Transportation and Power Grid in Smart Cities

HUSSEIN T. MOUFTAH, PHD, is Canada Research Chair and Distinguished University Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada. MELIKE EROL-KANTARCI, PHD, is Assistant Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada. MUBASHIR HUSAIN REHMANI, PHD, is Assistant Professor, Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Cantt, Pakistan.

• List of Contributors xxiPreface xxviiSECTION I Communication Technologies for Smart Cities 11 Energy-Harvesting Cognitive Radios in Smart Cities 3Mustafa Ozger, Oktay Cetinkaya and Ozgur B. Akan1.1 Introduction 31.1.1 Cognitive Radio 51.1.2 Cognitive Radio Sensor Networks 51.1.3 Energy Harvesting and Energy-Harvesting Sensor Networks 61.2 Motivations for Using Energy-Harvesting Cognitive Radios in Smart Cities 61.2.1 Motivations for Spectrum-Aware Communications 71.2.2 Motivations for Self-Sustaining Communications 71.3 Challenges Posed by Energy-Harvesting Cognitive Radios in Smart Cities 81.4 Energy-Harvesting Cognitive Internet of Things 91.4.1 Definition 91.4.2 Energy-Harvesting Methods in IoT 101.4.3 System Architecture 121.4.4 Integration of Energy-Harvesting Cognitive Radios with the Internet 131.5 A General Framework for EH-CRs in the Smart City 141.5.1 Operation Overview 141.5.2 Node Architecture 151.5.3 Network Architecture 161.5.4 Application Areas 171.6 Conclusion 18References 182 LTE-D2D Communication for Power Distribution Grid: Resource Allocation for Time-Critical Applications 21Leonardo D. Oliveira, Taufik Abrao and Ekram Hossain2.1 Introduction 212.2 Communication Technologies for Power Distribution Grid 222.2.1 An Overview of Smart Grid Architecture 222.2.2 Communication Technologies for SG Applications Outside Substations 242.2.3 Communication Networks for SG 262.3 Overview of Communication Protocols Used in Power Distribution Networks 272.3.1 Modbus 272.3.2 IEC 60870 292.3.3 DNP3 312.3.4 IEC 61850 322.3.5 SCADA Protocols for Smart Grid: Existing State-of-the-Art 352.4 Power Distribution System: Distributed Automation Applications and Requirements 362.4.1 Distributed Automation Applications 362.4.1.1 Voltage/Var Control (VVC) 372.4.1.2 Fault Detection, Isolation, and Restoration (FDCIR) 382.4.2 Requirements for Distributed Automation Applications 392.5 Analysis of Data Flow in Power Distribution Grid 402.5.1 Model for Power Distribution Grid 402.5.2 IEC 61850 Traffic Model 422.5.2.1 Cyclic Data Flow 422.5.2.2 Stochastic Data Flow 452.5.2.3 Burst Data Flow 462.6 LTE-D2D for DA: Resource Allocation for Time-Critical Applications 472.6.1 Overview of LTE 472.6.2 IEC 61850 Protocols over LTE 482.6.2.1 Mapping MMS over LTE 492.6.2.2 Mapping GOOSE over LTE 502.6.3 Resource Allocation in uplink LTE-D2D for DA Applications 502.6.3.1 Problem Formulation 512.6.3.2 Scheduler Design 542.6.3.3 Numerical Evaluation 552.7 Conclusion 60References 613 5G and Cellular Networks in the Smart Grid 69Jimmy Jessen Nielsen, Ljupco Jorguseski, Haibin Zhang, Hervé Ganem, Ziming Zhu and Petar Popovski3.1 Introduction 693.1.1 Massive MTC 703.1.2 Mission-Critical MTC 703.1.3 Secure Mission-Critical MTC 713.2 From Power Grid to Smart Grid 713.3 Smart Grid Communication Requirements 743.3.1 Traffic Models and Requirements 743.4 Unlicensed Spectrum and Non-3GPP Technologies for the Support of Smart Grid 763.4.1 IEEE 802.11ah 763.4.2 Sigfox’s Ultra-Narrow Band (UNB) Approach 793.4.3 LoRaTM Chirp Spread Spectrum Approach 803.5 Cellular and 3GPP Technologies for the Support of