Cloud Computing in Smart Energy Meter Management E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
List of Contributors
Preface
1 Fundamentals of Smart Meter
1.1 Introduction
1.2 Advanced Metering Infrastructure (AMI)
1.3 Types of Smart Meters
1.4 Meter Standards
1.5 Testing and Maintenance of Smart Meters
1.6 AMI Data Management Services
1.7 Demand Response
1.8 Cloud Services
1.9 Security in Smart Meters
1.10 Case Studies
Conclusion
References
2 Empowering Consumers and Utilities for a Smarter Future: The Pivotal Role of Advanced Metering Infrastructure (AMI) in Smart Meter Technology
2.1 Introduction
2.2 AMI Architecture
2.3 How AMI Works?
2.4 Architecture and Components of AMI
2.5 AMI Protocols—Standards and Initiatives
2.6 Home Area Network
2.7 Neighborhood Area Network (NAN)
2.8 Functions of Head End Systems
2.9 Meter Data Management
2.10 AMI System Design/MDAS/MDMS
2.11 Metering Head End Design
2.12 Conclusion
References
3 Demystifying Smart Meters: Powering the Next-Generation Grid
3.1 Introduction
3.2 Exploring the Emerging Functionalities of Smart Meters
3.3 Smart Metering Infrastructure
3.4 Communication Technology for Smart Metering Applications
3.5 Regulatory Framework for Smart Meter Deployment
3.6 Benefits of Smart Meters in Grid Modernization
3.7 Hardware of Smart Meter
3.8 Smart Meters and Consumer Empowerment
3.9 Smart Meter Using Internet of Things Technology
3.10 A Meter Using Cloud and Edge Computing
3.11 Wide-Area Network for Smart Energy Meters
3.12 Smart Meter in Internet of Energy (IoE)
3.13 Implementation Strategies for Smart Meters in IoE
3.14 Future Prospects and Innovations in Smart Meter Technology
3.15 Conclusion
References
4 Communication and Networking in Advanced Metering
4.1 Olden Days Electric Meter
4.2 Government Initiative for Smart Meter
4.3 Introduction: Networking and Communication
4.4 IoT with Smart Meters
4.5 Connectivity of Smart Meters
4.6 Electric Utility Commission Architecture
4.7 Technology Selection in Advanced Metering Architecture
4.8 Case Study of Smart Meter Using RF
4.9 Why RF is Better Than Other Technologies Like 2G, 3G, and 4G
4.10 Concise Use of RF and WAN
4.11 Conclusion
References
5 Meter Data Acquisition Using Cloud Computing
5.1 Introduction
5.2 Literature Review
5.3 Methodology and Implementation of Smart Meters Using Cloud Platform
5.4 Machine Learning Algorithms for Advanced Metering
5.5 Applications of Cloud Data Acquisition for Smart Meters
5.6 Implementing OSS Layer for Smart Meters
5.7 Challenges and Opportunities of Smart Metering with Cloud-Based Data Acquisition
5.8 Future Directions of Smart Metering with Cloud-Based Data Acquisition [11]
5.9 Conclusion and Summary of Key Findings
References
6 Smart Energy Meter Data Management in the Cloud Hadoop, SQL, HBase
6.1 Introduction to Data Management
6.2 Benefits of Data Management
6.3 Significant Benefits of Smart Energy Meter Data Management
6.4 Challenges of Data Management
6.5 Solutions and Strategies for Effective SEM Cloud Data Management
6.6 Challenges in Data Management for Smart Energy Meter
6.7 Importance of Data Management for Smart Energy Meter
6.8 Data Management for Smart Energy Meter Architecture
6.9 Role of Cloud Computing in Data Management for Smart Energy Meter
6.10 Data Management for Smart Energy Meter in the Cloud
6.11 Smart Energy Meter Data Management Using Hadoop
6.12 Storing and Accessing Smart Energy Meter Data Using SQL Databases
6.13 Storing and Accessing Smart Energy Meter Data Using HBase
6.14 Modern Technology for a Modern Grid
6.15 Benefits of Using a Managed Service in the Cloud
6.16 Capabilities of the Highest Order in Data Analytics and Machine Learning
6.17 Case Studies of Successful SEM Cloud Data Management
6.18 Future Trends and Advancements in SEM Cloud Data Management
Conclusion
References
7 Smart Energy Meter Data Processing and Billing
7.1 Billing System
7.2 Big Data Analytics in Smart Metering
7.3 Data Flow From Smart Meter to Billing System
7.4 Security in Smart Metering System
7.5 Integrating Legacy Metering Infrastructure Into Smart Metering Systems
7.6 Conclusion and Future Scope
References
8 Smart Meter Security—Fraud Detection in Power Theft
8.1 Introduction
8.2 Different Aspects of Smart Meter Security
8.3 Data Privacy and Encryption
8.4 Authentication and Authorization
8.5 Firmware and Software Updates
8.6 Physical Security
8.7 Network Security
8.8 Remote Access Control
8.9 Device Identity Management
8.10 Anomaly Detection
8.11 Regulatory Compliance
8.12 User Understanding and Directions
8.13 Conclusion
References
9 Cybersecurity in ICT-Enabled Smart Metering Systems: Addressing Challenges and Implementing Solutions
9.1 Introduction
9.2 Cyber Attack in Smart Meters
9.3 Blockchain in Smart Meters
9.4 IoT-Enabled Smart Meters
