Smart Grids and Big Data Analytics for Smart Cities

412 Pages·2021·15.315 MB·English

by  Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chun Sing Lai
Loi Lei Lai
Qi Hong Lai
Smart Grids
and Big Data
Analytics
for Smart Cities
Smart Grids and Big Data Analytics for Smart
Cities
Chun Sing Lai • Loi Lei Lai • Qi Hong Lai
Smart Grids and Big Data
Analytics for Smart Cities
Chun Sing Lai Loi Lei Lai
Department of Electrical Engineering Department of Electrical Engineering
Guangdong University of Technology Guangdong University of Technology
Guangzhou, China Guangzhou, China
Department of Electronic and Computer
Engineering
Brunel University London
London, UK
Qi Hong Lai
Sir William Dunn School of Pathology
University of Oxford
Oxford, UK
ISBN 978-3-030-52154-7 ISBN 978-3-030-52155-4 (eBook)
https://doi.org/10.1007/978-3-030-52155-4
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Preface
This book was written in response to the increasing interest in smart city technology
and its deployment worldwide. There is a strong belief that smart city technology
will produce an all-win solution with regard to environmental, social, and eco-
nomic impact.
Major environmental, economic, and technological challenges such as: climate
change; economic restructuring; pressure on public finances; digitalization of the
retail and entertainment industries, and the growth of urban and ageing populations
has generated huge interest for cities to be run differently and smartly. The term
“smart city” was coined to describe such cities, and they promise a significant
improvement in the quality of life of its citizens through the combination of infor-
mation and communication technology (ICT), new services and improved city
infrastructure. The evolutionary process in the development of a smart city is mainly
driven by an innovative, user-centric vision—specifically by tackling urban issues
from the perspective of citizens and taking into account their need to engage with
city management and planning. The approach is based on emerging technology,
whereby the solution obtained through integration of human and social capital
allows their significant interaction as it is adopted to a city. The application of the
Internet of Things (IoT) to city operation is of special interest to support the aim of
efficiently transforming cities to acquire substantial and sustainable development as
well as high quality of life.
The mission of building smart cities is based on achieving improved utilization
of renewable energy, safeguarding of the environment, and waste reduction. At the
same time, fostering cohesion between citizens to obtain shared benefits derived
from the eco-sustainability vision which is headed by effective industrial and urban
development to allow pressing needs to be met without compromising the imminent
generations’ capacity. When considering an eco-sustainable method, practicality is
essential in the various facets and at different layers of the development process
such as environment, social services, and mobility. A smart city employs various
kinds of electronic IoT sensors to amass data and such data is used to control
resources and assets efficiently. The data is often sourced from devices, assets, and
citizens and is processed and studied to then understand and improve crime
v
vi Preface
detection, transportation and traffic systems, water supply networks, hospitals,
information systems, waste management, power plants, and additional community
services.
Smart city is now a popular term; however, its definition and specifics remain
unclear. This has led to different interpretations of a smart city. Most commonly, a
smart city can be described by six important pillars, namely smart people (social
and human capital), smart living (quality of life), smart economy (competitiveness),
smart mobility (transport and ICT), smart governance (participation), and smart
environment (natural resources). Smart city programs and technologies have now
been developed in many cities worldwide including London, New York, Hong
Kong, Singapore, Paris, Tokyo, Amsterdam, Barcelona, Dubai, Stockholm, and
Copenhagen—some of which will be discussed in more detail in case studies.
This book focuses on delivering comprehensive and detailed analysis of the fol-
lowing areas of smart cities: smart energy, smart mobility, smart health, and smart
water. The purpose is to inform the reader firstly through more general but compre-
hensive coverage of the concept of smart cities before diving into more specific
areas without excessive specialization as to avoid merely not only presenting quali-
tative data and numerical techniques, but also providing, where feasible, practical
case studies and project discussions.