Smart Grid 823.5.1 Limits of 3GPP Technologies up to Release 11 823.5.2 Recent Enhancements of 3GPP Technologies for IoT Applications (Releases 12–13) 833.5.2.1 LTE Cat-0 and Cat-M1 devices 843.5.2.2 Narrow-Band Internet of Things (NB-IoT) and Cat-NB1 Devices 853.5.3 Performance of Cellular LTE Systems for Smart Grids 863.5.4 LTE Access Reservation Protocol Limitations 873.5.4.1 LTE Access Procedure 873.5.4.2 Connection Establishment 903.5.4.3 Numerical Evaluation of LTE Random Access Bottlenecks 913.5.5 What Can We Expect from 5G? 933.6 End-to-End Security in Smart Grid Communications 943.6.1 Network Access Security 953.6.2 Transport Level Security 963.6.3 Application Level Security 963.6.4 End-to-End Security 963.6.5 Access Control 973.7 Conclusions and Summary 99References 1004 Machine-to-Machine Communications in the Smart City—a Smart Grid Perspective 103Ravil Bikmetov, M. Yasin Akhtar Raja and KhurramKazi4.1 Introduction 1034.2 Architecture and Characteristics of Smart Grids for Smart Cities 1054.2.1 Definition of a Smart Grid and Its Conceptual Model 1064.2.2 Standardization Approach in Smart Grids 1124.2.3 Smart Grid Interoperability Reference Model (SGIRM) 1134.2.4 Smart Grid Architecture Model 1144.2.5 Energy Sources in the Smart Grid 1154.2.6 Energy Consumers in a Smart Grid 1174.2.7 Energy Service Providers in the Smart Grid 1194.3 Intelligent Machine-to-Machine Communications in Smart Grids 1204.3.1 Reference Architecture of Machine-to-Machine Interactions 1204.3.2 Communication Media and Protocols 1214.3.3 Layered Structure of Machine-to-Machine Communications 1264.4 Optimization Algorithms for Energy Production, Distribution, and Consumption 1324.5 Machine Learning Techniques in Efficient Energy Services and Management 1344.6 Future Perspectives 1354.7 Appendix 136References 1385 5G and D2D Communications at the Service of Smart Cities 147Muhammad Usman,Muhammad Rizwan Asghar and Fabrizio Granelli5.1 Introduction 1475.2 Literature Review 1505.3 Smart City Scenarios 1535.3.1 Public Health 1545.3.2 Transportation and Environment 1555.3.3 Energy Efficiency 1575.3.4 Smart Grid 1575.3.5 Water Management 1585.3.6 Disaster Response and Emergency Services 1595.3.7 Public Safety and Security 1595.4 Discussion 1605.4.1 Multiple Radio Access Technologies (Multi-RAT) 1605.4.2 Virtualization 1605.4.3 Distributed/Edge Computing 1615.4.4 D2D Communication 1615.4.5 Big Data 1625.4.6 Security and Privacy 1635.5 Conclusion 163References 163SECTION II Emerging Communication Networks for Smart Cities 1716 Software Defined Networking and Virtualization for Smart Grid 173Hakki C. Cankaya6.1 Introduction 1736.2 Current Status of Power Grid and Smart Grid Modernization 1746.2.1 Smart Grid 1746.3 Network Softwarerization in Smart Grids 1776.3.1 Software Defined Networking (SDN) as Next-Generation Software-Centric Approach to Telecommunications Networks 1776.3.2 Adaptation of SDN for Smart Grid and City 1796.3.3 Opportunities for SDN in Smart Grid 1796.4 Virtualization for Networks and Functions 1836.4.1 Network Virtualization 1836.4.2 Network Function Virtualization 1846.5 Use Cases of SDN/NFV in the Smart Grid 1856.6 Challenges and Issues with SDN/NFV-Based Smart Grid 1876.7 Conclusion 187References 1887 GHetNet: A Framework Validating Green Mobile Femtocells in Smart-Grids 191Fadi Al-Turjman7.1 Introduction 1917.2 RelatedWork 1927.2.1 Static Validation Techniques 1947.2.2 Dynamic Validation Techniques 1957.3 System Models 1977.3.1 Markov Model 1997.3.2 Service-Rate Model 