9.5 Navigating the Complex Landscape of Smart Grid Communications
9.6 Securing Smart Meters
9.7 Conclusion
References
10 Challenges in Smart Metering
10.1 Introduction
10.2 Growth of Smart Meter
10.3 Challenges in the Replacement of Existing Meters with Smart Meters with Prepayment
10.4 Technology Challenges in Smart Metering
10.5 Operational Challenges
10.6 Case Study
References
11 Quality of Service (QoS) Protocol in Advanced Metering Infrastructure (AMI)
11.1 Introduction to QoS in AMI
11.2 Background
11.3 Smart Grid System
11.4 Proposed Research Contribution
11.5 Survey Related to QoS of AMI With Smart Grid
11.6 Proposed Deep Learning-Based Optimization Model
11.7 Modeling a System and Formulating a Problem
11.8 Strategy Performed Along with Terms of Effectiveness as Well as Quick Confluence
11.9 Results, Discussion, Findings, and Analysis
11.10 Conclusion
References
12 Web Services/Mobile Application to Monitor the Smart Meter Data
12.1 Introduction
12.2 Comparison of Kilowatt-Hour Meter and Smart Meter
12.3 Mobile Applications for Smart Meter Data
12.4 Comparison of Different Factors
12.5 Conclusion
12.6 Future Scope
References
13 Advanced Smart Prepaid Meter
13.1 Introduction
13.2 Literature Review
13.3 Cost-Efficient Futuristic M2M Smart Prepaid Meter
13.4 Smart Metering Results
13.5 Conclusions with Future Research Scopes
References
14 Edge Computing and Cyber-Physical System in Smart Meter
14.1 Introduction
14.2 Literature Survey
14.3 Smart Meter Components and Their Architecture
14.4 Smart Meter Data Analytics on Edge Devices
14.5 Smart Metering Infrastructure
14.6 IoT-Enabled Smart Meter
14.7 An Overview of Cyber-Physical System
14.8 Case Study and Application
14.9 Challenges and Future Research Scopes
14.10 Conclusion
References
15 Case Study on Real-Time Smart Meter
15.1 Introduction
15.2 Literature Review
15.3 Case Study 1: Smart Energy Monitoring
15.4 Case Study 2: Power Theft
15.5 Conclusion
Acknowledgments
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Intelligent data collection devices in smart grid.
Table 7.2 Outline of communication infrastructure in smart grid.
Chapter 10
Table 10.1 A study between conventional and smart grid.
Table 10.2 Issues and solutions of AMI system.
Chapter 13
Table 13.1 Primary aspect of the literature review.
Table 13.2 Challenges experienced by different prepaid smart meter projects ar...
Table 13.3 Remote connect/disconnect outcomes.
Chapter 14
Table 14.1 Description of smart meter components.
Table 14.2 Use cases of smart meter data analytics.
Table 14.3 Smart energy meter challenges.
Table 14.4 Challenges in edge computing.
Table 14.5 Research challenges.
List of Illustrations
Chapter 1
Figure 1.1 Advanced metering infrastructure (AMI).
Figure 1.2 Classification of smart meters.
Figure 1.3 Program interface that records the smart meter energy pulses for ei...
Figure 1.4 Data requirements for smart meter data analytics [12].
Figure 1.5 Meter data storage.
Figure 1.6 Outcome of meter data management services [26].
Figure 1.7 Implementation of demand response [6].
Figure 1.8 Examples of service modules in Landis+Gyr EMEA [17].
Figure 1.9 SAP HANA cloud platform for energy [27].
Figure 1.10 Cloud computing-based QoS-aware HQS model [25].
Figure 1.11 Details of AMI implementation in Houston, USA [13].
Figure 1.12 Details of AMI implementation in Maine, USA [13].
Figure 1.13 Details of AMI implementation in Oklahoma, USA [13].
Figure 1.14 Details of AMI implementation in Washington, USA [13].
Figure 1.15 Details of AMI implementation in Chattanooga, USA [13].
Figure 1.16 Details of AMI implementation in Northern Florida, USA [13].
Figure 1.17 Details of AMI implementation in Oregon, USA [13].
Figure 1.18 Details of AMI implementation in North Carolina, USA [13].
Chapter 2
Figure 2.1 General advanced metering infrastructure.
Figure 2.2 Advanced metering infrastructure architecture.
Figure 2.3 Advanced metering infrastructure (AMI) working.
Figure 2.4 Direct connected meter.
Figure 2.5 Smart meter–central nerve system of AMI.
Figure 2.6 Smart grid–home area network.
Figure 2.7 RF point-to-point network.
Figure 2.8 Neighborhood area network.
Chapter 3
Figure 3.1 General smart energy meter.
Figure 3.2 Unveiling the residential smart metering system.
Figure 3.3 Smart meter smart metering infrastructure.
Figure 3.4 Smart meter architecture.
Figure 3.5 AMI blockchain.
Figure 3.6 Internal structure of a three-phase four-wire smart meter.
Figure 3.7 IoT-based smart meter connection.
Figure 3.8 Functional diagram of LoRA energy meter.
Chapter 4
Figure 4.1 Electric smart meter.
Figure 4.2 Rise in the use of smart meters.
Figure 4.3 Network technologies.
Figure 4.4 IoT architecture.
Figure 4.5 Network communication.
Figure 4.6 Process of conventional energy meter.
