Smart Grid Communication and Information Technologies: A Review

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Renewable energy plays an important role in the future energy framework of the European Union. The European Union will reach a 20 % share of renewable energy in total energy consumption and increase energy efficiency by 20 % by 2020. Smart grids will be the backbone for facilitating the integration of renewable energy resources into future energy systems. The plans and status of renewable energy resources development and energy policy in Europe are introduced in this chapter. The development of smart grid technologies for facilitating the renewable-based energy systems in the European Union is also discussed. The role of Denmark, one of the leading countries for developing smart grid technologies and using renewable energy resources, has been emphasized in this chapter.

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

The cognitive smart grid (SG) communication paradigm aims to mitigate quality of service (QoS) issues in obsolete communication architecture associated with the conventional electrical grid. This paradigm entails the integration of advanced information and communication technologies (ICTs) into power grids, enabling a two-way flow of information. However, due to the exponential increase in wireless applications and services, also driven by the deployment of the Internet of Things (IoT) smart devices, SG communication systems are expected to handle large volumes of data. As a result, the operation of SG networks is confronted with the major challenge of managing and processing data in a reliable and secure manner. The existing works in the literature proposed architectures with the objective to mitigate the underlying QoS issues such as latency, bandwidth, data congestion, energy efficiency, etc. In addition, a variety of communication technologies have been analyzed for their capacity to support stringent QoS requirements for diverse SGs environments. This notwithstanding, a standard architecture designed to mitigate the aforementioned issues for SG networks remains a work-in-progress. The main objective of this paper is to investigate the emerging technologies such as cognitive radio networks (CRNs) as part of the Fifth-Generation (5G) mobile technology for reliable communication in SG networks. Furthermore, a hybrid architecture based on the combination of fog computing and cloud computing is proposed. In this architecture, real-time latency-sensitive information is given high priority, with fog edge based servers deployed in close proximity to home area networks (HANs) for preprocessing and analyzing of information collected from smart IoT devices. In comparison to the recent works in the literature, which are mainly based on CRNs and 5G separately, the proposed architecture in this paper incorporates the combination of CRNs and 5G for reliable and efficient communication in SG networks.

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.

Cloud computing can be considered as a key tool in the energy management process that reflects effective monitoring, controlling as well as conserving energy. Energy management is a key concern in pepper implementation of smart grid technology. Due to the absence of cloud computing, energy cannot be supplied to a broad range of users as limited storage and memory are present in traditional technology. In the traditional energy management process, various kinds of stability issues can be seen in the absence of any cloud. Concerning all those reasons cloud computing is used in smart grid technology for providing a broad range of energy without interrupting the power supply. On the other hand, smart grid technology reflects a unique network that generates a two-way electricity flow by using various kinds of digital tools and techniques. This innovative technology can easily detect and at the same time react against any kind of issue generated in this technology due to the presence of cloud computing techniques. Compared to traditional technology, a huge level of renewable energy resources are used in smart grid technologies. A less amount of carbon emission reflects the generation of a better future environment. A survey analysis has occurred by taking 51 random people and three questions are asked related to effectiveness of cloud computing on smart grid technology and impact of this technology on the environment. A detailed discussion has also given after analysing survey.

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

Since the concerns for energy conservation and environment are growing, renewable energy resources are rapidly increasing, which lead to additional variability and uncertainty for the power system operators. The uncertainty imposes considerable technical and economic challenges to ISOs. According to recent advances in smart grid technologies, Demand Response Programs (DRPs) are expected to facilitate the integration of intermittent renewable energy resources into the grid. In this paper, DRPs are considered as an option to reduce and manage renewable energy resources uncertainty. On this basis, this paper proposes a flexible load approach with the application to wind power grid integration. In this context, and in order to model the reality of the power market, a network constrained unit commitment problem associated with DRPs is presented to clear the market transactions. Simulation results show that utilizing DRPs can improve grid integration of wind power, while making significant economic and technical benefits for the system.

Integration of renewable energy resources is a good solution for electrifying the remote unelectrified villages. In this paper, optimal and economical combination of renewable energy resources has been done using Homer software for the electrification of remote village, Kadura in the state of Jammu and Kashmir. The renewable energy resources that we have considered in our case are hydro, solar, and wind. First the commercial, domestic, industrial, agricultural, and irrigation load demand have been estimated of this particular area. The optimized combination provided cost-effective and reliable electricity to this remote unelectrified village. The paper also explains sensitive analysis of external disturbances like climatic changes or technical snags that would affect the overall optimal combination. The combination provided by integrating renewable energy resources is environment friendly which is the most challenging task for modern power sector.

Smart Grid technology will revolutionize modern industries providing powerful solutions to increase the efficiency of traditional Electric Grids. Smart Grid is an energy supply network that uses digital communications technology. The increasing load and consumption demands increase electricity complications, for instance, the demand has increased, along with problems like blackouts, overloads, and voltage sags as well as current electrical network growing critical carbon emissions and foremost dealing with cyber-attacks. However, the Smart Grid technology originates with vulnerabilities and complications especially for securing information which is the most vital concern. This article analyzes the threats and potential solutions of smart grids based on the Internet of Things. We focus on the types of cyber attacks and provide in-depth information The cyber security status of the smart grid.

