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Cloud-Fog Based Smart Grid Paradigm for Effective Resource Distribution
Abstract
Smart grid (SG) provides observable energy distribution where utility and consumers are enabled to control and monitor their production , consumption, and pricing in almost, real time. Due to increase in the number of smart devices complexity of SG increases. To overcome these problems, this paper proposes cloud-fog based SG paradigm. The proposed model comprises three layers: cloud layer, fog layer, and end user layer. The 1st layer consists of the cluster of buildings. The renewable energy source is installed in each building so that buildings become self-sustainable with respect to the generation and consumption. The second layer is fog layer which manages the user's requests, network resources and acts as a middle layer between end users and cloud. Fog creates virtual machines to process multiple users request simultaneously, which increases the overall performance of the communication system. MG is connected with the fogs to fulfill the energy requirement of users. The top layer is cloud layer. All the fogs are connected with a central cloud. Cloud provides services to end users by itself or through the fog. For efficient allocation of fog resources, artificial bee colony (ABC) load balancing algorithm is proposed. Finally, simulation is done to compare the performance of ABC with three other load balancing algorithms, particle swarm optimization (PSO), round robin (RR) and throttled. While considering the proposed scenario, results of these algorithms are compared and it is concluded that performance of ABC is better than RR, PSO and throttled.
... The results show that the proposed algorithm achieved effective results against Active VM Load Balancer and Particle Swarm Optimization (PSO) algorithms. Ismail et al. [19] used the artificial bee colony algorithm to handle the overload problem in a smart grid system. The author achieved effective fog resource allocation at the top layer for optimizing the performance of fog-based smart grid systems. ...
... They have deployed two patches of fog in the network connected to the cloud and microgrid, where the controller was responsible for maintaining the log of user requests. Ismail et al. [106] proposed fog architecture for the smart grid having a layered architecture. Virtual machines, having the capability of multitasking, have been used to address multiple customer requests at the same time to reduce the response time. ...
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.
With the increasing use of the Internet of Things (IoT) in various fields and the need to process and store huge volumes of generated data, Fog computing was introduced to complement Cloud computing services. Fog computing offers basic services at the network for supporting IoT applications with low response time requirements. However, Fogs are distributed, heterogeneous, and their resources are limited, therefore efficient distribution of IoT applications tasks in Fog nodes, in order to meet quality of service (QoS) and quality of experience (QoE) constraints is challenging. In this survey, at first, we have an overview of basic concepts of Fog computing, and then review the application placement problem in Fog computing with focus on Artificial intelligence (AI) techniques. We target three main objectives with considering a characteristics of AI-based methods in Fog application placement problem: (i) categorizing evolutionary algorithms, (ii) categorizing machine learning algorithms, and (iii) categorizing combinatorial algorithms into subcategories includes a combination of machine learning and heuristic, a combination of evolutionary and heuristic, and a combinations of evolutionary and machine learning. Then the security considerations of application placement have been reviewed. Finally, we provide a number of open questions and issues as future works.
Demand response (DR) is an effective way to control demand-side resources for power grids through advanced information and communication technologies. In the context of ubiquitous power Internet of Things (UPIoT), single cloud computing can hardly meet the requirements of large-scale real-time data processing in DR. Edge computing is a computing paradigm that deploys computing resources on the edge of the network, and its combination with cloud computing will help improve the ability of the power system to process large-scale sensor data. However, there is currently no comprehensive investigation on the application of edge computing technology in UPIoT DR. In this paper, the concept of DR and recent advances in the context of UPIoT are introduced first. Besides, the concept of edge-cloud computing (ECC) is introduced. Then, a comprehensive review of the existing work on DR based on ECC is presented. Furthermore, according to the existing work of Internet of Things (IoT) architecture based on ECC, a DR edge-cloud collaborative control architecture is proposed to meet the demand of UPIoT for ubiquitous perception and intelligent control. On this basis, the challenges in the practical application of edge computing in the UPIoT DR are introduced.
