Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Engineering
  4. Communications and signal processing
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

6829 results in Communications and signal processing

Page 111 of 342

  • Appendix B - Timeline of Cellular Technology Generations

  • Bartłomiej Błaszczyszyn, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt, Martin Haenggi, University of Notre Dame, Indiana, Paul Keeler, Sayandev Mukherjee
  • Book:
    Stochastic Geometry Analysis of Cellular Networks
    Published online:
    26 March 2018
    Print publication:
    19 April 2018, pp 175-176

  • Summary

    3GPP and LTE

    The scale and complexity of design, manufacture, and deployment of wireless cellular systems make it all but impossible for a single-company proprietary architecture to gain traction in the global telecommunications market. Instead, cellular network operators and equipment vendors from all over the world have joined to form standards bodies at the national and international level to facilitate the evolution of cellular communication systems. As of this writing, six national standards bodies from around the world have combined to form a single international standards organization for wireless cellular communications, called the Third Generation Partnership Project or 3GPP. The 3GPP partnership was first created to further the universal mobile telecommunications system (UMTS) standard which defined the so-called third generation of cellular communications using code division multiple access on the air interface, but the name 3GPP was retained for the fourth generation (4G) using orthogonal frequency division multiple access. This fourth generation of cellular systems came to be known as the long-term evolution of the 3GPP standard and is simply abbreviated as 3GPP-LTE or just LTE.

    Support for HetNets in LTE

    3GPP standards are published as releases, with a typical interval of 12–18 months between releases. As the UMTS standard matured and the standards activity ramped up on LTE, the same release (Releases 8 and 9) contained standards specifications for both UMTS and LTE.

    Release 9 provided end-to-end support for LTE small cells, but these were assumed to be femtocells deployed in homes (called home enhanced node base stations or HeNBs, NodeB being the technical term for a base station in 3GPP), with backhaul provided by the internet service provider to the subscriber's home. However, there was no widespread deployment of such an LTE HeNB network. This standard also ported the “high interference indicator” flag from UMTS, whereby a cell suffering high interference on some orthogonal frequency division multiplexed subcarriers could broadcast this flag so that its neighboring cells could reduce their activity (or the activity of their served users) when they detected the flag. This frequency-domain information exchange was the only form of intercell interference coordination (ICIC) supported by this standard.

  • 14 - Simulation Tools

  • from Part IV - Smart Grid Design and Deployment
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 227-230

  • Summary

    In Chapters 5–13, we discussed several methodologies that are useful to address different problems present in the smart grid in order to provide electricity to the users in a cost-effective and reliable manner. However, we also need to realize the system behavior and impact of the parameters used in a particular model through different controllable experiments. Thus, the use of suitable simulation tools is an important concern.

    As we saw earlier, smart grid technologically combines two different entities–power grid and communication network. Therefore, a smart grid simulator needs to take into account the properties of both the power grid and the communication network. There are many tools available for the simulation of smart grid. In this chapter, we discuss some of the useful simulation tools that can be used for conducting smart grid experiments.

    Simulation Tools

    Open DSS

    Open distribution system simulator (OpenDSS) [1] is one of the popular power grid simulation tools available. It deals with power grid system planning and analysis. As the name suggests, using this tool, one can model the power distribution system and analyze the behavior of the system prior to actual deployment. Primarily, it can help to analyze the needs of distributed generation units in a power grid system. Holistically, it supports future needs such as the modeling of smart grid applications, power delivery, and harmonics analysis. Some of the wide applications of OpenDSS simulation tools are as follows [1]:

  • • Power distribution planning and analysis

  • • Analysis of integrating multiple distributed generations

  • • Distributed storage modeling

  • • Wind power simulation

  • • Solar photo-voltaic simulations

  • • Impact of electric vehicles modeling and simulation

  • • Daily/annual energy load and generation simulation

    • By default, this simulator does not include any provision to have communication protocols that can be used to design the smart grid communication networks. However, it provides the flexibility of integrating communication models into it. It can also easily be integrated with MATLAB or other well-known simulators.

    MATPOWER

    MATPOWER [2] is another simulation tool designed for analyzing power problems in a power grid. This is a package that can be integrated with MATLAB. It is useful to simulate optimal power flow problems present in the smart grid. However, the features of simulation supported by this tool are purely related to power grid simulation. MATPOWER does not have any provision to model the communication networks.