Figure 4.7 Process of smart meter.
Figure 4.8 Wide area network (WAN).
Figure 4.9 Smart meter skeleton model [31].
Figure 4.10 Accessing smart meter.
Figure 4.11 Electric meter grid.
Chapter 5
Figure 5.1 Smart metering system in various domains [3].
Figure 5.2 Cloud acquisition smart metering system [29].
Figure 5.3 Smart metering data acquisition and prediction system [15].
Chapter 6
Figure 6.1 Technology in cloud data management.
Figure 6.2 Structure of data handling in SEM.
Figure 6.3 Benefits of data management.
Figure 6.4 Challenges of data management.
Figure 6.5 Solution and strategy for effective cloud data management.
Figure 6.6 Data management architecture.
Figure 6.7 Future trends and advancements in cloud data management.
Chapter 7
Figure 7.1 Conventional analog electromechanical-type energy meter.
Figure 7.2 Digital energy meter installed in consumer premises nowadays.
Figure 7.3 Digital type time-of-use energy meter.
Figure 7.4 Simple approach for automatic electricity billing system.
Figure 7.5 Block diagram of smart meter billing system.
Figure 7.6 Conventional energy meter versus smart energy meter.
Figure 7.7 IoT-based smart meter billing system.
Figure 7.8 Big data analytics.
Figure 7.9 Functional components of metering infrastructure.
Figure 7.10 Classifications of SG threats by sources.
Chapter 9
Figure 9.1 Smart metering infrastructure.
Figure 9.2 Attacks on smart meters.
Figure 9.3 Blockchain framework for smart meters.
Figure 9.4 Smart meter security accuracy.
Figure 9.5 IoT-enabled smart meters.
Figure 9.6 IoT-enabled smart meters in home appliances.
Chapter 10
Figure 10.1 Conventional and smart energy meter.
Figure 10.2 Evolution of energy meter and its functions.
Figure 10.3 Smart grid.
Figure 10.4 Smart meter.
Figure 10.5 Smart meter system design concerns.
Figure 10.6 Challenges with the system’s maintenance.
Figure 10.7 Data transfer problems with a smart meter system.
Figure 10.8 Hopping on the mesh network.
Chapter 11
Figure 11.1 AMI system architecture.
Figure 11.2 Smart grid systems.
Figure 11.3 CDF of all interconnections to DAPs aggregated.
Figure 11.4 Comparison of delay satisfaction probability.
Figure 11.5 CDF for the quantity of hops.
Figure 11.6 CDF of the MC versus NC traffic’s waiting delays.
Chapter 12
Figure 12.1 DC energy meter [1].
Figure 12.3 Kilowatt-hour meter [1].
Figure 12.2 AC energy meter [2].
Figure 12.4 Smart meter [2].
Figure 12.5 Distortion-based approach [19].
Figure 12.6 Automatic meter reading [7].
Figure 12.7 Hierarchical model [3].
Figure 12.8 Ensemble detection model [6].
Figure 12.9 Data-driven detection [21].
Figure 12.10 Management of smart meters [18].
Figure 12.11 Comparison between kilowatt-hour meter and smart meter [11].
Figure 12.12 Energy management [20].
Figure 12.13 Cost management [20].
Figure 12.14 Mobile applications [15].
Figure 12.15 Software working [22].
Figure 12.16 Flo application [22].
Figure 12.17 Revogi application [22].
Figure 12.18 Complexity of usage [15].
Chapter 13
Figure 13.1 Net metering.
Figure 13.2 Prepaid meter block diagram.
Figure 13.3 Global trend on energy meter type [3].
Figure 13.4 Add-on device block diagram.
Figure 13.5 TTL to RS485 converter.
Figure 13.6 Arduino Uno.
Figure 13.7 LoRa schematic diagram.
Figure 13.8 Electronic relay.
Figure 13.9 Arduino power adopter.
Figure 13.10 NESEM design architecture.
Figure 13.11 LoRa star topology.
Figure 13.12 GSM module.
Figure 13.13 Node MCU.
Figure 13.14 SD card schematic.
Figure 13.15 Hardware design of add-on device.
Figure 13.16 NESEM hardware design.
Figure 13.17 Add-on device flow chart.
Figure 13.18 NESEM smart prepaid metering algorithm.
Figure 13.19 AI personalized energy recommendation.
Figure 13.20 AI algorithm for short-term load prediction.
Figure 13.21 Security algorithm.
Figure 13.22 Test bed.
Figure 13.23 Test bed results.
Figure 13.24 Real-time testing results.
Figure 13.25 Web interface with energy consumption details.
Figure 13.26 Consumer notification result.
Figure 13.27 Short-term load prediction.
Figure 13.28 Short-term load prediction result.
Chapter 14
Figure 14.1 Hardware components of a smart meter.
Figure 14.2 Smart meter architecture.
Figure 14.3 Advantages of edge computing.
Figure 14.4 Edge computing architecture.
Figure 14.5 Architecture of IoT-enabled power management system.
Figure 14.6 Data flow in a smart grid.
Figure 14.7 Smart grid cyber-physical system.
Figure 14.8 Hierarchical structure of smart meter data.
Figure 14.9 Applications of smart metering.
Figure 14.10 Edge vs cloud computing.
Chapter 15
Figure 15.1 (a) Generation, transmission, and distribution of electricity [1]....
