using a centralized energy management system. Moreover, the electricity and network resources utilization charges can be calculated. We analyze the performance of cloud based centralized system, considering the fog computing as an intermittent layer between system user layer and cloud layer and without considering fog computing. Simulation results prove that fog layer enhances the efficient utilization of network resources and also reduces the bottleneck on the cloud computing.

Optimal Energy Management in Microgrids Using Meta-heuristic Technique

The energy crisis and greenhouse gas emission are increasing around the world. In order to overcome these problems, distributed energy resources are integrated which introduce the concept of microgrid (MG). The microgrid exchanges power with utility to meet load demand with the help of common coupling point. An energy management strategy is proposed in this work, which helps to minimize the operating cost of MG while considering all constraints of the system. For this purpose, a firefly algorithm is used to schedule generators of MG to fulfill the consumer demand considering the desired objectives. The proposed scheme employs FA to minimize the operating cost of a MG. In both grid-connected and islanded modes of MG, proposed scheme is applied for scheduling of distributed generators. The Significance of the proposed strategy is verified through simulations and results.

Optimized Energy Management Strategy for Home and Office

In smart grid, Demand Side Management (DSM) plays a vital role in dealing with consumer’s demand and making communication efficient. DSM not only reduces electricity cost but also increases the stability of the grid. In this regard, we introduce an energy management system model for a home and office, then propose efficient scheduling techniques for power usage in both. This system schedule the appliances on the basis of four different optimization techniques to achieve objectives that are electricity cost minimization, reduction in Peak to Average Ratio and energy consumption management. Moreover, we use Real Time Pricing because it is highly flexible and provides an understanding to consumer about price signal variations. Simulation results show that the proposed model for energy management work efficiently to achieve the objectives and provide cost-effective solution to increase the stability of smart grid.

Exploiting Meta-heuristic Technique for Optimal Operation of Microgrid

A power system with different types of micro-sources are very popular in recent years. The aim of the paper is to make the environment green by reducing green house gases and meet the load demand in an efficient way. However, we propose a grid-connected microgrid system which meets the load demand in an efficient manner to achieve our objectives. The objective of this work is to find the optimal set points of controllable micro-sources in terms of cost minimization. The grid-connected microgrid also helps to exchange power with utility during different intervals of a day to meet the load demand. The significance and performance of the proposed strategy is proved through performing simulations in MATLAB. However, the overall cost of MG is less, while in schedulable microsources the cost of FC is less as compared to MT and DE.

User Satisfaction Based Home Energy Management System for Smart Cities

With the advent of smart grid and demand side management techniques, users have opportunity to reduce their electricity cost without compromising their comfort much. In this paper, we evaluate the performance of home energy management system based on user satisfaction. Our objective is to maximize the total user satisfaction within user defined budget. For budget three different scenarios are presented that are;

Home Energy Management Using Fish Swarm Optimization Bacterial Foraging Algorithm and Genetic Algorithm in Smart Grid

0.25/day,

A Hybrid Genetic Based on Harmony Search Method to Schedule Electric Tasks in Smart Home

0.50/day and