  • 1 - Introduction to Smart Grid

  • from Part I - Introduction
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 3-17

  • Summary

    A smart grid is conceptualized as the existing power grid supported by bi-directional communication networks. Therefore, with the help of communication networks, service providers have real-time information about energy supply and demand. Moreover, customers also have real-time price information, based on which they can consume energy in an optimized manner, so that the total energy consumption cost is reduced. In addition to communication networks, several distributed energy generation units (such as solar, wind, and combined heat power) are also integrated to deploy distributed energy management policies. These distributed units are known as micro-grids. Typically, a smart grid consists of the following components – micro-grid, smart meter, renewable energy sources, and plug-in hybrid electric vehicles (PHEVs) [1]. Figure 1.1 depicts a schematic view of the smart grid architecture. Table 1.1 presents the basic differences between the traditional power grid and the smart grid.

    Smart Grid Framework and Communication Model

    Smart grid framework was initially conceptualized by the National Institute of Standards and Technology (NIST) in 2009. Figure 1.2 shows the smart grid framework conceptualized by NIST in its framework version 1.0 (adopted from [2]). The smart grid framework includes several entities – operators, markets, customers, utility providers, generation units, transmission units, and distribution units. The role of operators includes monitoring of the overall smart grid system and implementation of policies, while considering the interests of other entities. On the other hand, utility providers act as middle-persons between the customers and the energy markets. Therefore, it is possible to

    Distribution side includes local substations and customers. Further, the local substations are built with modern technologies to integrate energy from the main power grid with energy generated locally at the substation in a distributed manner. Therefore, the power supply to the customers is done in a semidistributed manner. The local substation reports to the main power grid about the local energy generation and demand from the customers.

    Customers’ homes are equipped with ‘smart meters,’ which send the energy consumption report to the substations. Further, the appliances installed at the customers’ ends are connected to the smart meters, in order to have smart energy monitoring system.

  • Foreword

  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp xxi-xxiv

  • Summary

    The growing concern about climate change and energy conservation necessitates the existing power grid to be modernized. Consequently, bi-directional communication network is placed atop the existing power grid, named as smart grid, with an aim to reduce energy loss and to provide improved energy management. This makes smart grid a cyber-physical system with a strong intertwining between the software, communication, control, and physical components. The cyber-physical property of smart grid necessitates adequate energy and information management, while considering the associated security concerns. Further, millions of smart meters are expected to be deployed to collect real-time status of energy consumption, and the collected information should be processed in real-time. Consequently, smart grid requires support of advanced computing platform and data management tools to manage real-time energy requirements and to process real-time information, respectively. Cloud computing and big data technologies are the topics of interest among the scientific community due to their inherent features of advanced computation and data management.

    Unlike most smart grid books, Sudip Misra and Samaresh Bera in this book focus on the specific challenges that are presented in the smart grid while integrating multiple entities together, which can be potentially solved using cloud computing technology and advanced data management approaches. It covers main aspects of smart grid, touching energy management, information management, and security, by leveraging the advantages of cloud computing technology and advanced data management approaches. The book discusses the important issues, such as demand response, dynamic pricing, load balancing, and smart meter data management, and includes the treatment to deal with such issues. In brief, the attractive features of this book are catalogued below:

  • a) Fundamental concepts of smart grid, cloud computing, and big data analytics are discussed. Additionally, the commonly used mathematical models and tools in this domain are also given concisely.

  • b) Cloud computing technology for smart grid presents a comprehensive discussion on the benefits of cloud computing technology in the smart grid for improved energy management and security.

  • c) Data management approaches mainly focus on the information management in the smart grid.

  • d) Smart grid simulation tools and initiatives worldwide highlight the recent efforts and on-going projects to realize the smart grid technology in a large-scale.

  • 12 - PHEVs: Internet of Vehicles

  • from Part III - Smart Grid Data Management and Applications
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 189-206

  • Summary

    Plug-in hybrid electric vehicles (PHEVs) are attracting the interest of researchers due to their unique features such as real-time demand side management and green technology. PHEVs use a combination of electric energy and traditional fossil fuel. They have an electric motor and an internal combustion engine. It is expected that PHEVs will produce less amount of air pollution compared to traditional vehicles. Moreover, PHEVs can charge and discharge their batteries, depending on the real-time energy status of the grid, which is widely termed as vehicle-to-grid (V2G) and grid-to-vehicle (G2V) transfer.

    Vehicle-to-grid (V2G) transfer is defined as the energy transfer from an electric vehicle to the power grid. In smart grid, the energy supply from generators and demand from its customers are dynamic over time. Therefore, sometimes, the grid may face supply–demand imbalance, which, in turn, may lead to grid failure. Using the concept of V2G transfer, energy can be supplied to the grid from the vehicle when there is a deficit in energy supply to the grid compared to the energy demand from its customers. Therefore, sudden energy demand peaks can be avoided using such technology.

