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ARTIFICIAL INTELLIGENCE-BASED SMART POWER SYSTEMS
Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies
Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.
To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.
Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:
Engineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications.
Sanjeevikumar Padmanaban, PhD, is a Faculty Member with the Department of Business Development and Technology, Aarhus University, Denmark. He serves as an Editor/Associate Editor/Editorial Board of refereed journals, in particular, the IEEE Systems Journal, the IEEE Access Journal, the IET Power Electronics, and Journal of Power Electronics, Korea.
P. Sivaraman, PhD, is an Assistant Manager (Electrical) in Leading Engineering Organisation, Chennai, India. He is an active participant in IEEE standards association.
C. Sharmeela, PhD, is an Associate Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She is a senior member of IEEE, Life member of CBIP, Life-member of the Institution of Engineers (India), ISTE and SSI.
Jens Bo Holm-Nielsen, PhD, is the Head of the Esbjerg Energy Section with the Department of Energy Technology at Aalborg University. He has been an organizer of various international conferences, workshops, and training programs.
Editor Biography xv
List of Contributors xvii
1 Introduction to Smart Power Systems 1
Sivaraman Palanisamy, Zahira Rahiman, and Sharmeela Chenniappan
1.1 Problems in Conventional Power Systems 1
1.2 Distributed Generation (DG) 1
1.3 Wide Area Monitoring and Control 2
1.4 Automatic Metering Infrastructure 4
1.5 Phasor Measurement Unit 6
1.6 Power Quality Conditioners 8
1.7 Energy Storage Systems 8
1.8 Smart Distribution Systems 9
1.9 Electric Vehicle Charging Infrastructure 10
1.10 Cyber Security 11
1.11 Conclusion 11
References 11
2 Modeling and Analysis of Smart Power System 15
Madhu Palati, Sagar Singh Prathap, and Nagesh Halasahalli Nagaraju
2.1 Introduction 15
2.2 Modeling of Equipment’s for Steady-State Analysis 16
2.2.1 Load Flow Analysis 16
2.2.1.1 Gauss Seidel Method 18
2.2.1.2 Newton Raphson Method 18
2.2.1.3 Decoupled Load Flow Method 18
2.2.2 Short Circuit Analysis 19
2.2.2.1 Symmetrical Faults 19
2.2.2.2 Unsymmetrical Faults 20
2.2.3 Harmonic Analysis 20
2.3 Modeling of Equipments for Dynamic and Stability Analysis 22
2.4 Dynamic Analysis 24
2.4.1 Frequency Control 24
2.4.2 Fault Ride Through 26
2.5 Voltage Stability 26
2.6 Case Studies 27
2.6.1 Case Study 1 27
2.6.2 Case Study 2 28
2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm 29
2.6.2.2 Power Evacuation Study for 50 MW Generation 30
2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 31
2.6.2.4 Observations Made from Table 2.6 31
2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation 31
2.6.2.6 Normal Condition without Considering Contingency 32
2.6.2.7 Contingency Analysis 32
2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 33
2.7 Conclusion 34
References 34
3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 37
Marimuthu Marikannu, Vijayalakshmi Subramanian, Paranthagan Balasubramanian, Jayakumar Narayanasamy, Nisha C. Rani, and Devi Vigneshwari Balasubramanian
3.1 Introduction 37
3.2 Multilevel Cascaded Boost Converter 40
3.3 Modes of Operation of MCBC 42
3.3.1 Mode-1 Switch S A Is ON 42
3.3.2 Mode-2 Switch S A Is ON 42