Many regions in the world exhibit temporal complementarity of solar and wind energy. In such regions, during certain time periods, when more solar energy is present, lesser amount of wind energy is available and vice-verse. The motivation of this work is to develop a general planning methodology for the integration of these variable renewable energy (VRE) resources in smart power grids, while exploiting temporal complementarity to minimize the supply demand mismatch. We develop an appropriate analytical framework to determine the total investment needed in such VRE resources and the fraction of the total investment in each resource. A multivariate optimization problem is formulated and an optimal algorithm is developed to determine these parameters. To test the proposed methodology, a case study is developed for a region in Northern Ireland. In this region, based on the historical data of past 10 years and using daily average wind and solar capacity factors, we determine the Pearson correlation coefficient, which turns out to be −0.34, showing a sufficient degree of complementarity (anti-correlation) between solar and wind energy. Planning parameters are determined for different load profiles in our case study. General conclusion of the work is that once the temporal complementarity of solar and wind energy resources is exploited, the net supply variability is significantly reduced in microgrids. However, the total investment costs also increases, which is offset by the savings in the storage costs. In addition, reduced variability also leads to a reduction in storage losses, emissions losses, dispatch losses, fuel costs and network congestion.

Over the last decade, Internet of Things (IoTs) have brought radical changes to the means and forms of communication for monitoring and control of a large number of applications including Smart Grid (SG). Traditional energy networks have been modernized to SGs to boost the energy industry in the context of efficient and effective power management, performance, real-time control and information flow using two-way communication between utility provides and end-users. However, integrating two-way communication in SG comes at the cost of cyber security vulnerabilities and challenges. In the context of SG, node compromise is a severe security threat due to the fact that a compromised node can significantly impact the operations and security of the SG network. Therefore, in this chapter, Key Management Scheme for Communication Layer in the Smart Grid (KMS-CL-SG) has proposed. In order to achieve a secure end-to-end communication we assign a unique key to each node in the group.

The Internet of Things (IoT) has transpired as a fascinating technology for smart cities, smart homes, and smart grids by using a vast amount of IoT data. A smart grid is one of the core components where transport, generation, delivery, and electricity consumption are enhanced in terms of protection and reliability. The existing power grid is suffering from many problems such as outages and unpredictable power disturbances, inflexible energy rates, unnoticeable customer fraud, and many other disadvantages. These problems lead to the ever-rising demand for fossil fuel and service costs. For example, the peak hour demand needs to be overestimated and more energy generated to minimize the risk of an outage. The main problem of the smart grid is the tremendous amount of data needs to be collected from the IoT devices, and processing the data is a challenge. Using and predicting a large amount of data in smart Grid and IoT is still in its infancy. To remedy this problem, we propose a hybrid solution by using the Cloud and Edge Computing to process the data. Processing and predicting at the edge that is close to the embedded devices and homes to save in latency and storage compared to putting all the processing in the Cloud. In this paper, we define a hybrid solution where we use the edge computing for the smart grid information processing where the microgrids are located on the edge of the IoT network, and on the Cloud to use for the power grid that distributes power to the microgrids. We proposed a machine learning engine that used the decision tree to establish the communication between the edge layer, failover between edges, and the Cloud layer.

Summary from only given. Three features characterize electricity generation from renewable sources: it is located far away from where it will be used; it is decentralized; and it is volatile. What looks like a long list of technical details actually conceals a true revolution in terms of energy supply. In Germany, we are in a special situation. Although the EU Commission wants fewer fossil fuels to be burned and more renewable energies to be used in electricity generation in all member states, the time pressure has increased dramatically for Germany because of the upcoming energy turnaround. Because of the scheduled phasing out of all nuclear power plants by 2022, we must establish a Smart Grid over the next ten years and provide a stable energy supply for a prosperous industrial nation. The three features of producing energy from renewable sources add up to three problem areas that must be resolved for the Smart Grid to succeed: · The generation of energy far from where it is used, especially on offshore wind farms at sea, requires a major upgrade in transmission of power from the north of Germany to the south. · To reorganize central power generation and supply renewable energies that are produced locally, we need more and improved data about the current state of the grid. · To compensate for volatility, we must be able to respond faster at distribution level. This requires automatic voltage regulation in the medium- and low-voltage grid. Smart Grid will serve as the nervous system of energy supply after the energy evolution. In addition to being an electricity network, it is also a communication network in which intelligent devices record energy supply and consumption data in real-time and adjust the grid voltage automatically. For this energy grid, the intelligent devices installed must have two attributes: standards compliance and IT security. Besides expanding the transmission capacities, the use of uniform and secure communication protocols is the biggest challenge in building this central infrastructure.