    Vehicles also consume energy to charge their batteries. Consequently, they can charge their batteries when there is a surplus in energy supply to the grid. This energy transfer technology is termed as grid-to-vehicle (G2V) transfer. Therefore, surplus energy can be stored in the PHEVs’ batteries without wasting it. Hence, V2G and G2V techniques play an important role in real-time energy management in the smart grid. It is noteworthy that PHEVs are also integrated with different communication technologies, in order to have real-time energy supply–demand information. Consequently, suitable communication technology is also required for efficient operation of the PHEVs, while integrating them with the smart grid.

    Convergence of PHEVs and Internet of Vehicles

    Internet-of-vehicles (IoVs) are expected to play a major role in real-time information sharing and gathering information about roads and traffic. It is also useful to process, compute, and release the gathered information to other platforms. Based on the received data, the system can guide its users to take appropriate decisions. Moreover, it can provide Internet connectivity to its users as is done in vehicular ad-hoc networks (VANETs).

    Today's PHEVs will become tomorrow's IoVs with the energy management facilities. This is because, in addition to energy management, PHEVs can fulfill the objectives of the concept of IoVs.

  • Preface

  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp xxv-xxviii

  • Summary

    Overview and Goals

    Due to the growing concerns about climate change and energy conservation, there has been a greater focus than ever before on the development of digitalized and green technology for modern power delivery systems – Smart Grid – to the customers. Smart grid is conceptualized as the integration of bi-directional communication networks on the existing electricity networks. With the help of this type of communication networks, service providers and customers have real-time information about the energy consumption and price of energy, respectively. Therefore, service providers can maintain a balance between real-time supply and demand, so as to maximize their revenues. On the other hand, customers can consume energy in an optimized manner to minimize their energy consumption cost. Consequently, different intelligent mechanisms – demand response, dynamic pricing, integration of renewable energy sources, and distributed energy service – are introduced to support the smart grid architecture.

    Concurrently, cloud computing is an emerging technology that facilitates on-demand, real-time service platform dynamically with shared services to its clients. Cloud computing primarily provides three different services: software as a service (SaaS), infrastructure as a service (IaaS), and platform as a service (PaaS). Therefore, clients can utilize cloud resources according to their individual requirements. Another important feature of cloud computing is that it supports the implementation of a distributed architecture.

    Recently, different studies have revealed that cloud computing is a promising technology that can support the smart grid requirements from different perspectives: energy management, information management, and security services. Real-time energy management is the most important feature of the smart grid. Different schemes are introduced for this purpose: demand scheduling, integration of storage devices, cooperative architecture, are a few examples. A micro-grid in smart grid provides electricity to customers in a distributed manner with the help of renewable and non-renewable energy sources. Therefore, all entities (such as customers, service providers, and third parties) require adequate real-time information in order to execute the said schemes. Consequently, it is required to have a common platform from where all the entities can fetch adequate real-time information, which, in turn, necessitates the availability of suitable information management schemes. In the smart grid, a massive number of smart meters is expected to be deployed in order to have real-time information about customers’ energy consumption patterns. Presently, big data management approaches have been popular for processing and storage of massive volumes of heterogeneous data generated at high speeds.

  • 8 - Virtual Power Plant

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 106-117

  • Summary

    In a smart grid, distributed energy generators are expected to play an important role in providing cost-effective and green energy-services to customers. Implementation of wind farms and solar panels are two popular distributed energy generating sources. In addition to this, combined heat power (CHP) and fuel cells are also expected to take part in the distributed energy generation process. However, due to the intermittent behavior of such distributed generators, maintaining reliable energy-services to customers is a big challenge for service providers. Additionally, energy generation from the distributed energy sources are environment dependent. Therefore, it may not be cost-effective if more energy is generated during off-peak periods. Consequently, it is required to have some kind of storage devices that can store energy on a situation basis (i.e., during off-peak hours), and can discharge energy to the distribution side during on-peak hours. In order to meet these requirements, a dynamic energy consumption/distribution platform, called virtual power plant (VPP), is considered.

    Concept of Virtual Power Plant

    A virtual power plant (VPP) is conceptualized as a combination of different distributed energy resources (DERs). Therefore, VPP can be considered a decentralized energy resource system with a large number of small-scale DERs such as solar energy, wind energy, CHPs, fuel cells, and plug-in hybrid electric vehicles (PHEVs). Consequently, it creates a single energy monitoring profile, while considering different parameters characterizing each distributed energy source. Figure 8.1 shows a schematic diagram of a virtual power plant consisting of different energy resources. As a whole, a VPP is capable of providing reliable and cost-effective energy supply to customers. Additionally, a large-scale VPP can be formed with a combination of different small-scale VPPs, as shown in Figure 8.2. Depending on various characteristics, there are two types of VPP – commercial and technical – that will be discussed in this section.