Figure 15.2 (a) Smart meter prototype architecture, (b) Interfacing energy met...
Figure 15.3 (a) Complete setup of the smart energy monitoring system. (b) Ener...
Figure 15.4 Waveform in DSO indicates detection of pulses.
Figure 15.5 Program flowchart.
Figure 15.6 (a) Units and cost estimation displayed on LCD 15. (b) Limit reach...
Figure 15.7 (a) Message (limit reached) sent to the user through GSM, (b) Bill...
Figure 15.8 Project methodology adopted.
Figure 15.9 Block diagram of the proposed power theft and transformer health m...
Figure 15.10 Android architecture.
Figure 15.11 Flowchart of the proposed work.
Figure 15.12 Flowchart for power theft detection.
Figure 15.13 Flowchart for oil level detection.
Figure 15.14 Flowchart for temperature detection.
Figure 15.15 Flowchart for intruder detection.
Figure 15.16 (a) No power theft indication. (b) Power theft detection.
Figure 15.17 (a) Oil level detection. (b) Detection of high temperature of the...
Figure 15.18 (a) Intruder detection. (b) Power theft app.
Figure 15.19 (a) Messages received by electricity board officials. (b) Detecti...
Figure 15.20 (a) Capturing of the photo during power theft. Actual photos of t...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Begin Reading
About the Editors
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Cloud Computing in Smart Energy Meter Management
Edited by
G. Senbagavalli
T. Kavitha
N. Amuthan
and
Ferdin Joe John Joseph
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394193653
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List of Contributors
G. SenbagavalliAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karnataka, India
T. KavithaNew Horizon College of EngineeringVisvesvaraya Technological UniversityBangalore, Karnataka, India
S.T. Bibin ShaliniKuwait UniversitySabah Al-Salem University CampusSafat, Kuwait
N. AmuthanAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karnataka, India
M. SathyaNadar Saraswathi College of Engineering and TechnologyTheni, Tamil Nadu, India
Nisha C. RaniThe Oxford College of EngineeringVisvesvaraya Technological UniversityBangalore, Karnataka, India
M. Marsaline BenoSt. Xavier’s Catholic College of EngineeringNagercoil, Tamil Nadu, India
N. SivakumarAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karnataka, India
R. SaravananAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karnataka, India
N. Palani KarthikVellore Institute of TechnologyChennai, Tamil Nadu, India
Behara Mohith RVellore Institute of TechnologyChennai, Tamil Nadu, India
Vallidevi KrishnamurthyVellore Institute of TechnologyChennai, Tamil Nadu, India
S.P. Angelin ClaretSRM Institute of Science and TechnologyChennai, Tamil Nadu, India
B. PrashanthiSRM Institute of Science and TechnologyChennai, Tamil Nadu, India
B. Priya EstherSRM Institute of Science and TechnologyChennai, Tamil Nadu, India
Priya BoopalanZF Active safety and electronics LLCMichigan, USA
P. VelrajkumarVels Institute of Science, Technology and Advanced StudiesChennai, Tamil Nadu, India
S. JeyadeviMangayarkarasi College of EngineeringMadurai, Tamil Nadu, India
S. KalyaniLarsen & Toubro LimitedChennai, Tamil Nadu, India
B. Devi VighneswariThe Oxford College of EngineeringVisvesvaraya UniversityBangalore, Karnataka, India
Kothai Andal C.AMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karanataka, India
J. Selvin Paul PeterSRM Institute of Science and TechnologyChennai, Tamil Nadu, India
C. Rajesh BabuGalgotias UniversityUttar Pradesh, India
B. Priya EstherSRM Institute of Science and TechnologyChennai, Tamil Nadu, India
R. SelvamathiAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karanataka, India
V. IndragandhiVellore Institute of TechnologyVellore, Tamil Nadu, India
N. AmuthanAMC Engineering CollegeVisvesvaraya Technological UniversityBangalore, Karnataka, India
Robin Rohit VincentPresidency UniversityBangalore, Karnataka, India
Nisha F.CMR UniversityBangalore, Karnataka, India
V. Rose PriyankaT. John Institute of TechnologyVisvesvaraya Technological UniversityBangalore, Karnataka, India
Jarin T.Jyothi Engineering CollegeAPJ Abdul Kalam Technological UniversityKerala, India
Muniraj RathinamP.S.R. Engineering CollegeAnna UniversityTamil Nadu, India
Ulaganathan M.P.S.R. Engineering CollegeAnna UniversityTamil Nadu, India
Aswin V.M.Jyothi Engineering CollegeAPJ Abdul Kalam Technological UniversityKerala, India
Jithin K. JoseJyothi Engineering CollegeAPJ Abdul Kalam Technological UniversityKerala, India
Ezhilarasi P.Electronics and Computer ScienceUniversity of Southampton, SouthamptonUnited Kingdom
Ramesh L.Dr. M.G.R. Educational and Research InstituteChennai, Tamil Nadu, India
Balamurugan J.Tamil Nadu Energy Development AgencyChennai, Tamil Nadu, India
Jens Bo Holm-NielsenAalborg UniversityAalborg, Denmark
Revathi M.Bharath Institute of Higher Education and ResearchChennai, Tamil Nadu, India
Udayakumar K.Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennai, Tamil Nadu, India
Prabhakaran M.V.Dhanalakshmi Srinivasan College of Engineering and TechnologyMamallapuram, Tamil Nadu, India
Yasha Jyothi M. ShirurBNM Institute of TechnologyVisvesvaraya Technological UniversityBangalore, Karnataka, India
Bindu S.BNM Institute of TechnologyVisvesvaraya Technological UniversityBangalore, Karnataka, India
Jyoti R. MunavalliBNM Institute of TechnologyVisvesvaraya Technological UniversityBangalore, Karnataka, India
Preface
The book Cloud Computing in Smart Energy Meter Management explores smart metering concepts with the flow of power consumption from consumer to utility centre. It includes the advanced metering infrastructure which contains the meter installation, meter advising, commissioning, integration, master data synchronization, billing, customer interface, complaints and resolution. This book provides a collection of high-quality research work that addresses the broad challenges in smart energy metering while replacing the existing conventional meters in developing countries. This book contains 15 chapters exploring the electrical metering concepts merged with IoT and Cloud computing.
