Smart Metering: Infrastructure, Methodologies, Applications, and Challenges

By VIjay K. Sood

Smart Metering: Infrastructure, Methodologies, Applications and Challenges combines the fundamentals of smart meters in smart grids with the latest advances and technologies in advanced smart infrastructure. With a strong focus on practical guidance and applications, this book examines the design and implementation of smart meters, as well as cyber security and data management challenges. Following an introduction to smart grid architecture, the book details design elements of smart meters to enable them for specific applications, such as recording the energy consumption of users, load forecasting, resilience enhancement and energy theft detection. A deep dive into smart meter data analytics is then presented, accompanied by load forecasting methods and their advantages and challenges. Subsequent chapters also discuss outage management, fault identification and other applications of smart meters, including power network connection verifications. This is a comprehensive resource on smart metering and a valuable read to students, researchers and engineers interested in power systems engineering, smart grids, and smart energy technologies.

• Discusses advanced architecture in the context of establishing smart meters in smart grids for enhanced operation and data utilization
• Provides detailed discussions on smart meter data analysis
• Explores the design of smart meters and possible implementation of AI, ML, and other advanced methodologies to enhance the functions of power systems using smart meter data

• Language: English
• Publisher: Elsevier Science
• Release date: Feb 26, 2024
• ISBN: 9780443155864

Book preview

Preface

The smart grid relies on modern digital technology for enhanced measurement, stable operation, control, monitoring, and protection based on widespread information sharing, faster communication, and computation. The Internet and smart devices have influenced many aspects of our lives and revised the overall process of data collection, handling, and management. This smart grid runs the power grid using advanced technology, communication architectures, faster computing facilities, and faster controllers so that the security and stability of the grid can be improved and interruption rates can be minimized. With the smart grid architecture, dynamic grid operation is possible.

Within the physical smart grid infrastructure, the smart devices and sensors check and process the data and then communicate to the operation centers to take necessary corrective actions. Smart meters (SMs) come under the first category. Using smart meters, multiple functions can be performed in the smart grid architecture to secure and stabilize the operation of the power network. Smart meters include different sophisticated measurements and computational hardware, software, communication, and calibration capabilities. In a smart grid architecture for interoperability facility, smart meters are designed with various functions to store and communicate data according to different grid codes. Smart meters help secure the grid information, restrict intruders from attacking the system data, manage demand-side load, reduce outages, enhance resilience, and detect energy theft. Hence, a detailed discussion on the operational infrastructure is needed to implement smart meters in smart grids, applications of smart meters, security aspects of smart meters, and the challenges faced in handling smart meter data. It is also essential to discuss the different methodologies used to access and process the smart meter data.

This book is about how to guide and address the significant issues inside the most overlooked sector of the electrical grid: electrical distribution. Various issues, as mentioned earlier, are discussed in detail: present smart meter and its different attributes, methodologies used to access and process the data from a smart meter for secure and stable operation of the power system, and challenges in the smart grid that can be mitigated using smart grid and its operational limitations and enhancement.

This book will aid researchers working in the smart grid, smart meter, smart architecture, power system resiliency enhancement, power theft, demand side management, cyber security, and communication systems. The researchers will also be able to find various interesting problems that have been solved previously and the ones that remain. Similarly, postgraduate students will benefit from the book. The book will be useful to industrial practitioners looking for practical problem statements that are commercially practical and safe for consumers. Policymakers will draw perspectives from both researchers and industrial experts to design policies or update or overhaul existing policies. Each potential user requires data and knowledge in both technical and social domains.

To the best of our knowledge, none of the existing books related to smart grid and smart meter consider these individuals' perspectives. Although a few of the available books are focused on specific technical subdomains of smart meter and smart grid, they lack the wider perspective to meet the requirements of all potential users. This book is unique and one of a kind, as it has information that is neither easily accessible nor available in a concise format. The techno-socioeconomic knowledge incorporated in this book serves as a standard literature for all. A brief description of the topics covered in various chapters is given in the following paragraphs.

Chapter 1: In this chapter, a comprehensive survey on the architectural requirements to set up SMs in a smart grid environment, SM data analytics, various mathematical approaches implemented to conduct the studies, and developed models is provided. A brief discussion of the key challenges and security analysis with the application of SM is provided. A thorough discussion of the opportunities, advantages, and shortcomings of individual processes is also supplied to encourage researchers to conduct future studies in this area.

