Microgrid Technologies
Description
Many aspects of microgrids are discussed in this volume, including, in the early chapters of the book, the various types of energy storage systems, power and energy management for microgrids, power electronics interface for AC & DC microgrids, battery management systems for microgrid applications, power system analysis for microgrids, and many others.
The middle section of the book presents the power quality problems in microgrid systems and its mitigations, gives an overview of various power quality problems and its solutions, describes the PSO algorithm based UPQC controller for power quality enhancement, describes the power quality enhancement and grid support through a solar energy conversion system, presents the fuzzy logic-based power quality assessments, and covers various power quality indices.
The final chapters in the book present the recent advancements in the microgrids, applications of Internet of Things (IoT) for microgrids, the application of artificial intelligent techniques, modeling of green energy smart meter for microgrids, communication networks for microgrids, and other aspects of microgrid technologies.
Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of microgrids, this is a must-have for any library.
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Sharmeela Chenniappan, PhD, is an associate professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She has 20 years of teaching experience at both the undergraduate and postgraduate levels and has done a number of research projects and consultancy work in renewable energy, power quality and design of power quality compensators for various industries. She is currently working on future books for the Wiley-Scrivener imprint.
Sivaraman Palanisamy has an M.E. in power systems engineering from Anna University, Chennai and is an assistant engineering manager at a leading engineering firm in India He has more than six years of experience in the field of power system studies and related areas and is an expert in many power systems simulation software programs. He is also currently working on other projects to be published under the Wiley-Scrivener imprint.
Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He is a fellow in multiple professional societies and associations and is an editor and contributor for multiple science and technical journals in this field. Like his co-editors, he is also currently working on other projects for Wiley-Scrivener.
Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University and is Head of the Esbjerg Energy Section. Through his research, he helped establish the Center for Bioenergy and Green Engineering in 2009 and serves as the head of the research group. He has vast experience in the field of bio-refineries and biogas production and has served as the technical advisory for many industries in this field.
Content
Foreword xxi
Acknowledgements xxiii
1 A Comprehensive Review on Energy Management in Micro-Grid System 1
Sanjay Kumar, R. K. Saket, Sanjeevikumar Padmanaban and Jens Bo Holm-Nielsen
1.1 Introduction 2
1.2 Generation and Storage System in MicroGrid 6
1.2.1 Distributed Generation of Electrical Power 6