3.3.3 Mode-3-Operation – Switch S A Is ON 42
3.3.4 Mode-4-Operation – Switch S A Is ON 42
3.3.5 Mode-5-Operation – Switch S A Is ON 42
3.3.6 Mode-6-Operation – Switch S A Is OFF 42
3.3.7 Mode-7-Operation – Switch S A Is OFF 42
3.3.8 Mode-8-Operation – Switch S A Is OFF 43
3.3.9 Mode-9-Operation – Switch S A Is OFF 44
3.3.10 Mode 10-Operation – Switch S A is OFF 45
3.4 Simulation and Hardware Results 45
3.5 Prominent Structures of Estimated DC–DC Converter with Prevailing Converter 49
3.5.1 Voltage Gain and Power Handling Capability 49
3.5.2 Voltage Stress 49
3.5.3 Switch Count and Geometric Structure 49
3.5.4 Current Stress 52
3.5.5 Duty Cycle Versus Voltage Gain 52
3.5.6 Number of Levels in the Planned Converter 52
3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB) 54
3.6.1 MCBC Connected with PV Panel 54
3.6.2 Output Response of PV Fed MCBC 54
3.6.3 H-Bridge Inverter 54
3.7 Modes of Operation 55
3.7.1 Mode 1 55
3.7.2 Mode 2 55
3.7.3 Mode 3 56
3.7.4 Mode 4 56
3.7.5 Mode 5 56
3.7.6 Mode 6 56
3.7.7 Mode 7 58
3.7.8 Mode 8 58
3.7.9 Mode 9 59
3.7.10 Mode 10 59
3.8 Simulation Results of MCBC Fed Inverter 60
3.9 Power Electronic Converter for E-Vehicles 61
3.10 Power Electronic Converter for HVDC/Facts 62
3.11 Conclusion 63
References 63
4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 65
Naveenkumar Marati, Shariq Ahammed, Kathirvel Karuppazaghi, Balraj Vaithilingam, Gyan R. Biswal, Phaneendra B. Bobba, Sanjeevikumar Padmanaban, and Sharmeela Chenniappan
4.1 Introduction 65
4.2 Applications of Power Electronic Converters 66
4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem 66
4.2.2 Power Electronic Converters in Renewable Energy Resources 67
4.3 Classification of DC-Link Topologies 68
4.4 Briefing on DC-Link Topologies 69
4.4.1 Passive Capacitive DC Link 69
4.4.1.1 Filter Type Passive Capacitive DC Links 70
4.4.1.2 Filter Type Passive Capacitive DC Links with Control 72
4.4.1.3 Interleaved Type Passive Capacitive DC Links 74
4.4.2 Active Balancing in Capacitive DC Link 75
4.4.2.1 Separate Auxiliary Active Capacitive DC Links 76
4.4.2.2 Integrated Auxiliary Active Capacitive DC Links 78
4.5 Comparison on DC-Link Topologies 82
4.5.1 Comparison of Passive Capacitive DC Links 82
4.5.2 Comparison of Active Capacitive DC Links 83
4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation 86
4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques 94
4.7 Conclusion 95
References 95
5 Energy Storage Systems for Smart Power Systems 99
Sivaraman Palanisamy, Logeshkumar Shanmugasundaram, and Sharmeela Chenniappan
5.1 Introduction 99
5.2 Energy Storage System for Low Voltage Distribution System 100
5.3 Energy Storage System Connected to Medium and High Voltage 101
5.4 Energy Storage System for Renewable Power Plants 104
5.4.1 Renewable Power Evacuation Curtailment 106
5.5 Types of Energy Storage Systems 109
5.5.1 Battery Energy Storage System 109
5.5.2 Thermal Energy Storage System 110
5.5.3 Mechanical Energy Storage System 110
5.5.4 Pumped Hydro 110
5.5.5 Hydrogen Storage 110
5.6 Energy Storage Systems for Other Applications 111
5.6.1 Shift in Energy Time 111
5.6.2 Voltage Support 111
5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary) 112
5.6.4 Congestion Management 112
5.6.5 Black Start 112
5.7 Conclusion 112
References 113
6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 115
Thamatapu Eswararao, Sundaram Elango, Umashankar Subramanian, Krishnamohan Tatikonda, Garika Gantaiahswamy, and Sharmeela Chenniappan
6.1 Introduction 115
6.2 Structure of Supercapacitor 117
6.2.1 Mathematical Modeling of Supercapacitor 117
6.3 Bidirectional Buck–Boost Converter 118
6.3.1 FPGA Controller 119
6.4 Experimental Results 120
6.5 Conclusion 123
References 125
7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 129
Rania Moutchou, Ahmed Abbou, Bouazza Jabri, Salah E. Rhaili, and Khalid Chigane
7.1 Introduction 129
7.2 Proposed MPPT Control Algorithm 130
7.3 Wind Energy Conversion System 131
7.3.1 Wind Turbine Characteristics 131
7.3.2 Model of PMSG 132
7.4 Fuzzy Logic Command for the MPPT of the PMSG 133
7.4.1 Fuzzification 134
7.4.2 Fuzzy Logic Rules 134
7.4.3 Defuzzification 134
7.5 Results and Discussions 135
7.6 Conclusion 139
References 139
8 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 141
Aleena Swetapadma, Shobha Agarwal, Satarupa Chakrabarti, and Soham Chakrabarti
8.1 Introduction 141
8.2 Nearest Neighbor Searching 142
8.3 Proposed Method 144
8.3.1 Power System Network Under Study 144
8.3.2 Proposed Fault Location Method 145
8.4 Results 146
8.4.1 Performance Varying Nearest Neighbor 147
8.4.2 Performance Varying Distance Matrices 147
8.4.3 Near Boundary Faults 148
8.4.4 Far Boundary Faults 149
8.4.5 Performance During High Resistance Faults 149
8.4.6 Single Pole to Ground Faults 150
8.4.7 Performance During Double Pole to Ground Faults 151
8.4.8 Performance During Pole to Pole Faults 151
8.4.9 Error Analysis 152
8.4.10 Comparison with Other Schemes 153
8.4.11 Advantages of the Scheme 154
8.5 Conclusion 154
Acknowledgment 154
References 154
9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 157
Md. I. H. Pathan, Mohammad S. Shahriar, Mohammad M. Rahman, Md. Sanwar Hossain, Nadia Awatif, and Md. Shafiullah
9.1 Introduction 157
9.2 Power System Models 159
9.2.1 PSS Integrated Single Machine Infinite Bus Power Network 159
9.2.2 PSS-UPFC Integrated Single Machine Infinite Bus Power Network 160
9.3 Methods 161
9.3.1 Group Method Data Handling Model 161
9.3.2 Extreme Learning Machine Model 162
9.3.3 Neurogenetic Model 162
9.3.4 Multigene Genetic Programming Model 163
9.4 Data Preparation and Model Development 165
9.4.1 Data Production and Processing 165
9.4.2 Machine Learning Model Development 165
9.5 Results and Discussions 166
9.5.1 Eigenvalues and Minimum Damping Ratio Comparison 166
9.5.2 Time-Domain Simulation Results Comparison 170
9.5.2.1 Rotor Angle Variation Under Disturbance 170
9.5.2.2 Rotor Angular Frequency Variation Under Disturbance 171
9.5.2.3 DC-Link Voltage Variation Under Disturbance 173
9.6 Conclusions 173
References 174
10 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 179
Jyoti Shukla, Basanta K. Panigrahi, and Monika Vardia
10.1 Introduction 179
10.2 PV-Wind Hybrid Power Generation Configuration 180
10.3 Proposed Systems Topologies 181
10.3.1 Structure of PV System 181
10.3.2 The MPPTs Technique 183
10.3.3 NN Predictive Controller Technique 183
10.3.4 ANFIS Technique 184
10.3.5 Training Data 186
10.4 Wind Power Generation Plant 187
10.5 Pitch Angle Control Techniques 189
10.5.1 PI Controller 189
10.5.2 NARMA-L2 Controller 190
10.5.3 Fuzzy Logic Controller Technique 192
10.6 Proposed DVRs Topology 192
10.7 Proposed Controlling Technique of DVR 193
10.7.1 ANFIS and PI Controlling Technique 193
10.8 Results of the Proposed Topologies 196
10.8.1 PV System Outputs (MPPT Techniques Results) 196
10.8.2 Main PV System outputs 196
10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result) 198
10.8.4 Proposed PMSG Wind Turbine System Output 199
10.8.5 Performance of DVR (Controlling Technique Results) 203
10.8.6 DVRs Performance 203
10.9 Conclusion 204
References 204
11 Deep Reinforcement Learning and Energy Price Prediction 207
Deepak Yadav, Saad Mekhilef, Brijesh Singh, and Muhyaddin Rawa
Abbreviations 207
11.1 Introduction 208
11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems 210
11.2.1 Reinforcement Learning 210
11.2.1.1 Markov Decision Process (MDP) 210
11.2.1.2 Value Function and Optimal Policy 211
11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings 212
11.2.3 Deep Reinforcement Learning Algorithms 212
11.3 Applications in Power Systems 213
11.3.1 Energy Management 213
11.3.2 Power Systems’ Demand Response (DR) 215
11.3.3 Electricity Market 216
11.3.4 Operations and Controls 217
11.4 Mathematical Formulation of Objective Function 218
11.4.1 Locational Marginal Prices (LMPs) Representation 219
11.4.2 Relative Strength Index (RSI) 219
11.4.2.1 Autoregressive Integrated Moving Average (ARIMA) 219
11.5 Interior-point Technique & KKT Condition 220
11.5.1 Explanation of Karush–Kuhn–Tucker Conditions 220
11.5.2 Algorithm for Finding a Solution 221
11.6 Test Results and Discussion 221
11.6.1 Illustrative Example 221
11.7 Comparative Analysis with Other Methods 223
11.8 Conclusion 224
11.9 Assignment 224
Acknowledgment 225
References 225
12 Power Quality Conditioners in Smart Power System 233
Zahira Rahiman, Lakshmi Dhandapani, Ravi Chengalvarayan Natarajan, Pramila Vallikannan, Sivaraman Palanisamy, and Sharmeela Chenniappan
12.1 Introduction 233
12.1.1 Voltage Sag 234
12.1.2 Voltage Swell 234
12.1.3 Interruption 234
12.1.4 Under Voltage 234
12.1.5 Overvoltage 234
12.1.6 Voltage Fluctuations 234
12.1.7 Transients 235
12.1.8 Impulsive Transients 235
12.1.9 Oscillatory Transients 235
12.1.10 Harmonics 235
12.2 Power Quality Conditioners 235
12.2.1 STATCOM 235
12.2.2 Svc 235
12.2.3 Harmonic Filters 236
12.2.3.1 Active Filter 236
12.2.4 UPS Systems 236
12.2.5 Dynamic Voltage Restorer (DVR) 236
12.2.6 Enhancement of Voltage Sag 236
12.2.7 Interruption Mitigation 237
12.2.8 Mitigation of Harmonics 241
12.3 Standards of Power Quality 244
12.4 Solution for Power Quality Issues 244
12.5 Sustainable Energy Solutions 245
12.6 Need for Smart Grid 245
12.7 What Is a Smart Grid? 245
12.8 Smart Grid: The “Energy Internet” 245
12.9 Standardization 246
12.10 Smart Grid Network 247
12.10.1 Distributed Energy Resources (DERs) 247
12.10.2 Optimization Techniques in Power Quality Management 247
12.10.3 Conventional Algorithm 248
12.10.4 Intelligent Algorithm 248
12.10.4.1 Firefly Algorithm (FA) 248
12.10.4.2 Spider Monkey Optimization (SMO) 250
12.11 Simulation Results and Discussion 254
12.12 Conclusion 257
References 257
13 The Role of Internet of Things in Smart Homes 259
Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Ebrahim Shiri, Hamid Haj Seyyed Javadi, Morteza Azimi Nasab, Mohammad Zand, and Tina Samavat
13.1 Introduction 259
13.2 Internet of Things Technology 260
13.2.1 Smart House 261
13.3 Different Parts of Smart Home 262
13.4 Proposed Architecture 264
13.5 Controller Components 265
13.6 Proposed Architectural Layers 266
13.6.1 Infrastructure Layer 266
13.6.1.1 Information Technology 266
13.6.1.2 Information and Communication Technology 266
13.6.1.3 Electronics 266
13.6.2 Collecting Data 267
13.6.3 Data Management and Processing 267
13.6.3.1 Service Quality Management 267
13.6.3.2 Resource Management 267
13.6.3.3 Device Management 267
13.6.3.4 Security 267
13.7 Services 267
13.8 Applications 268
13.9 Conclusion 269
References 269
14 Electric Vehicles and IoT in Smart Cities 273
Sanjeevikumar Padmanaban, Tina Samavat, Mostafa Azimi Nasab, Morteza Azimi Nasab, Mohammad Zand, and Fatemeh Nikokar
14.1 Introduction 273
14.2 Smart City 275
14.2.1 Internet of Things and Smart City 275
14.3 The Concept of Smart Electric Networks 275
14.4 IoT Outlook 276
14.4.1 IoT Three-layer Architecture 276
14.4.2 View Layer 276
14.4.3 Network Layer 277
14.4.4 Application Layer 278
14.5 Intelligent Transportation and Transportation 278
14.6 Information Management 278
14.6.1 Artificial Intelligence 278
14.6.2 Machine Learning 279
14.6.3 Artificial Neural Network 279
14.6.4 Deep Learning 280
14.7 Electric Vehicles 281
14.7.1 Definition of Vehicle-to-Network System 281
14.7.2 Electric Cars and the Electricity Market 281
14.7.3 The Role of Electric Vehicles in the Network 282
14.7.4 V2G Applications in Power System 282
14.7.5 Provide Baseload Power 283
14.7.6 Courier Supply 283
14.7.7 Extra Service 283
14.7.8 Power Adjustment 283
14.7.9 Rotating Reservation 284
14.7.10 The Connection between the Electric Vehicle and the Power Grid 284
14.8 Proposed Model of Electric Vehicle 284
14.9 Prediction Using LSTM Time Series 285
14.9.1 LSTM Time Series 286
14.9.2 Predicting the Behavior of Electric Vehicles Using the LSTM Method 287
14.10 Conclusion 287
Exercise 288
References 288
15 Modeling and Simulation of Smart Power Systems Using HIL 291
Gunapriya Devarajan, Puspalatha Naveen Kumar, Muniraj Chinnusamy, Sabareeshwaran Kanagaraj, and Sharmeela Chenniappan
15.1 Introduction 291
15.1.1 Classification of Hardware in the Loop 291
15.1.1.1 Signal HIL Model 291
15.1.1.2 Power HIL Model 292
15.1.1.3 Reduced-Scaled HIL Model 292
15.1.2 Points to Be Considered While Performing HIL Simulation 293
15.1.3 Applications of HIL 293
15.2 Why HIL Is Important? 293
15.2.1 Hardware-In-The-Loop Simulation 294
15.2.2 Simulation Verification and Validation 295
15.2.3 Simulation Computer Hardware 295
15.2.4 Benefits of Using Hardware-In-The-Loop Simulation 296
15.3 HIL for Renewable Energy Systems (RES) 296
15.3.1 Introduction 296
15.3.2 Hardware in the Loop 297
15.3.2.1 Electrical Hardware in the Loop 297
15.3.2.2 Mechanical Hardware in the Loop 297
15.4 HIL for HVDC and FACTS 299
15.4.1 Introduction 299
15.4.2 Modular Multi Level Converter 300
15.5 HIL for Electric Vehicles 301
15.5.1 Introduction 301
15.5.2 EV Simulation Using MATLAB, Simulink 302
15.5.2.1 Model-Based System Engineering (MBSE) 302
15.5.2.2 Model Batteries and Develop BMS 302
15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS) 303
15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software 304
15.5.2.5 Deploy, Integrate, and Test Control Algorithms 304
15.5.2.6 Data-Driven Workflows and AI in EV Development 305
15.6 HIL for Other Applications 306
15.6.1 Electrical Motor Faults 306
15.7 Conclusion 307
References 308
16 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 311
Geethanjali Muthiah, Meenakshi Devi Manivannan, Hemavathi Ramadoss, and Sharmeela Chenniappan
16.1 Introduction 311
16.2 ComparisonofPMUsandSCADA 312