Chapter 2: This chapter discusses the design structure of a smart meter to measure the power consumption of electrical appliances. The energy measurement system consists of voltage and current sensors and is used to send data to an Arduino Uno, which is a universally known microcontroller board.

Chapter 3: This chapter covers the basics of load demand forecasting, an overview of statistical and data-driven methodologies, and the associated challenges. Furthermore, a proposal of the most suitable machine learning method is presented through a numerical example. The primary goal of this chapter is to raise the knowledge of load demand forecasting and the issues that come with it, which will aid in the correct planning of the power system using smart methodologies. It also guides fraternities and practitioners about the current and future trends of AI technologies in forecasting studies.

Chapter 4: The effects of cyberattacks on traditional power system protection philosophies such as overcurrent relay and distance relay are discussed in this chapter. In addition, a deep learning (DL) network-based supervisory solution is introduced to protect the protective relays from cyberattacks. The results of this approach show that the model can detect cyberattacks with a high degree of accuracy.

Chapter 5: This chapter covers an overview of cyberattacks that are expected against the smart grid infrastructure's smart meters, the many types of smart meters that are available, and detecting methods to thwart the attacks.

Chapter 6: The goal of this chapter is to show how detection and location techniques can be designed using smart metering. Among the several existing approaches, this chapter concentrates on state estimation-based ones, supplying a critical analysis of the challenges and advantages to guide related decision-making processes.

Chapter 7: This chapter discusses the constraints and future needs of smart meter applications for distribution system fault localization computation. The benefits of the smart meter data for fault location identification are tested on the standard IEEE 33-bus distribution system.

Chapter 8: In this chapter, the task of naming the strategic locations for installing measuring instruments as an optimization problem to reach a trade-off between minimizing the monitoring cost and maximizing the SE accuracy against cyberattacks is proposed. The effectiveness of the proposed approach was confirmed under realistic constraints of phasor measurement unit (PMU) channel limitations for benchmark test systems.

Chapter 9: The outage management system (OMS) is used by utilities to manage and respond to power outages efficiently. The OMS is designed to streamline the outage management process, from the detection and reporting of outages to the final restoration of power. However, the smart grid framework incorporates initiative-taking measures, advanced analytics, and intelligent decision-making to hasten the restoration process. With real-time monitoring, analytics and artificial intelligent tools are integrated to minimize the outage time. The intelligent decision-making part focuses on fault isolation, best resource allocation, crew scheduling, and coordination with stakeholders. A detailed discussion about the impact of outages in the distribution system, the need for OMS, available methodologies, limitations of existing techniques, and advancements to develop intelligent OMS is provided.

Chapter 10: This chapter focuses on the architecture of the smart grid system for precision agriculture and the systems deployed for microclimate monitoring and soil characterization. In smart grid systems, for microclimate monitoring, air temperature and humidity are sensed using IoT-based systems.

Chapter 11: For various applications, high-end computation, handling, and management of large data are essential. The smart meters may face challenges in doing so. This chapter explores the main obstacles that energy suppliers and utility companies must overcome to efficiently manage and use the enormous volume of data produced by smart meters. The importance of tackling the problems with smart meter data processing is highlighted in this chapter.

Chapter 12: This chapter presents the conclusion and future prospects.

Vijay K. Sood

Monalisa Biswal

Chapter 1 Smart meters in smart grid

Fanidhar Dewangana; Saniya Siddiquia; Monalisa Biswala; Vijay K. Soodb    a Department of Electrical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

b Department of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON, Canada

Abstract

A smart meter (SM) is an essential component of the smart grid. From the collected data from the SM, power grid operation, control, monitoring, protection, and efficiency can be enhanced. To conduct multiple operations and for the development of the sustainable smart power grid, the role of SM data is crucial. In the past years, research is ongoing to handle and exploit the SM. Load forecasting, outer management, load analysis, energy theft detection, demand side management, load reallocation, data management, bad data detection, and fault localization are some of the research topics being focused on. In the future, the research scope on other prime areas such as big data analysis, information of energy injected/consumed through integrated sources, data privacy, data security, and handling of the energy information during the transition state of hybrid energy sources-based systems will be more. The objective of this chapter is to provide a comprehensive survey on the architectural requirements to establish SM in a smart grid environment, SM data analytics, various mathematical approaches implemented to conduct the studies, and developed models. A brief discussion of the key challenges and security analysis with the application of SM is provided. A thorough discussion of the opportunities, advantages, and shortcomings of individual processes is provided so that future research in this area can be conducted.

