Artificial Intelligence-based Smart Power Systems

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: Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC/FACTs Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations Power and energy management systems 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.

Table of Contents:
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

About the Author :
Sanjeevikumar Padmanaban, PhD, is a Full Professor with the Department of Electrical Engineering, IT and Cybernetics, at the University of South-Eastern Norway, Porsgrunn, Norway. He serves as an Editor/Associate Editor/Editorial Board Member of many refereed journals, in particular, the IEEE Systems Journal, the IEEE Access Journal, IEEE Transactions on Industry Applications, the Deputy Editor/Subject Editor of IET Renewable Power Generation, and IET Generation, Transmission and Distribution Journal, Subject Editor of FACETS and Energies MDPI Journal. Sivaraman Palanisamy is a Program Manager - EV Charging Infrastructure in WRI India. He is an IEEE Senior Member, a Member of CIGRE, and Life Member of the Institution of Engineers (India). He is an active participant in the IEEE Standards Association. Sharmeela Chenniappan, PhD, is a Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She is an IEEE Senior Member, a Life Member of CBIP, and 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.