Chapter 1 discusses what a smart city should be. In this chapter, characteristics,
functionality, and domain of smart city will be explained. Different elements of a
smart city, such as smart energy, smart water, smart health, smart infrastructure, and
big data analytics will be examined. Case studies will be used to demonstrate the
work done to help to establish a smart city deployment and some benefits derived
from the effort spent. Some examples of smart cities worldwide will be reported.
Challenges and opportunities derived from future smart city projects will be discussed.
Due to the need to use a large number of renewables in the near future and the
requirement to have a stable energy system, Chapter 2 covers data analytics for solar
energy in promoting smart cities. In this chapter, a comprehensive review of high
penetration of photovoltaic (PV) and an overview of electrical energy storage (EES)
for PV systems is presented. Solar power forecasting techniques for operation and
planning of PV and EES are included. A deterministic approach for sizing PV and
ESS with anaerobic digestion (AD) biogas power plant is developed to achieve a
minimal levelized cost of energy (LCOE). The aim is to minimize energy imbalance
between generation and demand due to AD generator constraint and high penetra-
tion of PV. For data analytics, the chapter presents the issues in correlation analysis
due to imbalanced data and data uncertainty in real-life solar data. A robust correla-
tion framework was proposed and tested on real-life solar irradiance and weather
condition data. For solar data cluster analysis, a novel method with Fuzzy C-Means
with dynamic time warping distance was proposed to determine patterns in daily
clearness index (CI) profiles. CI profiles could be varied significantly in different
seasons.
Based on high security, transparency, tamper-proof, and decentralization, block-
chain is suitable for microgrids with high renewable penetration and advanced
supervisory control and data acquisition (SCADA) sensors as there is a need for a
Preface vii
new market approach to facilitate the power generation and load balance and make
the optimal use of low carbon energy sources. Chapter 3 presents blockchain appli-
cations in microgrid clusters. Microgrids with blockchain can give a more resilient,
cost-efficient, low-transmission-loss, and environment-friendly grid. Smart
contract-b ased hybrid peer-to-peer (P2P) energy trading model with cryptocurrency
named localized renewable energy certificate (LO-REC) will be discussed. The
advantages and challenges of combining blockchain with microgrids are identified.
This chapter serves as a guide for future research on blockchain applications in
microgrids.
Water management is a critical task and impacts on the environment and eco-
nomics. Chapter 4 deals with a time-synchronized ZigBee building network for
smart water management. It is essential for the development of a flexible, reliable,
and scalable sensor network to install and replace water sensors in buildings.
Wireless communication will be of utmost importance. Nevertheless, incorrect net-
work time synchronization will create packet loss and long latency which reduces
the network performance. In this chapter, time-synchronized ZigBee building net-
work is proposed for water management according to the node-to-node time syn-
chronization. The simulation result shows that the mean synchronization error and
variance are reduced. Also, an interference-mitigated ZigBee-based advanced
metering infrastructure solution was created for high-traffic smart metering.
Without energy, any city cannot be in proper operation. Also, for the convenience
of the citizens, electrical vehicles would be needed. As a result, there will be many
batteries within a city. However, this could give risk to human and it is essential to
minimize the damage. Chapter 5 reports a narrowband internet of things (NB-IoT)-
based temperature prediction for valve-regulated lead-acid battery (VRLA). Due to
its huge market, VRLA gained a significant part in industries. However, VRLA
safety has been a wide concern since it is prone to self-heating problems which
generate extra cost or even cause accidents when the internal temperature (IT) of
VRLA is exceeded. To prevent potential hazards, effective internal VRLA tempera-
ture monitoring methods are required. In the method, the internal temperature is
estimated by ambient temperature (AT) and input current (IC) through a pre-trained
prediction model. The measured temperature data will be sent to the backend server
using NB-IoT. A kind of recurrent neural network, namely nonlinear autoregressive
exogenous is applied to determine the potential relationship between the input AT,
IC, and the output IT.