1997.3.3 Communication Model 2007.4 The Green HetNet (GHetNet) Framework 2017.5 A Case Study: E-Mobility for Smart Grids 2067.5.1 Performance metrics and parameters 2077.5.2 Simulation Setups and Baselines 2087.5.3 Results and Discussion 2087.5.3.1 The Impact of Velocity on FBS Performance 2097.5.3.2 The Impact of the Grid Load on Energy Consumption 2117.6 Conclusion 213References 2138 Communication Architectures and Technologies for Advanced Smart Grid Services 217Francois Lemercier, Guillaume Habault, Georgios Z. Papadopoulos, Patrick Maille, NicolasMontavont and Periklis Chatzimisios8.1 Introduction 2178.2 The Smart Grid Communication Architecture and Infrastructure 2198.2.1 DSO-Based Communications 2208.2.1.1 The Existing AMI Organization 2208.2.1.2 Communication Technologies used in the AMI 2228.2.1.3 AMI Limitations 2238.2.2 Internet-Based Architectures 2248.2.2.1 IP-Based Architecture Limitations 2258.2.3 Next-Generation Smart Grid Architecture 2258.2.3.1 Technical Issues for Next-Generation Smart Grids 2278.2.3.2 Handing Back the Keys to the User: Energy Management Should Be Separated from the Smart Meter 2278.2.3.3 To Build an Open Market, Use an Open Network 2288.2.3.4 Multi-Level Aggregation 2288.2.3.5 Security Concerns 2298.2.3.6 Ongoing Research Efforts 2298.3 Routing Information in the Smart Grid 2318.3.1 Routing Family of Protocols 2318.3.1.1 Proactive Routing Protocol 2328.3.1.2 Topology Management under RPL 2328.3.1.3 Routing Table Maintenance under RPL 2338.3.1.4 Routing Strategy: Metrics and Constraints 2348.3.1.5 Path Computation under RPL 2348.3.1.6 Summary of the RPL DODAG construction 2358.3.1.7 Reactive Routing Protocol 2368.3.1.8 Topology Management under AODV 2378.3.2 Reactive Routing Protocol in a Constrained Network 2388.3.2.1 Performance Evaluation 2398.3.2.2 Summary on Routing Protocols 2418.4 Conclusion 242References 2439 Wireless Sensor Networks in Smart Cities: Applications of Channel Bonding to Meet Data Communication Requirements 247Syed Hashim Raza Bukhari, Sajid Siraj andMubashir Husain Rehmani9.1 Introduction, Basics, and Motivation 2479.2 WSNs in Smart Cities 2489.2.1 WSNs in Underground Transportation 2499.2.2 WSNs in Smart Cab Services 2499.2.3 WSNs in Waste Management Systems 2499.2.4 WSNs in Atmosphere Health Monitoring 2499.2.5 WSNs in Smart Grids 2529.2.6 WSNs in Weather Forecasting 2529.2.7 WSNs in Home Automation 2529.2.8 WSNs in Structural Health Monitoring 2529.3 Channel Bonding 2539.3.1 Channel Bonding Schemes in Traditional Networks 2539.3.2 Channel Bonding Schemes in Wireless Sensor Networks 2549.3.3 Channel Bonding Schemes in Cognitive Radio Networks 2559.3.4 Channel Bonding for Cognitive Radio Sensor Networks 2579.4 Applications of Channel Bonding in CRSN-Based Smart Cities 2589.4.1 CRSNs in Smart Health Care 2589.4.2 CRSNs in M2M Communications 2589.4.3 CRSNs Multiple Concurrent Deployments in Smart Cities 2599.4.4 CRSNs in Smart Home Applications 2599.4.5 CRSNs Smart Environment Control 2599.4.6 CRSNs-Based IoT 2599.5 Issues and Challenges Regarding the Implementation of Channel Bonding in Smart Cities 2599.5.1 Privacy of Citizens 2609.5.2 Energy Conservation 2609.5.3 Data Storage and Aggregation 2609.5.4 Geographic Awareness and Adaptation 2609.5.5 Interference and Spectrum Issues 2609.6 Conclusion 261References 26110 A Prediction Module for Smart City IoT Platforms 269Sema F. Oktug, Yusuf Yaslan and Halil Gulacar10.1 Introduction 26910.2 IoT Platforms for Smart Cities 27110.2.1 ARM Mbed 27110.2.2 