1.2.2 Incorporation of Electric Car in Micro-Grid as a Device for Backup 7
1.2.3 Power and Heat Integration in Management System 8
1.2.4 Combination of Heat and Electrical Power System 9
1.3 System of Energy Management 10
1.3.1 Classification of MSE 10
1.3.1.1 MSE Based on Conventional Sources 10
1.3.1.2 MSE Based on SSE 10
1.3.1.3 MSE Based on DSM 11
1.3.1.4 MSE Based on Hybrid System 11
1.3.2 Steps of MSE During Problem Solving 11
1.3.2.1 Prediction of Uncertain Parameters 12
1.3.2.2 Uncertainty Modeling 12
1.3.2.3 Mathematical Formulation 12
1.3.2.4 Optimization 13
1.3.3 Micro-Grid in Islanded Mode 13
1.3.3.1 Objective Functions and Constraints of System 13
1.3.4 Micro-Grid Operation in Grid-Connected Mode 14
1.3.4.1 Objective Functions and Constraints of the Systems 14
1.4 Algorithms Used in Optimizing Energy Management System 16
1.5 Conclusion 19
References 20
2 Power and Energy Management in Microgrid 25
Jayesh J. Joglekar
2.1 Introduction 25
2.2 Microgrid Structure 26
2.2.1 Selection of Source for DG 27
2.2.1.1 Phosphoric Acid Fuel Cell (PAFC) 27
2.2.1.2 Mathematical Modeling of PAFC Fuel Cell 27
2.3 Power Flow Management in Microgrid 31
2.4 Generalized Unified Power Flow Controller (GUPFC) 33
2.4.1 Mathematical Modeling of GUPFC 34
2.5 Active GUPFC 38
2.5.1 Active GUPFC Control System 39
2.5.1.1 Series Converter 40
2.5.1.2 Shunt Converter 42
2.5.2 Simulation of Active GUPFC With General Test System 43
2.5.3 Simulation of Active GUPFC With IEEE 9 Bus Test System 43
2.5.3.1 Test Case: 1-Without GUPFC and Without Fuel Cell 45
2.5.3.2 Test Case: 2-Without GUPFC and With Fuel Cell 47
2.5.3.3 Test Case: 3-With GUPFC and Without Fuel Cell 48
2.5.3.4 Test Case: 4-With GUPFC and With Fuel Cell 49
2.5.3.5 Test Case: 5-With Active GUPFC 49
2.5.4 Summary 52
2.6 Appendix General Test System 53
2.6.1 IEEE 9 Bus Test System 53
References 55
3 Review of Energy Storage System for Microgrid 57
G.V. Brahmendra Kumar and K. Palanisamy
3.1 Introduction 58
3.2 Detailed View of ESS 60
3.2.1 Configuration of ESS 60
3.2.2 Structure of ESS With Other Devices 60
3.2.3 ESS Classifications 62
3.3 Types of ESS 62
3.3.1 Mechanical ESS 62
3.3.2 Flywheel ESS 63
3.3.3 CAES System 64
3.3.4 PHS System 65
3.3.5 CES Systems 66
3.3.6 Hydrogen Energy Storage (HES) 67
3.3.7 Battery-Based ESS 68
3.3.8 Electrical Energy Storage (EES) System 71
3.3.8.1 Capacitors 71
3.3.8.2 Supercapacitors (SCs) 72
3.3.9 SMES 73
3.3.10 Thermal Energy Storage Systems (TESS) 74
3.3.10.1 SHS 75
3.3.10.2 Latent 75
3.3.10.3 Absorption 75
3.3.10.4 Hybrid ESS 76
3.4 Comparison of Current ESS on Large Scale 77
3.5 Importance of Storage in Modern Power Systems 77
3.5.1 Generation Balance and Fluctuation in Demand 77
3.5.2 Intermediate Penetration of Renewable Energy 77
3.5.3 Use of the Grid 80
3.5.4 Operations on the Market 80
3.5.5 Flexibility in Scheduling 80
3.5.6 Peak Shaving Support 80
3.5.7 Improve the Quality of Power 81
3.5.8 Carbon Emission Control 81
3.5.9 Improvement of Service Efficiency 81
3.5.10 Emergency Assistance and Support for Black Start 81
3.6 ESS Issues and Challenges 81
3.6.1 Selection of Materials 82
3.6.2 ESS Size and Cost 82
3.6.3 Energy Management System 83