Chapter 1 outlines the fundamentals of smart meters, advanced metering infrastructure, data management services and cloud services in AMI. It explores the case studies of development of AMI system in developed and developing countries.
Chapter 2 highlights the Advanced Metering Infrastructure (AMI) which serves as the backbone of smart grid technology, empowering both consumers and utilities through real-time energy data. It gives granular insights into usage patterns enable informed decision-making, fostering efficiency, sustainability, and grid stability. Consumers gain personalized feedback, optimizing consumption and lowering bills.
Chapter 3 explores the smart meters, vital components of modern power grids. It dives into their design, hardware, and communication protocols, explaining how they enable real-time data collection and analysis. Leveraging the Internet of Things (IoT), smart meters collect various data points like voltage, current, and energy usage, which are processed and transmitted to external systems. This information unlocks economic, social, and environmental benefits, paving the way for a more efficient and sustainable energy future.
Chapter 4 provides an overview of advanced metering communication and networking, highlighting EUCA, Kubernetes, RF technology selection, and a comparative analysis of various technologies to meet the escalating demand for smart electric grid meters.
Chapter 5 outlines the manifold applications of deploying smart meters while projecting future trajectories in this domain. The pivotal focus herein lies in the evolution of communication networks essential for data acquisition in smart metering, leveraging cloud technology intertwined with machine learning, deep learning, and an OSS layer-based smart meter framework. Additionally, it delves into a comprehensive exploration of existing literature and presents a compelling case study to underscore these concepts.
Chapter 6 delves into the realm of smart energy meter data management in the cloud, exploring the intersection of cutting-edge technologies such as Hadoop, SQL, and HBase. By leveraging the scalability and flexibility of cloud computing, coupled with the power of distributed computing frameworks like Hadoop, organizations can unlock the full potential of their energy data.
Chapter 7 starts with an outline of conventional energy meters and its development towards smart metering system. This chapter presents the infrastructure of smart metering systems available in various countries, significance of smart billing system, and the role of big data analytics on billing system. Additionally, this chapter describes IoT based smart billing system and security threats with solutions in smart metering frame work. The description of integration of legacy metering infrastructure into smart metering system is also embraced as a major part of this chapter. This chapter will give a better understanding of smart metering systems and their developments in the present era to the readers and will be highly beneficial for the academicians to take up research work in this domain.
Chapter 8 highlights the significance of smart meter security, specifically focusing on its role in identifying and preventing the power theft and fraud. Furthermore, it emphasizes the need for a multi-faceted approach to smart meter security, which includes the secure communication protocols, tamper-proof hardware, and continuous monitoring systems.
Chapter 9 provides the information on potential threats, defence strategies, and emerging trends that will likely affect the market. This research attempts to give policymakers, energy providers, and technology developers with the information and resources necessary to successfully negotiate the complex web of smart meter security through a thorough examination of the most recent research and industry practices.
Chapter 10 represents a turning point in the development of energy use and management. With smart meters providing real-time data and enabling users to makeeducated decisions about their energy usage, smart meters become more important tools associeties struggle with the pressing need to shift towards sustainable energy practices. It also represents the challenges that call for creative solutions and cooperative effortsacross industries and sectors, from technological barriers to complicated legislative frameworks of smart meters.
Chapter 11 delves into the intricate mechanisms governing data acquisition points (DAPs) and the strategic placement thereof on existing utility poles. This chapter deals with exploration of the critical role played by QoS protocols in optimizing the performance of advanced metering infrastructures.
Chapter 12 extensively explores the enhanced landscape of energy meter monitoring through IoT integration, spotlighting the advantages inherent in smart meters. These technological advancements empower users to access and manage power consumption, fostering energy conservation and cost reduction. It discusses the historical evolution of energy metering, scrutinizing the limitations of traditional meters and emphasizing the emergence of smart meters. Privacy-preserving metering approaches, automatic meter reading applications, and ensemble detection models contribute to a robust and secure energy ecosystem.
Chapter 13 investigates the recent developments in prepaid metering technology and the development of cost-effective prepaid meters with advanced features. This chapter explore the design and development of a Network Enabled Smart Energy prepaid Meter (NESEM) with advanced prepaid functionalities at a cost-effective level.
Chapter 14 seeks to unravel the intricacies of deploying these cutting-edge technologies in the realm of smart meters. This chapter embarks on a journey through the fusion of edge computing and cyber-physical systems, exploring their symbiotic relationship within the context of smart metering networks.