Cumulocity 27110.2.3 DeviceHive 27310.2.4 Digi 27310.2.5 Digital Service Cloud 27410.2.6 FiWare 27410.2.7 Global Sensor Networks (GSN) 27410.2.8 IoTgo 27410.2.9 Kaa 27510.2.10 Nimbits 27510.2.11 RealTime.io 27510.2.12 SensorCloud 27510.2.13 SiteWhere 27610.2.14 TempoIQ 27610.2.15 Thinger.io 27610.2.16 Thingsquare 27610.2.17 ThingWorx 27710.2.18 VITAL 27710.2.19 Xively 27710.3 Prediction Module Developed 27710.3.1 The VITAL IoT Platform 27810.3.2 VITAL Prediction Module 27810.4 AUse Case Employing the Traffic Sensors in Istanbul 28110.4.1 Prediction Techniques Employed 28210.4.1.1 Data Preprocessing 28410.4.1.2 Feature Vectors 28410.4.2 Results 28510.4.2.1 Regression Results 28610.5 Conclusion 288Acknowledgment 288References 289SECTION III Renewable Energy Resources and Microgrid in Smart Cities 29111 Integration of Renewable Energy Resources in the Smart Grid: Opportunities and Challenges 293Mohammad UpalMahfuz, Ahmed O. Nasif,MdMaruf Hossain andMd. Abdur Rahman11.1 Introduction 29311.2 The Smart Grid Paradigm 29411.2.1 The Smart Grid Concept 29411.2.2 System Components of the SG 29611.3 Renewable Energy Integration in the Smart Grid 29811.3.1 Resource Characteristics and Distributed Generation 29811.3.2 Why Is Integration Necessary? 29911.4 Opportunities and Challenges 29911.4.1 Energy Storage (ES) 30011.4.1.1 Key Energy Storage Technologies 30011.4.1.2 Key Energy Storage Challenges in SG 30111.4.2 Distributed Generation (DG) 30211.4.2.1 Key DG Sources and Generators 30311.4.2.2 Key Parts and Functions of a DG System and Its Distribution 30311.4.2.3 DG and Dispatch Challenges 30411.4.3 Resource Forecasting, Modeling, and Scheduling 30511.4.3.1 Resource Modeling and Scheduling 30511.4.3.2 Resource Forecasting (RF) 30711.4.4 Demand Response 30811.4.5 Demand-Side Management (DSM) 30911.4.6 Monitoring 31011.4.7 Transmission Techniques 31111.4.8 System-Related Challenges 31111.4.9 V2G Challenges 31211.4.10 Security Challenges in the High Penetration of RE Resources 31411.5 Case Studies 31411.6 Conclusion 315References 31612 Environmental Monitoring for Smart Buildings 327Petros Spachos and Konstantinos Plataniotis12.1 Introduction 32712.2 Wireless Sensor Networks in Monitoring Applications 32912.3 Application Requirements and Challenges 33012.3.1 Monitoring Area 33012.3.2 Application Scenario and Design Goal 33212.3.3 Requirements 33312.3.3.1 Sensor Type 33312.3.3.2 Real-Time Data Aggregation 33512.3.3.3 Scalability 33512.3.3.4 Usability, Autonomy, and Reliability 33612.3.3.5 Remote Management 33612.3.4 Challenges 33612.3.4.1 Power Management 33612.3.4.2 Wireless Network Coexistence 33712.3.4.3 Mesh Routing 33712.3.4.4 Robustness 33712.3.4.5 Dynamic Changes 33712.3.4.6 Flexibility 33712.3.4.7 Size and cost 33712.4 Wireless Sensor Network Architecture 33812.4.1 Framework 33812.4.2 Hardware Infrastructure 33912.4.3 Data Processing 34112.4.3.1 Noise Reduction, Data Smoothing, and Calibration 34112.4.3.2 Packet formation process 34212.4.3.3 Information Processing and Storage 34312.4.4 Indoor Monitoring System 34312.5 Experiments and Results 34312.5.1 Experimental Setup 34312.5.2 Results Analysis 34712.6 Conclusions 350References 35013 Cooperative EnergyManagement in Microgrids 355Ioannis Zenginis, John Vardakas, Prodromos-VasileiosMekikis and Christos Verikoukis13.1 Introduction 35513.2 The Cooperative Energy Management System Model 35713.2.1 PV Panel Modeling 35913.2.2 Energy Storage System 36013.2.3 Inverter 36113.2.4 Microgrid Energy Exchange 36113.3 Evaluation and Discussion 