3.6.4 Impact on the Environment 83
3.6.5 Issues of Safety 83
3.7 Conclusion 84
Acknowledgment 85
References 85
4 Single Phase Inverter Fuzzy Logic Phase Locked Loop 91
Maxwell Sibanyoni, S.P. Daniel Chowdhury and L.J. Ngoma
4.1 Introduction 91
4.2 PLL Synchronization Techniques 92
4.2.1 T/4 Transport Delay PLL 95
4.2.2 Inverse Park Transform PLL 96
4.2.3 Enhanced PLL 97
4.2.4 Second Order Generalized Integrator Orthogonal Signal Generator Synchronous Reference Frame (SOGI-OSG SRF) PLL 98
4.2.5 Cascaded Generalized Integrator PLL (CGI-PLL) 99
4.2.6 Cascaded Delayed Signal Cancellation PLL 100
4.3 Fuzzy Logic Control 101
4.4 Fuzzy Logic PLL Model 103
4.4.1 Fuzzification 103
4.4.2 Inference Engine 105
4.4.3 Defuzzification 108
4.5 Simulation and Analysis of Results 110
4.5.1 Test Signal Generator 110
4.5.2 Proposed SOGI FLC PLL Performance Under Fault Conditions 113
4.5.2.1 Test Case 1 113
4.5.2.2 Test Case 2 114
4.5.2.3 Test Case 3 115
4.5.2.4 Test Case 4 115
4.5.2.5 Test Case 5 116
4.5.2.6 Test Case 6 117
4.6 Conclusion 118
Acknowledgment 118
References 119
5 Power Electronics Interfaces in Microgrid Applications 121
Indrajit Sarkar
5.1 Introduction 122
5.2 Microgrid Classification 122
5.2.1 AC Microgrid 122
5.2.2 DC Microgrids 124
5.2.3 Hybrid Microgrid 126
5.3 Role of Power Electronics in Microgrid Application 127
5.4 Power Converters 128
5.4.1 DC/DC Converters 128
5.4.2 Non-Isolated DC/DC Converters 129
5.4.2.1 Maximum Power Point Tracking (MPPT) 130
5.4.3 Isolated DC/DC Converters 135
5.4.4 AC to DC Converters 137
5.4.5 DC to AC Converters 139
5.5 Conclusion 143
References 143
6 Reconfigurable Battery Management System for Microgrid Application 145
Saravanan, S., Pandiyan, P., Chinnadurai, T., Ramji, Tiwari., Prabaharan, N., Senthil Kumar, R. and Lenin Pugalhanthi, P.
6.1 Introduction 146
6.2 Individual Cell Properties 147
6.2.1 Modeling of Cell 147
6.2.1.1 Second Order Model 147
6.2.2 Simplified Non-Linear Model 148
6.3 State of Charge 149
6.4 State of Health 150
6.5 Battery Life 150
6.6 Rate Discharge E¿ect 151
6.7 Recovery E¿ect 152
6.8 Conventional Methods and its Issues 152
6.8.1 Series Connected 152
6.8.2 Parallel Connected 154
6.9 Series-Parallel Connections 154
6.10 Evolution of Battery Management System 155
6.10.1 Necessity for Reconfigurable BMS 156
6.10.2 Conventional R-BMS Methods 156
6.10.2.1 First Design 157
6.10.2.2 Series Topology 158
6.10.2.3 Self X Topology 158
6.10.2.4 Dependable Efficient Scalable Architecture Method 159
6.10.2.5 Genetic Algorithm-Based Method 160
6.10.2.6 Graph-Based Technique 161
6.10.2.7 Power Tree-Based Technique 162
6.11 Modeling of Reconfigurable-BMS 163
6.12 Real Time Design Aspects 164
6.12.1 Sensing Module Stage 165
6.12.2 Control Module Stage 165
6.12.2.1 Health Factor of Reconfiguration 166
6.12.2.2 Reconfiguration Time Delay and Transient Load Supply 166
6.12.3 Actuation Module 167
6.12.3.1 Order of Switching 167
6.12.3.2 Stress and Faults of Switches 169
6.12.3.3 Determining Number of Cells in a Module 170
6.13 Opportunities and Challenges 171
6.13.1 Modeling and Simulation 171
6.13.2 Hardware Design 171
6.13.3 Granularity 171
6.13.4 Hardware Overhead 172
6.13.5 Intelligent Algorithms 172