It is learnt that over 60% of adult drivers experienced sleepiness during driving
and over 40% of traffic accidents are created by intoxicated drivers. Chapter 6
reports a health detection scheme for drunk drivers. The integration of the wearable
sensors facilitates the real-time monitoring of human conditions under different sce-
narios including patient tracking and human safety. In this chapter, an electrocardio-
gram (ECG)-based status of human detection (ECG-HSD) scheme was proposed to
sense both drowsy and intoxicated conditions. In ECG-HSD scheme, resemblances
of ECG signals during ordinary, drowsy, and intoxicated conditions are collected
and the equivalent feature vector was constructed. The essential data points on ECG
samples are weighted to improve detection accuracy. With multiple criteria
viii Preface
decision-m aking approach, the results showed that ECG-HSD scheme could achieve
acceptable accuracy and rapid testing time.
This book addresses the most up-to-date problems of a smart city and their solu-
tions in a cohesive manner. It is the product of contributions from world-class
experts, educators, and students so to cover all levels of understanding to optimize
its delivery. Therefore, we are confident that it will provide invaluable insight for
decision-makers, engineers, doctors, educators, system operators, managers, plan-
ners, and researchers across all levels of career and academic progression.
London, UK Chun Sing Lai
Guangzhou, China Loi Lei Lai
Oxford, UK Qi Hong Lai
25 May 2020
Acknowledgments
The authors wish to thank Mr. Michael McCabe of Springer Nature and his team in
supporting this project. Special help from Mr. Menas Donald Kiran, Ms.
Mohanarangan Gomathi, and Ms. Cynthya Pushparaj in producing the book is very
much appreciated.
The authors wish to thank friends, colleagues, and students, without their support
this book could not have been completed. In particular, the authors thank Dr. Kim
Fung Tsang, Professor Ruiwen He, Professor Zhao Xu, Professor Wing W. Y. Ng,
Dr. Youwei Jia, Dr Haoliang Yuan, Dr. Zhuoli Zhao, Dr. Fang Yuan Xu, Dr. Yifei
Wang, Ms. Liping Huang, Ms. Yingshan Tao, Ms. Xin Cun, Ms. Mengxuan Yan, Mr.
Zhiheng Huang, Mr. Xiaoqing Zhong, and Professor Mohammad Shahidehpour.
The permission to reproduce copyright materials by the IEEE and Elsevier for a
number of papers mentioned in some of the chapters is most helpful.
Last but not least, the authors appreciate the extraordinary support given from
their family during the preparation of the book. In particular, to Ms. Qi Ling Lai in
designing some of the art works and Ms. Li Rong Li in providing a workable envi-
ronment under a pandemic.
ix
Contents
1 Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Functional Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Sensors and Intelligent Electronic Devices . . . . . . . . . . . . . 4
1.2.2 Communication Networks and Cyber Security . . . . . . . . . . 4
1.2.3 Systems Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.4 Intelligence and Data Analytics . . . . . . . . . . . . . . . . . . . . . . 5
1.2.5 Management and Control Platforms . . . . . . . . . . . . . . . . . . 5
1.3 Elements of a Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.1 Smart Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.2 Smart Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
1.3.3 Smart Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
1.3.4 Smart Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
1.3.5 Smart Infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
1.4 Smart Cities Examples Worldwide . . . . . . . . . . . . . . . . . . . . . . . . . 156
1.4.1 Barcelona Has Set a Zero-Energy Poverty
Target by 2030 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
1.4.2 Copenhagen Aims to Become the First Carbon-Neutral
Capital by 2025 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
1.4.3 London Sets the Target for a Zero-Emission
Transport Network by 2050 . . . . . . . . . . . . . . . . . . . . . . . . . 157
1.4.4 Oslo Aims to Cut City Emissions by 95% by 2030 . . . . . . . 157
1.4.5 Stockholm Plans to Achieve Net-Zero Emissions
by 2040, Paris by 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
1.4.6 Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
1.4.7 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
1.4.8 Some Practical Applications . . . . . . . . . . . . . . . . . . . . . . . . 160
1.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
xi