Chapter 15 presents an insightful Case Study on Real-Time Smart Meters, offering readers a practical knowledge and real-world impact of associated devises. This chapter provides the readers an understanding of the challenges, opportunities, and tangible benefits associated with the deployment of Real-Time Smart Meters. This chapter also deals with the power theft, a prototype for power theft detection has been developed, which is cost-effective, accurate in measuring the required parameters, and promptly communicates with relevant officials. This system is aimed at preventing power theft, and its effectiveness has been demonstrated through successful detection of power theft incidents, monitoring of oil levels in transformers, temperature detection, and intruder detection.
1Fundamentals 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 costs, quicker outages besides restorations, and better inventory control [6].
1.2.3 Features of Smart Meters in AMI
Electromagnetic compatibility: The meter and NIC must be resistant to quick transient bursts, electromagnetic HF fields, and electrostatic discharge up to and including 35 kV. The meter must be made such that electromagnetic disturbances that are transmitted or emitted, as well as electrostatic discharge, cannot affect it. Meters must pass an electromagnetic compatibility type test [8].
Modes of metering: Forwarded only: Any active energy that is being exported in this mode must be considered as imported energy and must be noted in the forward-only register. Bidirectional: In this manner of metering, both import and export energy recording shall be applied.
Communication modules: A smart meter must include one plug-in type communication module or network interface card (NIC) to link the device to the HES: i. Communication module/NIC type 1: RF-based and appropriate for the RF canopy provider. ii. Communication module/NIC type 2 (LTE 4G with 3G and 2G fallback, following Indian Telecom Standards): RF and cellular communication module. a. The meter must have separate indications for local and remote communication on the display. b. The communication module must be housed in a casing that can be inserted directly into the meter [8].
Local reconnection mechanism: Only for disconnections due to overload and load control limit, meters must be able to locally reconnect load switches. The meter will attempt to reconnect the load up to a preset time with a preset interval (the preset times are adjustable). After locking lock out, it will wait for half an hour if the usage is still higher than the preset limits. HES commands must be able to remotely connect and detach the relay. The typical connect/disconnect cycle must not be interrupted by the remote reconnect. If a relay malfunction, meaning the connect/disconnect operation of the relay does not occur because the contacts have been welded together or for any other reason, this must be recorded as an event in the non-rollover compartment. The same must be delivered to HES as a warning. Local communication should be subordinate to remote command [8].
Mechanism for reconnection: Except in cases of overcurrent and load control limits, reconnecting must be done from HES. Reconnection must be feasible using a hand-held device (CMRI) locally using the appropriate security in the event of communication or HES failure. The prepayment profile must be followed while reconnecting a prepayment meter [8].
Payment mode: The following payment methods are configurable for the meter: Post-payment mode and prepayment mode. Any modification to the payment method must be recorded in an event with a date and time stamp. The prepayment option must be accomplished by the server or HES. Unless otherwise indicated, after payment will be the default mode of metering [8].
1.3 Types of Smart Meters
For households, there are primarily two kinds of smart meters [9]: smart meters that employ advanced metering infrastructure (AMI) use communications hub and wireless connectivity to send data. Some smart meters need wired connections for data transmission, such as automated meter reading (AMR) smart meters. AMRs include a SIM that sends data through a mobile network.
1.3.1 Residential Smart Meter Types [9]
For residences, there are primarily two types of smart meters: SMETS1 and SMETS2 are the smart meter equipment technical specifications (SMETS).
SMETS1: These are the initial generation of smart meters, and not all energy providers can use them. SMETS1 meter might need to be changed if energy providers have changed.
SMETS2: These smart meters, which are of the second generation, are made to work with all energy providers. They provide more sophisticated features, such as the capability to change rates remotely. The most secure data is used by SMETS2 meters.
1.3.2 Consumer-Based Smart Meter Types
Smart single-phase whole current meter, smart three-phase whole current meter, low tension current transformer (LT-CT) smart meter, high-tension current transformer (HT-CT) smart meter, and distribution transformer (DT)-operated meter as shown in Figure 1.2.
Smart single-phase whole current meter: This meter needs two wires to transmit power: a phase wire and a neutral wire. Because it is less effective at distributing alternating current, a single-phase energy meter is less efficient than a three-phase energy meter [10].
Figure 1.2 Classification of smart meters.
Smart three-phase whole current meter: Three-phase meters typically come in two varieties: triple-phase and triple-wire meters. This fusion consists of two single-phase meters. It makes use of a powerful horseshoe magnet. Three-phase, four-wire meter: Three coils and three aluminum discs make up this arrangement. It is used to calculate the amps of a three-phase motor’s kWh [10].
1.4 Meter Standards
1.4.1 COSEM/DLMS
A highly well-liked and frequently used standard for many applications, including smart energy metering, is device language message specification (DLMS)/companion specification for energy metering (COSEM) [2]. The DLMS User Association established and maintained the set of standards, which have been included in the IEC 62056 family of standards.
The DLMS User Association (DLMS UA) created as well as maintains the DLMS/COSEM suite of standards, which may be used with different types of energy metering. The aforementioned has remained combined into the IEC 62056 family of principles for the exchange of electricity meterreadings, pricing, and load control information.
The DLMS UA’s Blue Book contains information about COSEM and OBIS. The bottom layers, applications, and communication characteristics of the DLMS/COSEM are included in the book. It contains specified application layer and communication protocols with the help of an object-oriented model data that can be utilized over many communications media. It contains specially designed interface classes for its naming scheme and structure.