Smart grid and cloud computing architectures have been perfectly suiting each other naturally. As a result, over the years cloud computing architectures have dominated the implementations of smart grid applications to address computing needs. However, due to continuing additions of heterogeneous (sensing and actuating) devices, emergence of Internet of Things (IoT), and massive amount of data collected across the grids for analytics, have contributed to the complexity of smart grids, making cloud computing architectures no longer suitable to provide smart grid services effectively. Edge and Fog computing approaches have relieved the cloud computing architectures of problems related to network congestion, latency and locality by shift of control, intelligence and trust to the edge of the network. In this paper, a systematic literature review is used to explore the research trend of the actual implementations of edge and fog computing for smart grid applications. A total of 70 papers were reviewed from the popular digital repositories. The study has revealed that, there is significant increase in the number of smart grid applications that have exploited the use edge and fog computing approaches. The study also shows that, considerable number of the smart grid applications are related to energy optimizations and intelligent coordination of smart grid resources. There are also challenges and issues that hinder smooth adoption of edge and fog computing for smart grid applications, which include security, interoperability and programming models.
One big challenge in building a smart grid arises from the fast growing amount of data and limited communication resources. The traditional centralized communication architecture does not scale well with the explosive increase of data and has a high probability of encountering communication bottlenecks due to long communication paths. To address this challenging issue, this article presents a distributed communication architecture that implements smart grid communications in an efficient and cost-effective way. This distributed architecture consists of multiple distributed operation centers, each of which is connected to several data concentrators serving one local area and only sends summary or required integrated information to a central operation center. Using this distributed architecture, communication distance is much shortened, and thus data will be delivered more efciently and reliably. In addition, such a distributed architecture can manage and analyze data locally, rather than backhauling all raw data to the central operation center, leading to reduced cost and burden on communication resources. Advanced metering infrastructure is chosen as an example to demonstrate benefits of this architecture on improving communication performance. The distributed communication architecture is also readily applicable to other smart grid applications, for example, demand response management systems.
Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which pre-stores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing towards low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a work-load allocation problem which suggests the optimal workload allocations between fog and cloud towards the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be respectively solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.
Cloud Computing is a new paradigm where data and services of Information Technology are provided via the Internet by using remote servers. It represents a new way of delivering computing resources allowing access to the network on demand. Cloud computing consists of several services, each of which can hold several tasks. As the problem of scheduling tasks is an NP-complete problem, the task management can be an important element in the technology of cloud computing. To optimize the performance of virtual machines hosted in cloud computing, several algorithms of scheduling tasks have been proposed. In this paper, we present an approach allowing to solve the problem optimally and to take into account the QoS constraints based on the different user requests. This technique, based on the Branch and Bound algorithm, allows to assign tasks to different virtual machines while ensuring load balance and a better distribution of resources. The experimental results show that our approach gives very promising results for an effective tasks planning.
This paper gives a comprehensive discussion on applying the cloud computing technology as the new information infrastructure for the next-generation power system. First, this paper analyzes the main requirements of the future power grid on the information infrastructure and the limitations of the current information infrastructure. Based on this, a layered cloud-based information infrastructure model for next-generation power grid is proposed. Thus, this paper discussed how different categories of the power applications can benefit from the cloud-based information infrastructure. For the demonstration purpose, this paper develops three specific cloud-enabled power applications. The first two applications demonstrate how to develop practical compute-intensive and data-intensive power applications by utilizing different layered services provided by the state-of-the-art public cloud computing platforms. In the third application, we propose a cloud-based collaborative direct load control framework in a smart grid and show the merits of the cloud-based information infrastructure on it. Some cybersecurity considerations and the challenges and limitations of the cloud-based information infrastructure are also discussed.
The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.