36213.4 Conclusion 366Acknowledgment 367References 36814 Optimal Planning and Performance Assessment of Multi-Microgrid Systems in Future Smart Cities 371ShouxiangWang, LeiWu, Qi Liu and Shengxia Cai14.1 Optimal Planning of Multi-Microgrid Systems 37214.1.1 Introduction 37214.1.2 Optimal Structure Planning 37314.1.2.1 Definition of Indices 37314.1.2.2 Structure Planning Method 37514.1.3 Optimal Capacity Planning 37714.1.3.1 Definition of Indexes 37714.1.3.2 Capacity Planning Method 38114.1.4 Conclusions 38414.2 Performance Assessment of Multi-Microgrid System 38414.2.1 Introduction 38414.2.2 Comprehensive Evaluation Indexes 38614.2.2.1 MMGS Source-Charge Capacity Index 38614.2.2.2 MMGS Energy Interaction Index 38814.2.2.3 MMGS Reliability Index 39014.2.2.4 MMGS Economics Index 39514.2.2.5 Energy Utilization Efficiency Index 39814.2.2.6 Energy Saving and Emission Reduction Index 39814.2.2.7 Renewable Energy Utilization Index 39914.2.3 Performance Assessment 40014.2.3.1 Performance Assessment of Grid-Connected MMGS 40014.2.3.2 Performance Assessment of Islanded MMGS 40114.2.3.3 Annual Performance Assessment of the MMGS 40214.2.4 Case Studies 40314.2.4.1 System Description 40314.2.4.2 Numerical Results 40314.3 Conclusions 406Acknowledgment 407References 407SECTION IV Smart Cities, Intelligent Transportation Systemand Electric Vehicles 41115 Wireless Charging for Electric Vehicles in the Smart Cities: Technology Review and Impact 413Alicia Triviño-Cabrera and José A. Aguado15.1 Introduction 41315.2 Review of theWireless Charging Methods 41515.2.1 Technologies SupportingWireless Power Transfer for EVs 41515.2.2 Operation Modes forWireless Power Transfer in EVs 41615.3 Electrical Effect of Charging Technologies on the Grid 41815.3.1 Harmonics Control in EVWireless Chargers 41815.3.2 Power Factor Control in EVWireless Chargers 41915.3.3 Implementation of Bidirectionality in EVWireless Chargers 42015.3.4 Discussion 42115.4 Scheduling Considering Charging Technologies 42115.5 Conclusions and Future Guidelines 423References 42416 Channel Access Modelling for EV Charging/Discharging Service through Vehicular ad hoc Networks (VANETs) Communications 427Dhaou Said and Hussein T. Mouftah16.1 Introduction 42816.2 Technical Environment of the EV Charging/Discharging Process 42816.2.1 EVSE Overview 42916.2.2 Inductive Chargers: Opportunities and Potential 42916.3 Overview of Communication Technologies in the Smart Grid 43016.3.1 Power Line Communication 43016.3.2 Wireless Communications for EV–Smart Grid Applications 43116.4 Channel Access Model for EV Charging Service 43216.4.1 Overview of VANET and LTE 43216.4.2 Case Study: Access ChannelModel 43316.4.3 Simulations Results 43816.5 Conclusions 440References 44017 Intelligent Parking Management in Smart Citie s 443Sanket Gupte andMohamed Younis17.1 Introduction 44317.2 Design Issues and Taxonomy of Parking Solutions 44517.2.1 Design Issues for Autonomous Parking Systems 44517.2.2 Taxonomy of Parking Solutions 44517.3 Classification of Existing Parking Systems 44717.3.1 Sensing Infrastructure 44717.3.2 Communication Infrastructure 45717.3.3 Storage Infrastructure 46017.3.4 Application Infrastructure 46117.3.5 User Interfacing 46317.3.6 Comparison of Existing Parking Systems 46517.4 Participatory Sensing–Based Smart Parking 46517.4.1 The Components 46717.4.1.1 Users 46717.4.1.2 IoT Devices 46717.4.1.3 Server 46817.4.1.4 Parking Spots 46817.4.2 Parking Management Application 46917.4.2.1 User Interface 46917.4.2.2 Smart Reporting