The COSEM object model illustrates the meter’s operation as seen through its interfaces. The naming system known as the Object Identification System, or OBIS, is used to identify interface items like clocks, timetables, registers, profiles, and relationships. The transport layer is maintained apart from the COSEM or application layer. As a result, it is simple to adapt the various communication tools employed in advanced meter reading and advanced meter infrastructure, such as PLC, RF mesh, etc. The data stored in the objects are converted into messages through the application layer protocol known as DLMS. It controls the encryption and decryption of lengthy communications before transmitting these messages in blocks known as application protocol data units (APDUs).
IS 15959 Indian Standard (Data Exchange for Tariff and Load Control for Electricity Meter Reading) as well as the Indian-related specifications are issued by BIS. To enable India-specific employment for the safe communication of energy metering data in an open way: part II—current smart meters or part III—transformer smart meters are utilized in aggregation through the basic standards for smart meters.
Data modeling, messaging, and transportation are the three distinct layers that make up the basic three-step strategy, each of which is tightly connected. The protocol has previously been used to stereotype christening and message layouts for interoperability of static meters utilizing meter reading instruments, hand-held units, head-end schemes for AMR, GSM networks, and PSTN.
1.4.2 Reasons for Adopting COSEM in Various Nations, Consortiums, and Utilities
Tens of thousands of “standard” items are available in the DLMS; utilities must select the ones that are most suited to their portraying and effective requirements. The data feature must also be well stated, including the data type, the access permissions, the number of bits employed, etc. Even for the same use case, the right data model must be chosen from the many alternatives offered by DLMS. For instance, time-of-use metering may be done using either the activity calendar interface class or slated interface class of DLMS. Manufacturers could implement differently, which would cause problems with interoperability, except that the edge session to be utilized is very explicitly described through the relevant COSEM [2].
Although there are 65,535 potential interface classes, only the 89 that are utilized need to be provided. Similarly, out of a potential 281,474 billion OBIS codes, about 4,398 billion are set aside for standardization. COSEM is created by country standards committees, consortiums, utilities, or manufacturers such as TNB (Malaysia), Tepco (Tokyo), or BIS (India), which issued the three IS 15959 standards that can be utilized for manufacturer, consortium-specific goals and nation.
1.4.3 Types of Meters and the Corresponding Parameters
For COSEM procedures and services, India’s IS 15959—part 1 chose three kinds of electricity meters [2]:
Category A: for usage in distribution transformer centers and sub-station feeds. This category’s parameters are presented for energy audit and accounting purposes, for usage at cadence banks, and for web borders.
Category B: additionally appropriate for an availability-based tariff (ABT) system. The parameters for energy import and export are mentioned under this category.
Category C: intended for usage by users of HV-PT and CT operated and LV-CT operated. Customers who use the grid for electricity should use parameters.
The category B meter is advised for customers who additionally contribute electricity to the grid. For type C meters, for instance, the parameters given embrace: prompt parameters, wedge load survey/profile parameters, daily profile, accounting/billing parameters, name plate information, and constraints such as a programmable real-time clock and demand integration time are examples of general purpose parameters. There are conditions for events, which may be non-rollover-, control-, current-, voltage-, power-, transaction and other voltage, pattern cover opening, and current related events.
The extra standards for smart meters are covered by IS 15959—part 2. In home demonstration, smart meters may identify messages, events, and data to clients without solicitation as among the features and services triggered by the services of DLMS/COSEM. It also covered firmware updates, connect/disconnect service, communication profiles, and advanced security profiles. Parameter lists such as Indian event reference tables, billing profile, programmable parameters, block load and daily load profile, instantaneous parameters, and name plate information are all examples of advanced security profiles.
Smart meters that follow IS 16444 fall under the following categories:
Single-phase ac static direct connected watt-hour smart meters fall under category D1. Three-phase ac static direct connected watt-hour smart meters fall under category D2. D1 and D2 have separate OBIS codes and parameter tables.
For transformer-operated smart meters, IS 15959—part 3 handles the extra requirements. The attributes and offerings resemble IS 15959—part 2.
Smart meters that adhere to IS 16444 (part 2) fall under the following categories:
Transformer-operated HV/LV customer pulses (3Ph-3 wire/3Ph-4wire CT-PT, 3Ph-4wire CT) are under category D3. Boundary, bank, ring, and ABT meters that are operated by transformers (3Ph-3 wire and 3Ph-4 wire CT-PT customer meters) are under category D4.
1.5 Testing and Maintenance of Smart Meters
Smart meters may be tested [11] in a variety of ways. Data from USB, Ethernet, radio frequency (RF), signal on power line transmission(PLC), etc., is sent to readers and visually interpreted from the digital readout. This process makes use of an infrared pulse for analysis and readout reasons. Each pulse measures the amount of energy it contains according to its Kh factor, which is a fixed number of watt-hours per pulse. The infrared pulses were picked up by optical detectors on this test platform’s automated system. The software was used to record the pulses and time-stamp them as illustrated in Figure 1.3. During the test, the total number of pulses bourgeoned by the Kh factor is the amount of electrical energy utilized. Then, each smart meter reading is contrasted with the result of a power analyzer that has been independently calibrated.