Keywords

Smart grid; Smart meters; Demand side management; Energy theft; Load forecasting; Outage management; Cybersecurity

1 Introduction

Carbon footprint is continuously increasing, climate change is becoming overwhelming, and hydrocarbon resources are still being exploited for the generation of electrical energy in a major way. Hydrocarbon energy resources are finite and will exhaust within a few years. So, large-scale integration of renewable energy sources, optimization of energy consumption, and enhanced energy management are necessary for the smooth transition toward a stable operation of next-generation power networks. In other words, the world needs a smart grid for the reliable, efficient, and secure operation of the evolving power grid that can eventually contribute to the reduction of the carbon footprint.

According to the Electric Power Research Institute (EPRI), A smart grid is one that incorporates information and communication technology into every aspect of electricity generation, delivery and consumption in order to minimize environmental impact, enhance markets, improve reliability and service, and reduce cost and improve efficiency.

In a conventional grid (Fig. 1), the generation of power is centralized, and the nonrenewable-based energy resources are the major contributors for power generation. The power, energy, and information flow are unidirectional. The availability of limited information to the utility or consumer and restricted control over the energy flow and grid elements are common issues with a conventional grid [1]. Conversely, a smart grid (Fig. 2) has decentralized renewable energy sources integrated at different voltage levels, bidirectional flow of energy as well as information, and better control over energy flow and grid elements.

Fig. 1

Fig. 1 Conventional power network.

Fig. 2

Fig. 2 Smart power grid.

The conventional grid is not very efficient, reliable, or secure. It is also not suitable for quick outage detection, restoration after disturbances, prevention of energy theft, energy management, etc. On the other hand, the smart grid can address all these issues and even allow customer involvement, which further optimizes their energy consumption. Ultimately, the smart grid has the capability to make major contributions to the future reduction of the carbon footprint and to deal with climate change issues [2].

For the deployment of the smart grid, a specific infrastructure, viz., an infrastructure that will support digital technology, a two-way communication network, an operation technology (OT), an information technology (IT), etc., is needed. To implement this kind of infrastructure, an advanced communication architecture like home area network (HAN), neighbors’ area network (NAN), wide-area network (WAN), and various communication technologies such as Wi-Fi, ZigBee, PLC, fiber optics cable, etc. are required. Along with this, a data concentrator is required, which collects data from the smart sensors or devices such as intelligent electronic devices (IEDs) and smart meters. The data from these smart devices can be accessed through smart sensing networks and acquired from the data management system present at the utility end [3].

The smart grid has three major sections. The first section of the smart grid includes the smart energy system, with two-way flow of energy and data, and distributed generators. The second section consists of a smart transmission system with smart substations and monitoring centers. The third section is the smart distribution with energy storage components [4]. Other important functions are advanced monitoring, control, and protection [5]. But the important components of the smart grid are those that facilitate access and analysis of data collected from individual sections and the smart devices that transmit data through a communication architecture to diverse sections. These smart devices can be smart meters, smart sensors, measurement units, and/or data management systems.

The smart meter (SM) is an intelligent meter that records energy consumption at certain intervals and sends data to the utility center for analysis. Unlike a conventional energy meter, the smart meter has a communication interface, a user interface, a data storage unit, and a real-time clock [1]. As the smart meter supports two-way flow of information, there is no need for manual reading of meter data so that automatic billing is possible [3]. A smart meter can record energy consumption as well as measure other parameters such as system voltage, current, frequency, power factor, and active and reactive power consumption. All the home appliances can be controlled and monitored by smart meters. By real-time consumption data, the utility companies can effectively manage the energy demand. There are several other possible applications of SMs which are described in further sections.

1.1 Earlier works

In distribution systems, SMs have been replacing conventional meters for several years. As discussed earlier, SMs have many advantages that make them ideal for a wide variety of applications. In this chapter, earlier smart metering approaches are discussed, and collective information is gathered. The data collected from SMs are evaluated through different machine learning (ML) techniques and algorithms depending on their intended use. In most studies, the raw data from SMs are preprocessed by removing outliers. After the filtered data are collected, various methods are used to process it. Most of the articles previously used statistical or parametric approaches to present their findings. Nowadays, most researchers use artificial intelligence techniques. Table 1 shows various methodologies used for smart meter-based applications. In this context, applications such as theft detection, fault detection and outage management, demand side management, load scheduling, load forecasting, and dynamic pricing are considered.

Table 1