System 47017.4.2.3 Leaderboard 47017.4.2.4 Rewards Store 47117.4.2.5 Enforcement and Compliance 47217.4.2.6 External Integration 47217.4.3 Data Processing and Cloud Support 47217.4.3.1 Availability Computation 47217.4.3.2 Reputation System 47317.4.3.3 Scoring System 47417.4.3.4 ReservationModel 47417.4.3.5 Analysis and Learning 47417.4.4 Implementation and Performance Evaluation 47417.4.4.1 Prototype Application 47417.4.4.2 Experiment Setup 47517.4.4.3 Simulation Results 47517.4.5 Features and Benefits 47717.5 Conclusions and Future Advancements 479References 48018 Electric Vehicle Scheduling and Charging in Smart Cities 485Muhammmad Amjad, Mubashir Husain Rehmani and Tariq Umer18.1 Introduction 48518.1.1 Integration of EVs into Smart Cities 48618.1.1.1 Enhancing the Existing Power Capacity 48618.1.1.2 Designing the Communication Protocols to Support the Smart Recharging Structure 48618.1.1.3 Development of a Well-designed Recharging Architecture 48618.1.1.4 Considering the Expected Load on the Smart Grid 48618.1.1.5 Need for Scheduling Approaches for EVs Recharging 48618.1.2 Main Contributions 48718.1.3 Organization of the Chapter 48718.2 Smart Cities and Electric Vehicles: Motivation, Background, and ApplicationScenarios 48818.2.1 Smart Cities: An Overview 48818.2.1.1 Provision of Smart Transportation 48818.2.1.2 Energy Management in Smart cities 48818.2.1.3 Integration of the Economic and Business Model 48818.2.1.4 Wireless Communication Needs/Communication Architectures for Smart Cities 48918.2.1.5 Traffic Congestion Avoidance in Smart Cities 48918.2.1.6 Support of Heterogeneous Technologies in Smart Cities 48918.2.1.7 Green Applications Support in Smart Cities 48918.2.1.8 Security and Privacy in Smart Cities 49018.2.2 Motivation of Using EVs in Smart cities 49018.2.3 Application Scenarios 49018.2.3.1 Avoiding Spinning Reserves 49018.2.3.2 V2G and G2V Capability 49118.2.3.3 CO2 Minimization 49118.2.3.4 Load Management on the Local Microgrid 49118.3 EVs Recharging Approaches in Smart Cities 49118.3.1 Centralized EVs Recharging Approach 49118.3.1.1 Main Contributions and Limitations of Centralized EVs-Recharging Approach 49218.3.2 Distributed EVs Recharging Approach 49318.3.2.1 Main Contributions and Limitations of the Distributed EVs-recharging Approach 49318.4 Scheduling EVs Recharging in Smart Cities 49318.4.1 Objectives Achieved via Different Scheduling Approaches 49418.4.1.1 Reduction of Power Losses 49418.4.1.2 Minimizing Total Cost of Energy for Users 49518.4.1.3 Maximizing Aggregator Profit 49618.4.1.4 Frequency Regulation 49718.4.1.5 Voltage regulation 49718.4.1.6 Support for Renewable Energy Sources for Recharging of EVs 49718.4.2 Resource Allocation for EVs Recharging in Smart Cities (Optimization Approaches) 49818.5 Open Issues, Challenges, and Future Research Directions 49818.5.1 Support ofWireless Power Charger 49918.5.2 Vehicle-to-Anything 49918.5.3 Energy Management for Smart Grid via EVs 49918.5.4 Advance Communication Needs for Controlled EVs Recharging 49918.5.5 EVs Control Applications 49918.5.6 Standardization for Communication Technologies Used for EVs Recharging 50018.6 Conclusion 500References 500SECTION V Security and Privacy Issues and Big Data in Smart Cities 50719 Cyber-Security and Resiliency of Transportation and Power Systems in Smart Cities 509Seyedamirabbas Mousavian,Melike Erol-Kantarci and Hussein T. Mouftah19.1 Introduction 50919.2 EV Infrastructure and Smart Grid Integration 51019.3 System Model 51219.3.1 Model Definition