6.13.6 Distributed Reconfigurable Battery Systems 172
6.14 Conclusion 173
References 173
7 Load Flow Analysis for Micro Grid 177
Sivaraman Palanisamy, Dr. Sharmeela Chenniappan and Dr. S. Elango
7.1 Introduction 177
7.1.1 Islanded Mode of Operation 178
7.1.2 Grid Connected Mode of Operation 178
7.2 Load Flow Analysis for Micro Grid 179
7.3 Example 179
7.3.1 Power Source 180
7.4 Energy Storage System 180
7.5 Connected Loads 182
7.6 Reactive Power Compensation 182
7.7 Modeling and Simulation 182
7.7.1 Case 1 182
7.7.2 Case 2 184
7.7.3 Case 3 187
7.7.4 Case 4 189
7.7.5 Case 5 191
7.8 Conclusion 193
References 195
8 AC Microgrid Protection Coordination 197
Ali M. Eltamaly, Yehia Sayed Mohamed, Abou-Hashema M. El-Sayed and Amer Nasr A. Elghaffar
8.1 Introduction 197
8.2 Fault Analysis 200
8.2.1 Symmetrical Fault Analysis 201
8.2.2 Single Line to Ground Fault 202
8.2.3 Line-to-Line Fault 204
8.2.4 Double Line-to-Ground Fault 206
8.3 Protection Coordination 208
8.3.1 Overcurrent Protection 209
8.3.2 Directional Overcurrent/Earth Fault Function 211
8.3.3 Distance Protection Function 214
8.3.4 Distance Acceleration Scheme 217
8.3.5 Under/Over Voltage/Frequency Protection 219
8.4 Conclusion 221
Acknowledgment 224
References 224
9 A Numerical Approach for Estimating Emulated Inertia With Decentralized Frequency Control of Energy Storage Units for Hybrid Renewable Energy Microgrid System 227
Shubham Tiwari, Jai Govind Singh and Weerakorn Ongsakul
9.1 Introduction 228
9.2 Proposed Methodology 231
9.2.1 Response in Conventional Grids 231
9.2.2 Strategy for Digital Inertia Emulation in Hybrid Renewable Energy Microgrids 232
9.2.3 Proposed Mathematical Formulation for Estimation of Digital Inertia Constant for Static Renewable Energy Sources 235
9.3 Results and Discussions 238
9.3.1 Test System 238
9.3.2 Simulation and Study of Case 1 241
9.3.2.1 Investigation of Scenario A 241
9.3.2.2 Investigation of Scenario B 243
9.3.2.3 Discussion for Case 1 245
9.3.3 Simulation and Study of Case 2 246
9.3.3.1 Investigation of Scenario A 246
9.3.3.2 Investigation of Scenario B 248
9.3.3.3 Discussion for Case 2 250
9.3.4 Simulation and Study for Case 3 250
9.3.4.1 Discussion for Case 3 251
9.4 Conclusion 252
References 253
10 Power Quality Issues in Microgrid and its Solutions 255
R. Zahira, D. Lakshmi and C.N. Ravi
10.1 Introduction 256
10.1.1 Benefits of Microgrid 257
10.1.2 Microgrid Architecture 257
10.1.3 Main Components of Microgrid 258
10.2 Classification of Microgrids 258
10.2.1 Other Classifications 259
10.2.2 Based on Function Demand 259
10.2.3 By AC/DC Type 259
10.3 DC Microgrid 260
10.3.1 Purpose of the DC Microgrid System 260
10.4 AC Microgrid 261
10.5 AC/DC Microgrid 262
10.6 Enhancement of Voltage Profile by the Inclusion of RES 263
10.6.1 Sample Microgrid 263
10.7 Power Quality in Microgrid 267
10.8 Power Quality Disturbances 270
10.9 International Standards for Power Quality 270
10.10 Power Quality Disturbances in Microgrid 271
10.10.1 Modeling of Microgrid 271
10.11 Shunt Active Power Filter (SAPF) Design 272
10.11.1 Reference Current Generation 274
10.12 Control Techniques of SAPF 276
10.13 SPWM Controller 277
10.14 Sliding Mode Controller 277
10.15 Fuzzy-PI Controller 278