16.3 Basic Structure of Phasor Measurement Units 313
16.4 PMU Deployment in Distribution Networks 314
16.5 PMU Placement Algorithms 315
16.6 Need/Significance of PMUs in Distribution System 315
16.6.1 Significance of PMUs – Concerning Power System Stability 316
16.6.2 Significance of PMUs in Terms of Expenditure 316
16.6.3 Significance of PMUs in Wide Area Monitoring Applications 316
16.7 Applications of PMUs in Distribution Systems 317
16.7.1 System Reconfiguration Automation to Manage Power Restoration 317
16.7.1.1 Case Study 317
16.7.2 Planning for High DER Interconnection (Penetration) 319
16.7.2.1 Case Study 319
16.7.3 Voltage Fluctuations and Voltage Ride-Through Related to DER 320
16.7.4 Operation of Islanded Distribution Systems 320
16.7.5 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection 322
16.8 Conclusion 322
References 323
17 Blockchain Technologies for Smart Power Systems 327
A. Gayathri, S. Saravanan, P. Pandiyan, and V. Rukkumani
17.1 Introduction 327
17.2 Fundamentals of Blockchain Technologies 328
17.2.1 Terminology 328
17.2.2 Process of Operation 329
17.2.2.1 Proof of Work (PoW) 329
17.2.2.2 Proof of Stake (PoS) 329
17.2.2.3 Proof of Authority (PoA) 330
17.2.2.4 Practical Byzantine Fault Tolerance (PBFT) 330
17.2.3 Unique Features of Blockchain 330
17.2.4 Energy with Blockchain Projects 330
17.2.4.1 Bitcoin Cryptocurrency 331
17.2.4.2 Dubai: Blockchain Strategy 331
17.2.4.3 Humanitarian Aid Utilization of Blockchain 331
17.3 Blockchain Technologies for Smart Power Systems 331
17.3.1 Blockchain as a Cyber Layer 331
17.3.2 Agent/Aggregator Based Microgrid Architecture 332
17.3.3 Limitations and Drawbacks 332
17.3.4 Peer to Peer Energy Trading 333
17.3.5 Blockchain for Transactive Energy 335
17.4 Blockchain for Smart Contracts 336
17.4.1 The Platform for Smart Contracts 337
17.4.2 The Architecture of Smart Contracting for Energy Applications 338
17.4.3 Smart Contract Applications 339
17.5 Digitize and Decentralization Using Blockchain 340
17.6 Challenges in Implementing Blockchain Techniques 340
17.6.1 Network Management 341
17.6.2 Data Management 341
17.6.3 Consensus Management 341
17.6.4 Identity Management 341
17.6.5 Automation Management 342
17.6.6 Lack of Suitable Implementation Platforms 342
17.7 Solutions and Future Scope 342
17.8 Application of Blockchain for Flexible Services 343
17.9 Conclusion 343
References 344
18 Power and Energy Management in Smart Power Systems 349
Subrat Sahoo
18.1 Introduction 349
18.1.1 Geopolitical Situation 349
18.1.2 Covid-19 Impacts 350
18.1.3 Climate Challenges 350
18.2 Definition and Constituents of Smart Power Systems 351
18.2.1 Applicable Industries 352
18.2.2 Evolution of Power Electronics-Based Solutions 353
18.2.3 Operation of the Power System 355
18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart 356
18.3.1 Digitalization of Power Industry 359
18.3.2 Storage Possibilities and Integration into Grid 360
18.3.3 Addressing Power Quality Concerns and Their Mitigation 362
18.3.4 A Path Forward Towards Holistic Condition Monitoring 363
18.4 Ways towards Smart Transition of the Energy Sector 366
18.4.1 Creating an All-Inclusive Ecosystem 366
18.4.1.1 Example of Sensor-Based Ecosystem 367
18.4.1.2 Utilizing the Sensor Data for Effective Analytics 368
18.4.2 Modular Energy System Architecture 370
18.5 Conclusion 371
References 373
Index 377
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