Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Demand-Response in Smart Buildings

This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture