Cloud Computing in Smart Energy Meter Management
Description
Cloud Computing in Smart Energy Meter Management equips you with essential insights and practical solutions for effectively managing smart meter data through cutting-edge technologies like artificial intelligence and cloud computing, making it an invaluable resource for anyone looking to enhance their understanding of modern energy management.
Cloud Computing in Smart Energy Meter Management presents a structured review of the current research on smart energy meters with artificial intelligence and cloud computing solutions. This book will help provide solutions for processing and analyzing the massive amounts of data involved in smart meters through cloud computing. Readers will learn about data storage, processing, and dynamic pricing of smart energy data in the cloud, as well as smart metering concepts dealing with the flow of power consumption from consumer to utility center. It offers an in-depth explanation of advanced metering infrastructure (AMI) which includes meter installation, meter advising, commissioning, integration, master data synchronization, billing, customer interface, complaints, and resolution. In smart cities, components in household energy meters are fitted with sensors and can interconnect with the Internet of Things to measure power consumption with an automated meter reading. This book also acts as a new resource describing new technologies involved in the integration of smart metering with existing cellular networks. Cloud Computing in Smart Energy Meter Management provides knowledge on the vital role played by artificial intelligence and cloud computing in smart energy meter reading with precise evaluations.
More details
Other editions
Additional editions
Persons
G. Senbagavalli, PhD, is an associate professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over 18 years of experience in teaching and research. She has published three patents, two book chapters, and 15 papers in national and international conferences and journals. She is also a lifetime member of the International Society for Technology in Education and the Institution of Electronics and Telecommunications Engineers. Her research interests include image and video processing, computer vision, machine learning, and VLSI Design.
T. Kavitha, PhD, is a professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over twenty years of experience in teaching and research. She has published five patents, two book chapters, 15 papers in international journals, and over 30 papers in national and international conferences. She is also a lifetime member of the International Society for Technology in Education and the Institution of Engineers (India). Her research interests include wireless networks, wireless sensor networks, information security, Internet of Things, deep learning, and machine learning.
N. Amuthan, PhD, is a professor at AMC Engineering College, Bengaluru, India with over 22 years of teaching experience. He has over 26 publications in reputed national and international conferences, workshops, and journals and serves as a reviewer for various national and international journals. He is also a member of numerous national and international committees and societies. His research interests include power electronics, energy conservation, auditing, renewable energy sources, and implementation of the cloud for integration at the national level.
Ferdin Joe John Joseph, PhD, is an assistant professor in the Department of Information Technology at the Thai Nichi Institute of Technology, Bangkok with over a decade of teaching experience. He has several publications in international journals and conferences and has been designated as a Most Valuable Professional with Alibaba Cloud. His areas of research include deep learning, Internet of Things, and Cloud AI.
Content
List of Contributors xvii
Preface xxiii
1 Fundamentals of Smart Meter 1 G. Senbagavalli, T. Kavitha and S.T. Bibin Shalini
1.1 Introduction 1
1.2 Advanced Metering Infrastructure (AMI) 3
1.3 Types of Smart Meters 6
1.4 Meter Standards 8
1.5 Testing and Maintenance of Smart Meters 11
1.6 AMI Data Management Services 12
1.7 Demand Response 14
1.8 Cloud Services 16
1.9 Security in Smart Meters 20
1.10 Case Studies 21
Conclusion 27
References 28
2 Empowering Consumers and Utilities for a Smarter Future: The Pivotal Role of Advanced Metering Infrastructure (AMI) in Smart Meter Technology 31 N. Amuthan, M. Sathya and Nisha C. Rani
2.1 Introduction 32
2.2 AMI Architecture 36
2.3 How AMI Works? 39
2.4 Architecture and Components of AMI 41
2.5 AMI Protocols-Standards and Initiatives 45
2.6 Home Area Network 47
2.7 Neighborhood Area Network (NAN) 52
2.8 Functions of Head End Systems 55
2.9 Meter Data Management 56
2.10 AMI System Design/MDAS/MDMS 56
2.11 Metering Head End Design 57
2.12 Conclusion 62
References 63
3 Demystifying Smart Meters: Powering the Next-Generation Grid 67 M. Marsaline Beno, N. Sivakumar and R. Saravanan
3.1 Introduction 67
3.2 Exploring the Emerging Functionalities of Smart Meters 69
3.3 Smart Metering Infrastructure 71
3.4 Communication Technology for Smart Metering Applications 76
3.5 Regulatory Framework for Smart Meter Deployment 79
3.6 Benefits of Smart Meters in Grid Modernization 80
3.7 Hardware of Smart Meter 82
3.8 Smart Meters and Consumer Empowerment 85
3.9 Smart Meter Using Internet of Things Technology 85
3.10 A Meter Using Cloud and Edge Computing 87
3.11 Wide-Area Network for Smart Energy Meters 88
3.12 Smart Meter in Internet of Energy (IoE) 89
3.13 Implementation Strategies for Smart Meters in IoE 90
3.14 Future Prospects and Innovations in Smart Meter Technology 92
3.15 Conclusion 93
References 95
4 Communication and Networking in Advanced Metering 99 N. Palani Karthik, Behara Mohith and Vallidevi Krishnamurthy
4.1 Olden Days Electric Meter 100
4.2 Government Initiative for Smart Meter 101
4.3 Introduction: Networking and Communication 104
4.4 IoT with Smart Meters 107
4.5 Connectivity of Smart Meters 108
4.6 Electric Utility Commission Architecture 111
4.7 Technology Selection in Advanced Metering Architecture 119
4.8 Case Study of Smart Meter Using RF 122
4.9 Why RF is Better Than Other Technologies Like 2G, 3G, and 4G 127
4.10 Concise Use of RF and WAN 129
4.11 Conclusion 131
References 133
5 Meter Data Acquisition Using Cloud Computing 137 S. P. Angelin Claret and B. Prashanthi
5.1 Introduction 138
5.2 Literature Review 139
5.3 Methodology and Implementation of Smart Meters Using Cloud Platform 142
5.4 Machine Learning Algorithms for Advanced Metering 146
5.5 Applications of Cloud Data Acquisition for Smart Meters 150
5.6 Implementing OSS Layer for Smart Meters 152
5.7 Challenges and Opportunities of Smart Metering with Cloud-Based Data Acquisition 155
5.8 Future Directions of Smart Metering with Cloud-Based Data Acquisition 159
5.9 Conclusion and Summary of Key Findings 164
References 166
6 Smart Energy Meter Data Management in the Cloud Hadoop, SQL, HBase 169 B. Priya Esther, Priya Boopalan and P. Velrajkumar
6.1 Introduction to Data Management 170
6.2 Benefits of Data Management 174
6.3 Significant Benefits of Smart Energy Meter Data Management 177
6.4 Challenges of Data Management 178
6.5 Solutions and Strategies for Effective SEM Cloud Data Management 182
6.6 Challenges in Data Management for Smart Energy Meter 185
6.7 Importance of Data Management for Smart Energy Meter 186
6.8 Data Management for Smart Energy Meter Architecture 187
6.9 Role of Cloud Computing in Data Management for Smart Energy Meter 187
6.10 Data Management for Smart Energy Meter in the Cloud 188
6.11 Smart Energy Meter Data Management Using Hadoop 189
6.12 Storing and Accessing Smart Energy Meter Data Using SQL Databases 191
6.13 Storing and Accessing Smart Energy Meter Data Using HBase 192
6.14 Modern Technology for a Modern Grid 193
6.15 Benefits of Using a Managed Service in the Cloud 194
6.16 Capabilities of the Highest Order in Data Analytics and Machine Learning 195
6.17 Case Studies of Successful SEM Cloud Data Management 198
6.18 Future Trends and Advancements in SEM Cloud Data Management 200
Conclusion 202
References 204
7 Smart Energy Meter Data Processing and Billing 207 S. Jeyadevi and Kalyani
7.1 Billing System 208
7.2 Big Data Analytics in Smart Metering 218
7.3 Data Flow From Smart Meter to Billing System 224
7.4 Security in Smart Metering System 228
7.5 Integrating Legacy Metering Infrastructure Into Smart Metering Systems 233
7.6 Conclusion and Future Scope 236
References 236
8 Smart Meter Security-Fraud Detection in Power Theft 239 B. Devi Vighneswari and Kothai Andal C.
8.1 Introduction 240
8.2 Different Aspects of Smart Meter Security 241
8.3 Data Privacy and Encryption 243
8.4 Authentication and Authorization 245
8.5 Firmware and Software Updates 247
8.6 Physical Security 248
8.7 Network Security 250
8.8 Remote Access Control 252
8.9 Device Identity Management 254
8.10 Anomaly Detection 255
8.11 Regulatory Compliance 257
8.12 User Understanding and Directions 259
8.13 Conclusion 260
References 261
9 Cybersecurity in ICT-Enabled Smart Metering Systems: Addressing Challenges and Implementing Solutions 263 J. Selvin Paul Peter, C. Rajesh Babu and B. Priya Esther
9.1 Introduction 264
9.2 Cyber Attack in Smart Meters 265
9.3 Blockchain in Smart Meters 266
9.4 IoT-Enabled Smart Meters 273
9.5 Navigating the Complex Landscape of Smart Grid Communications 280
9.6 Securing Smart Meters 283
9.7 Conclusion 287
References 288
10 Challenges in Smart Metering 291 R. Selvamathi, V. Indragandhi and N. Amuthan
10.1 Introduction 292
10.2 Growth of Smart Meter 294
10.3 Challenges in the Replacement of Existing Meters with Smart Meters with Prepayment 300
10.4 Technology Challenges in Smart Metering 306
10.5 Operational Challenges 313
10.6 Case Study 314
References 315
11 Quality of Service (QoS) Protocol in Advanced Metering Infrastructure (AMI) 319 Robin Rohit Vincent, Nisha F. and Rose Priyanka
11.1 Introduction to QoS in AMI 320
11.2 Background 321
11.3 Smart Grid System 324
11.4 Proposed Research Contribution 325
11.5 Survey Related to QoS of AMI With Smart Grid 326
11.6 Proposed Deep Learning-Based Optimization Model 327
11.7 Modeling a System and Formulating a Problem 338
11.8 Strategy Performed Along with Terms of Effectiveness as Well as Quick Confluence 342
11.9 Results, Discussion, Findings, and Analysis 343
11.10 Conclusion 346
References 346
12 Web Services/Mobile Application to Monitor the Smart Meter Data 349 Jarin T., Muniraj Rathinam, Ulaganathan M., Aswin V. M. and Jithin K. Jose
12.1 Introduction 349
12.2 Comparison of Kilowatt-Hour Meter and Smart Meter 359
12.3 Mobile Applications for Smart Meter Data 362
12.4 Comparison of Different Factors 365
12.5 Conclusion 367
12.6 Future Scope 368
References 368
13 Advanced Smart Prepaid Meter 371 Ezhilarasi P., Ramesh L., Balamurugan J. and J.B. Holm-Nielsen
13.1 Introduction 372
13.2 Literature Review 379
13.3 Cost-Efficient Futuristic M2M Smart Prepaid Meter 386
13.4 Smart Metering Results 407
13.5 Conclusions with Future Research Scopes 415
References 416
14 Edge Computing and Cyber-Physical System in Smart Meter 419 Revathi M., Udayakumar K. and Prabhakaran M. V.
14.1 Introduction 420
14.2 Literature Survey 422
14.3 Smart Meter Components and Their Architecture 424
14.4 Smart Meter Data Analytics on Edge Devices 427
14.5 Smart Metering Infrastructure 428
14.6 IoT-Enabled Smart Meter 432
14.7 An Overview of Cyber-Physical System 434
14.8 Case Study and Application 438
14.9 Challenges and Future Research Scopes 442
14.10 Conclusion 448
References 449
15 Case Study on Real-Time Smart Meter 453 Yasha Jyothi M. Shirur, Bindu S. and Jyoti R. Munavalli
15.1 Introduction 454
15.2 Literature Review 458
15.3 Case Study 1: Smart Energy Monitoring 461
15.4 Case Study 2: Power Theft 468
15.5 Conclusion 482
Acknowledgments 483
References 484
About the Editors 487
Index 489
1
Fundamentals of Smart Meter
G. Senbagavalli1*, T. Kavitha2 and S.T. Bibin Shalini3
1AMC Engineering College, Visvesvaraya Technological University, Bangalore, Karnataka, India
2New Horizon College of Engineering, Visvesvaraya Technological University, Bangalore, Karnataka, India
3Kuwait University, Sabah Al Salem University City, Safat State, Kuwait
Abstract
A cloud-based smart metering infrastructure supports the management of smart meter readings, the automation of future distribution grids, and their intelligent monitoring and control. Smart metering's cloud-based software architecture aims to create cutting-edge services for managing the smart grid. Lately, several countries have started to use state-of-the-art smart meters and advanced metering infrastructures (AMI) to boost the energy industry's efficiency in the distribution sector. A cloud-based system provides the necessary interfaces to distribution grid services and simultaneously allows communication with smart meters. Many apps may be built on top of the cloud to provide communication with smart meters.
Keywords: Cloud architecture, cloud services, smart metering infrastructure
1.1 Introduction
The advantages of smart meters for the environment will be highlighted via the COVID-19 environmental theme. Smart meters' role is to lower the carbon footprint. The importance of AMI in outage management systems and service restoration is driven by persistent power quality concerns. Energy theft losses highlight the necessity of effective AMI. Both the development of smart grids and smart cities are two significant themes that might create new business prospects for AMI. The importance of smart cities in emergencies is highlighted by COVID-19. Smart grids are an important component of energy infrastructure in smart cities and a source of income for AMI. The increasing global interest in demand response (DR) has brought attention to AMI as a crucial enabler. Big data and data analytics are essential for maximizing AMI. Internet of Things (IoT), blockchain, and AI play important roles in improving AMI efficiency, and cloud computing transforms the market for smart meters. Consumers desire multiple tariffs and prepaid smart solutions. Government policy for deploying micro-grid solutions. Market rivalry and demand for IT solutions increase as a result of the privatization of power sectors [1]. Electro-mechanical meters gave way to static or electronic meters throughout time, and advanced metering infrastructure (AMI) gave way to automated meter reading (AMR). IT systems, including the distribution management, outage management system, geographic information system (GIS), enterprise ERP, billing system, and customer information system require links to be established with AMI. Also, load connect, load disconnect, and load verification in demand response programs are carried out through smart meters with interested customers and under the necessary rules [2].
On request from HES, upon event trigger (such as interference detection or supply failure, etc.) or by a schedule, AMI systems monitor, gather, and calculate data, assess energy consumption, regulate, and interact with metering equipment. Rate metering and monitoring based on energy can be enabled for time of day (TOD), critical peak pricing (CPP), and real-time pricing (RTP). Usage by using two-way communication to give the user information about consumption patterns and alarm messages, advanced metering infrastructure (AMI) consists of energy consumption meters (smart meters), a database service called the MDMS (meter data management system), and a head-end system (HES) between utilities and customers through two-way communication channels. AMI includes load disconnect switch in smart meters as a control element in the utility [2]. At the customer interconnection points, there is a smart meter that delivers not only revenue information but also power and power-quality information for all devices [3].
Smart meters are sensors that measure many characteristics, including how much electricity is spent. The major methods of communication are power line carrier (PLC), RF mesh (6 LoPAN), and occasionally GPRS through SIM modules. An RF mesh "canopy" is set up in many cities around the world so that different smart devices, such as smart street lighting systems, smart electricity meters, switched capacitor banks, and ring main units, can reliably, securely, and with two-way communication, communicate to their respective Head End Systems. The Head End or IoT Platform must be notified right away of any spontaneously reported irregularities from field devices (such as tampering or supply outages), and remedial signals like disconnecting meters or dispatching local staff must be set up [4, 22]. Numerous worldwide standard-setting organizations, including ETSI, NIST, IETF, CEN, IEEE, CENELEC, IEC, DLMS UA, ITU, and others, remain working together to develop standards for smart meters and grids.
Do Smart Meters Consume Energy Generated at Home That Is Renewable?
Traditional meters are only equipped to record consumption; therefore, they cannot account for any energy that a family generates. With a smart meter, you can determine how much energy you generate in your house, whether you already have solar panels or are intending to add them. If there is an excess that you might sell back to the grid, the smart meter will also determine whether or not there is one. However, because this is a rare demand, providers have been sluggish to put systems in place to accommodate it [5].
1.2 Advanced Metering Infrastructure (AMI)
1.2.1 Foundational Elements of AMI
AMI is made up of several hardware and software parts, each contributing to the measurement of energy use and the transmission of data about energy usage to utility providers and consumers as shown in Figure 1.1. The main technological elements of AMI are as follows [6]:
Smart meters are advanced metering devices with the ability to gather data on energy usage over time and transmit it to the utility via fixed communication networks. They can also receive data from the utility, such as pricing signals, and transmit it to the consumer.
Advanced communication networks that permit two-way communication make it possible for smart meters to provide information to utility companies and vice versa.
Data concentrator units (DCUs) and the control center hardware are used in the meter data acquisition system to collect meter data through a messagelinkage and deliver it through MDMS.
Figure 1.1 Advanced metering infrastructure (AMI).
The host system receives, stores, and analyzes metering data in the meter data management system (MDMS).
Every meter and adapter has a power line carrier (PLC) module or a GSM/ GPRS module that, for GSM/GPRS communication, connects directly to the central system or via a concentrator for PLC communication [7].
The primary duties of master meters are as follows [7]:
- Periodically record the power usage figures and store these values in profiles
- Report any electricity outages
- Keeping track of electrical status and alarm data
- Offer printable tariff switching tables
- Offer a disconnector that allows the customer's premises to be disconnected and reconnected locally or remotely
- Support the smart meter's in-house display with recent usage information
- Act as a conduit for messages to be sent starting the efficacy of the smart meter
- Afford a message interface (or interfaces) for communicating with the in-house display, operating the disconnector remotely, and reading the power and slave meter consumption figures remotely.
The meter completely schedules the recording of the measurement data (electricity) into profiles. The profiles are kept locally in the non-volatile memorial by the cadence. The PLC communication between the data concentrator and the PLC module is based on industry-standard protocols (IEC61334). When PLC is not effective (technically or economically), GSM/GPRS communication (IEC 62056 series) is used as a substitute. To ensure optimum dependability, the concentrator keeps a duplicate of the most recent sections of the energy values profile, the daily values profile, and the event logs in its buffer. Each meter node has such a buffer. The concentrator automatically retrieves the missing values of the respective meters during excellent communication circumstances if there are misplaced ideals in the buffer (due to momentary communication issues for data), the CS rarely needs to get in touch with the meter directly and regularly since, whenever it connects the concentrator, it has access to all the pertinent data [7].
1.2.2 Benefits of AMI
Benefits to system operation are mainly related to decreased meter readings and related management and administrative assistance, enhanced utility asset management, quicker energy theft detection [24], and simpler outage management.
Benefits for client amenities are mostly concerned with providing customers with a variety of TOD tariff alternatives, detecting meter failures early, improving billing accuracy, expediting the restoration of service, flexible billing cycles, and creating customer energy profiles to target energy efficiency/demand response programs.
Benefits to the utility's bottom line include lower apparatus and apparatus maintenance costs, lower support...