and Assumptions 51219.4 Estimating the Threat Levels in the EVSE Network 51319.5 Response Model 51419.6 Propagation Impacts on Power System Operations 51519.6.1 Cyberattack Propagation in PMU Networks 51519.6.2 Threat Level Estimation in PMU Networks 51519.6.3 Response Model in PMU Networks 51819.6.4 PMU Networks: Experimental Results 52119.7 Conclusion and Open Issues 525References 52520 Protecting the Privacy of Electricity Consumers in the Smart City 529Binod Vaidya and Hussein T. Mouftah20.1 Introduction 52920.2 Privacy in the Smart Grid 53020.2.1 Privacy Concerns over Customer Electricity Data Collected by the Utility 53120.2.2 Privacy Concerns on Energy Usage Information Collected by a Non-Utility-OwnedMetering Device 53220.2.3 Privacy Protection 53220.3 Privacy Principles 53220.4 Privacy Engineering 53520.4.1 Privacy Protection Goals 53520.4.2 Privacy Engineering Framework and Guidelines 53820.5 Privacy Risk and Impact Assessment 54020.5.1 System Privacy Risk Model 54020.5.2 Privacy Impact Assessment (PIA) 54120.6 Privacy Enhancing Technologies 54220.6.1 Anonymization 54420.6.2 Trusted Computation 54520.6.3 Cryptographic Computation 54520.6.4 Perturbation 54620.6.5 Verifiable Computation 547Acknowledgment 547References 54821 Privacy Preserving Power Charging Coordination Scheme in the Smart Grid 555Ahmed Sherif, Muhammad Ismail, Marbin Pazos-Revilla,Mohamed Mahmoud, Kemal Akkaya, Erchin Serpedin and Khalid Qaraqe21.1 Introduction 55521.1.1 Smart Grid Security Requirements 55521.1.2 Charging Coordination Security Requirement 55621.2 Charging Coordination and Privacy Preservation 55821.3 Privacy-Preserving Charging Coordination Scheme 56021.3.1 Network andThreat Models 56021.3.2 The Proposed Scheme 56121.3.2.1 Anonymous Data Submission 56121.3.2.2 Charging Coordination 56521.4 Performance Evaluation 56721.4.1 Privacy/Security Analysis 56721.4.2 Experimental Study 56821.4.2.1 Setup 56821.4.2.2 Metrics and Baselines 56821.4.2.3 Simulation Results 56921.5 Summary 572Acknowledgment 573References 57322 Securing Smart Cities Systems and Services: A Risk-Based Analytics-Driven Approach 577Mahmoud Gad and Ibrahim Abualhaol22.1 Introduction to Cybersecurity for Smart Cities 57722.2 Smart Cities Enablers 57922.3 Smart Cities Attack Surface 58022.3.1 Attack Domains 58022.3.1.1 Communications 58022.3.1.2 Software 58022.3.1.3 Hardware 58022.3.1.4 Social Engineering 58022.3.1.5 Supply Chain 58122.3.1.6 Physical Security 58122.3.2 Attack Mechanisms 58222.4 Securing Smart Cities: A Design Science Approach 58222.5 NIST Cybersecurity Framework 58322.6 Cybersecurity Fusion Center with Big Data Analytics 58522.7 Conclusion 58722.8 Table of Abbreviations 587References 58823 Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory 591Robert Qiu, Lei Chu, Xing He, Zenan Ling and Haichun Liu23.1 Introduction 59123.1.1 Perspective on Smart Grids 59123.1.2 The Role of Data in the Future Power Grid 59423.1.3 A Brief Account for RMT 59523.2 RMT: A Practical and Powerful Big Data Analysis Tool 59623.2.1 Modeling Grid Data using Large Dimensional Random Matrices 59623.2.2 Asymptotic Spectrum Laws 59823.2.3 Transforms 60023.2.4 Convergence Rate 60123.2.5 Free Probability 60323.3 Applications to Smart Grids 60823.3.1 Hypothesis Tests in Smart Grids 60923.3.2 Data-DrivenMethods for State Evaluation 60923.3.3 Situation Awareness based on Linear Eigenvalue Statistics 61223.3.4 Early Event Detection Using Free Probability 62123.4 Conclusion and Future Directions 626References 629Index 635