    Commercial VPP

    The concept of a commercial virtual power plant (CVPP) is used for energy trading in energy markets. With the integration of CVPP, energy-market risks can be reduced in order to utilize the distributed energy resources. Consequently, individual DER can benefit from an intelligent market in order to maximize its own revenue. A conceptual view of the commercial virtual power plant (CVPP) is shown in Figure 8.3. The operation of a CVPP consists of three phases – input, CVPP operation, and output. Depending on different inputs, CVPP optimizes revenue from individual DERs and offers different services.

  • 6 - Geographical Load-Balancing

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 81-95

  • Summary

    The distributed architecture of smart grid facilitates micro-grids to provide electricity to the end-users in a distributed manner. Additionally, the micro-grids provide electricity to the customers using a combination of renewable and non-renewable energy sources. Therefore, each micro-grid has distributed generation facilities (such as combined heat power, solar, and wind), which act as the renewable and non-renewable energy sources. Consequently, micro-grids maintain a balance between real-time energy supply and demand to them. In order to have real-time information of the supply and demand, data centers need to be deployed for each micro-grid with bi-directional communication facility. In such a scenario, distributed load-balancing among the energy sources and data servers is required. Load-balancing is a technique by which massive load on a particular micro-grid can be reduced significantly.

    Need for Load–Balancing in Smart Grid

    There is a need to implement load-balancing in a smart grid due to the following reasons.

  • • As micro-grids provide electricity to customers in a distributed manner, the energy management policy implementation also takes place in a distributed manner at the micro-grids’ end. Therefore, it may happen that in a particular micro-grid's area, demand from the customers is very high. Consequently, real-time energy load on that micro-grid is very high, and it has to provide adequate electricity to the customers in order to meet the energy requirements of the latter. As a result, the micro-grid has to buy electricity from the main grid (through a market pricing policy) in order to meet the requirements. However, at the same time, it may happen that real-time demand is very low in another micro-grid. Therefore, there is an energy excess in that micro-grid. Consequently, it sells the surplus energy to the main grid. In such an energy exchange mechanism, a massive amount of energy is lost due to transmission loss. However, with the implementation of the load-balancing scheme, service areas of the micro-grids can be dynamically changed in order to maintain the balance between real-time energy supply and demand.

  • • Similar to the energy scarcity problem in a micro-grid's area, real-time information propagation may lead to the creation of a massive data load on the servers. With the help of bi-directional communication in the smart grid, customers communicate with the service providers for energy consumption. Therefore, real-time information propagation at the data centers also increases due to the massive energy demand from the customers.

  • 7 - Dynamic Pricing

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 96-105

  • Summary

    With the implementation of bi-directional communication facility, service providers have real-time information about energy consumption by the customers and energy production by the generators. In such cases, dynamic pricing based mechanisms are more appealing than traditional static pricing based ones. Service providers decide real-time price based on the demand from customers or supply from the generation side or both the supply and demand. Consequently, customers have the opportunity to use electricity according to their preferred real-time price decided by the grid. Additionally, service providers maintain the real-time supply–demand curve by controlling the real-time price. When demand increases, the price may be increased to reduce the extra load from the grid, and vice-versa. Customers also schedule their energy consumption depending on the real-time price decided by the grid. Therefore, dynamic pricing policy is one of the important features of the smart grid that provides dynamic load balancing in the smart grid.

    Deployment of Dynamic Pricing in Smart Grids

    As the name suggests, in dynamic pricing, the price of energy varies over time according to the energy supply or demand or both. To implement the dynamic energy pricing policy, we need to address several challenges that will be discussed in this section.

    Determination of actual timeslot

    We discussed earlier that there are three different time-slots in the smart grid – on-peak, mid-peak, and off-peak. Based on the past days’ behavior, state (on, mid, or off-peak) of a particular time-slot is determined. For example, the office hour can be considered as on-peak, the morning and evening can be considered as mid-peak, and night can be considered as off-peak. However, in real-time, the situation in the grid may be different. For example, energy demand on a particular day may be different from another day. Moreover, as the smart grid is equipped with renewable energy sources, which are intermittent in nature, energy supply to the grid may also vary over multiple days. Therefore, it is not possible to blindly say that a particular time period is mid-peak, or on-peak, or off-peak. Consequently, it is important to define suitable strategies to determine the state of a time-slot.

Page 111 of 342