In this paper, a new orchestration of Fog-2-Cloud based framework is presented for efficiently managing the resources in the residential buildings. It is a three layered framework having: cloud layer, fog layer and consumer layer. Cloud layer is responsible for the on-demand delivery of the resources. Effective resource management is done through the fog layer because it minimizes the latency and enhances the reliability of cloud facilities. Consumer layer is based on the residential users who fulfill their daily electricity demands through fog and cloud layers. Six regions are considered in the study, where, each region has a cluster of buildings varying between 80 to 150 and each building has 80 to 100 homes. Load requests of the consumers are considered fixed during every hour in the complete day. Two control parameters are considered: clusters of buildings and load requests, whereas, three performance parameters: request per hour, response time and processing time are also included. These parameters are optimized by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. The simulation results show that our proposed technique has outperformed the previous techniques in terms of the aforementioned parameters. Tradeoff occurs in the processing time of the algorithms as compared to response time and request per hour.
The integration of Smart Grid (SG) with cloud computing promises to develop an improved energy management system for utilities and consumers. New applications and services are developed which create large amount of data to be processed on cloud. Fog computing as an extension of cloud computing which helps to mitigate load on cloud data centers. In this paper, a three layered model based on cloud and fog framework is proposed to reduce load of consumers and power generation system. End user layer contains clusters of buildings which are connected to fog server layer. Fog layer is an intermediate layer which connects the end user layer to cloud layer. Three load balancing algorithms Round Robin (RR), throttled and proposed Particle Swarm Optimization with Simulated Annealing (PSOSA) are used for resource allocation. The service broker policy considered in this paper is optimized response time. The findings demonstrate that PSOSA performs better than RR and throttled in order to alleviate response time, processing time and cost of virtual machine, microgrid and data transfer.
Fog computing concept is introduced to reduce the load on cloud and provide similar services as cloud. However, fog covers small area rather than cloud by storing the data temporarily and sends data to cloud for permanent storage. In this paper, an integrated fog and cloud based environment for effective energy management of buildings is proposed. So, the load on cloud and fog should be balanced. Various load balancing algorithms are used to manage the load among virtual machines (VMs). In this scenario, algorithm used for load balancing among VMs is round robin (RR). Service broker policies considered in this paper are; dynamically reconfigure with load (DR) and the proposed policy. New dynamic service proximity (DSP) service broker policy is proposed for fog selection and results of DSP policy are compared with DR policy. Therefore, a tradeoff is observed between cost and response time.
Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of Electric Vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for smart grid and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for smart grid based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging-discharging loads at peak-hours periods.
With the rapid increase of monitoring devices and controllable facilities in the demand side of electricity networks, more solid information and communication technology (ICT) resources are required to support the development of demand side management (DSM). Different from traditional computation in power systems which customizes ICT resources for mapping applications separately, DSM especially asks for scalability and economic efficiency, because there are more and more stakeholders participating in the computation process. This paper proposes a novel cost-oriented optimization model for a cloud-based ICT infrastructure to allocate cloud computing resources in a flexible and cost-efficient way. Uncertain factors including imprecise computation load prediction and unavailability of computing instances can also be considered in the proposed model. A modified priority list algorithm is specially developed in order to efficiently solve the proposed optimization model and compared with the mature simulating annealing based algorithm. Comprehensive numerical studies are fulfilled to demonstrate the effectiveness of the proposed cost-oriented model on reducing the operation cost of cloud platform in DSM.
By introducing microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g., in residential microgrids and homes. Energy management may also help us to reach zero net energy (ZNE) for the residential domain. Improvement in technology, cost, and feature size has enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, require powerful computing resources, which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog computing is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management (HEM) and microgrid-level energy management have been implemented and experimented.
The smartphone is a typical cyberphysical system (CPS). It must be low energy consuming and highly reliable to deal with the simple but frequent interactions with the cloud, which constitutes the cloud-integrated CPS. Dynamic voltage scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage andfrequency of processors. In this paper, based on the DVS technique, we propose a novel Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize the total energy consumption for smartphones, while satisfying stringent time constraints and the probability constraint for applications. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
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