Interactive maintenance of the directly connected meter as well as the data concentrator maintenance of the directly connected meter, the maintenance mode [7], provides comprehensive administration and analysis options. Functions include (i) remote firmware updates, (ii) display and updating of existing device settings and parameters, (iii) control various meter lists inside the concentrator, (iv) checking and downloading parameters, (v) reading position, happening logs, and message indicators, (vi) downloading time-of-use slabs for load shedding and tariff transferring, and (vii) downloading the basic, temporal parameters, and data dictionary will initialize the concentrator.
Figure 1.3 Program interface that records the smart meter energy pulses for eight separate meters [11].
1.6 AMI Data Management Services
1.6.1 AMI Data Requirements From Smart Meters
The utility installing the meters affects the AMI data’s properties. Delay lenient processes and different grid edge were used to AMI facts to determine the minimal granularity to enhance distribution system models. To identify AMI capabilities like topology rebuilding, load forecasting, load disaggregation, phase identification, etc., additional variables gathered from their current AMI infrastructure are used as shown in Figure 1.4 [12].
Sampling time interval: AMI meters collect data at sampling times of no more than an hour and provide that information to utilities or consumers at least once per day.
Alternating between average and instantaneous data is a programmed function of smart meters. Examples of these readings include voltage. Even though the industry tends to choose the past for portraying tenacities, there is still considerable uncertainty in this area, and several utilities have stated a desire to know which data format may be added advantageous.
Figure 1.4 Data requirements for smart meter data analytics [12].
Reactive power data: Voltage and active power data are often recorded by smart meters today [13]. Modern meters have enhanced sensing capabilities and can offer more data like harmonics and weight power factor; however, several conveniences have not activated these functions because of the cost or other issues.
Data resolution: Utilities may keep compressed data since their voltage and power readings with fewer decimal values in their long-standing databases to lessen the amount of data that has to be stored.
Time synchronization: The analysis of data compiled starting all smart pulses on a feeder is a key component of many smart meter algorithms. Understanding how time delays affect the interior clock of each meter is a crucial factor to take into account.
Non-controllable but frequent causes of error, such as arbitrary noise, missing data, and meter bias have also been examined by data analytics concerning the AMI algorithms.
1.6.2 Meter Data Management Services
Smart meter data received from PLC is recorded daily, and the record is maintained for at least 1 year. Hourly basis data is maintained in the record for 90 days. The event log is maintained with a minimum of 200 entries. Concentrator data received by GPRS/LAN was recorded daily and the record for n days on an hourly basis, and the record is maintained for n days. The event log is maintained with a minimum of n days’ entries. The n value depends on the data storage of the service provider. Central system data received by LAN is recorded on a daily basis and the record is maintained for at least 1 year and at hourly basis and the record is maintained for at least 1 year. The event log is maintained with a minimum of 1 year of data entries. All these AMI data are stored in the cloud, and the data is retrieved through the ERP system during data analysis for further applications as described in Figure 1.5.
AMI data are stored and managed in clouds and utilized for load analysis and load forecasting with probabilistic techniques. Stored load data is used to design electricity pricing schemes with respect characterization of customers and demand response. Stored load data is also used for other applications such as data privacy, data compression, connection verification and outage management and using data analytics techniques such as dimension reduction, low-rank matrix, time-series analysis, clustering, deep learning [19], outlier detection etc., as shown in Figure 1.6.
Figure 1.5 Meter data storage.
Figure 1.6 Outcome of meter data management services [26].
1.7 Demand Response
Through the use of demand response, the utility can remotely disconnect some of the customer’s equipment or reduce the load on their premises. Through rewards and sanctions, the DR program aims to increase customer involvement. The demand response programs’ main success aspect is client engagement. Here the utility performs the function of transferring the load of specific client equipment, which is mutually agreed upon, from peak hours or higher market price hours to off-peak hours or lower market price hours. This equipment includes heaters, air conditioners, machinery, huge pumps, etc. The advantages of DR are as follows: avoiding the extra generation, transmission, and distribution capacity, preventing brownouts and blackouts, and avoiding the use of the most expensive power plants during peak hours [14].
The value that demands response (DR) creates relies on the period of the reaction. DR operates throughout an assortment of spans, from rapid retorts in flashes to long-run adjustments in everyday behavior. A supply curve modeling framework founded on large samples of smart meter load shapes is created to depict the availability of system-level grid services from distributed resources in response to this dynamic analysis. A new taxonomy for DR services and an analytical methodology divide these services into four main categories to make it easier to compare the cost and utility of having a variety of flexible loads: shimmy, shape, shift, and shed [15]. Shape captures “load-modifying DR”—DR that reshapes consumer load profiles through pricing response or on behavioral campaigns—with warnings ranging from months to days.
Shift is DR, which promotes shifting energy use from periods of peak demand to periods of the day when renewable supply is abundant. The shift might level out net load ramps connected to solar energy production daily trends.
Shed refers to loads that, at the statewide level, locally in great load locations, and on the scattering system, with a range in dispatch prior notification times, can be reduced to offer peak capacity and sustain the system in emergency or contingency situations.
Shimmy uses loads to dynamically modify the system’s demand to reduce short-run ramps and disruptions across periods of a few seconds to an hour. Shimmy is a rapid DR service type that excels at handling transient variations in the net load on a minutes-to-hours (“load following”) and seconds-to-minutes (“regulation”) timescale.