10.16 GWO-PI Controller 279
10.17 Metaphysical Description of Optimization Problems With GWO 281
10.18 Conclusion 284
References 285
11 Power Quality Improvement in Microgrid System Using PSO-Based UPQC Controller 287
T. Eswara Rao, Krishna Mohan Tatikonda, S. Elango and J. Charan Kumar
11.1 Introduction 288
11.2 Microgrid System 289
11.2.1 Wind Energy System 290
11.2.1.1 Modeling of Wind Turbine System 290
11.2.2 Perturb and Observe MPPT Algorithm 291
11.2.3 MPPT Converter 291
11.3 Unified Power Quality Conditioner 293
11.3.1 UPQC Series Converter 293
11.3.2 UPQC Shunt APF Controller 295
11.4 Particle Swarm Optimization 297
11.4.1 Velocity Function 297
11.4.2 Analysis of PSO Technique 298
11.5 Simulation and Results 299
11.5.1 Case 1: With PI Controller 300
11.5.2 Case 2: With PSO Technique 301
11.6 Conclusion 304
References 305
12 Power Quality Enhancement and Grid Support Using Solar Energy Conversion System 309
CH. S. Balasubrahmanyam, Om Hari Gupta and Vijay K. Sood
12.1 Introduction 309
12.2 Renewable Energy and its Conversion Into Useful Form 312
12.3 Power System Harmonics and Their Cause 313
12.4 Power Factor (p.f.) and its Effects 316
12.5 Solar Energy System With Power Quality Enhancement (SEPQ) 317
12.6 Results and Discussions 320
12.6.1 Mode-1 (SEPQ as STATCOM) 320
12.6.2 Mode-2 (SEPQ as Shunt APF) 320
12.6.3 Mode-3 (SEPQ as D-STATCOM) 322
12.7 Conclusion 326
References 327
13 Power Quality Improvement of a 3-Phase-3-Wire Grid-Tied PV-Fuel Cell System by 3-Phase Active Filter Employing Sinusoidal Current Control Strategy 329
Rudranarayan Senapati, Sthita Prajna Mishra, Rajendra Narayan Senapati and Priyansha Sharma
13.1 Introduction 330
13.2 Active Power Filter (APF) 333
13.2.1 Shunt Active Power Filter (ShPF) 334
13.2.1.1 Configuration of ShPF 334
13.2.2 Series Active Power Filter (SAF) 335
13.2.2.1 Configuration of SAF 336
13.3 Sinusoidal Current Control Strategy (SCCS) for APFs 337
13.4 Sinusoidal Current Control Strategy for ShPF 342
13.5 Sinusoidal Current Control Strategy for SAF 349
13.6 Solid Oxide Fuel Cell (SOFC) 353
13.6.1 Operation 354
13.6.2 Anode 355
13.6.3 Electrolyte 355
13.6.4 Cathode 356
13.6.5 Comparative Analysis of Various Fuel Cells 356
13.7 Simulation Analysis 356
13.7.1 Shunt Active Power Filter 358
13.7.1.1 ShPF for a 3-f 3-Wire (3P3W) System With Non-Linear Loading 358
13.7.1.2 For a PV-Grid System (Constant Irradiance Condition) 360
13.7.1.3 For a PV-SOFC Integrated System 364
13.7.2 Series Active Power Filter 366
13.7.2.1 SAF for a 3-f 3-Wire (3P3W) System With Non-Linear Load Condition 366
13.7.2.2 For a PV-Grid System (Constant Irradiance Condition) 368
13.7.2.3 For a PV-SOFC Integrated System 370
13.8 Conclusion 373
References 373
14 Application of Fuzzy Logic in Power Quality Assessment of Modern Power Systems 377
V. Vignesh Kumar and C.K. Babulal
14.1 Introduction 378
14.2 Power Quality Indices 379
14.2.1 Total Harmonic Distortion 379
14.2.2 Total Demand Distortion 380
14.2.3 Power and Power Factor Indices 380
14.2.4 Transmission Efficiency Power Factor (TEPF) 381
14.2.5 Oscillation Power Factor (OSCPF) 382
14.2.6 Displacement Power Factor (DPF) 383
14.3 Fuzzy Logic Systems 383
14.4 Development of Fuzzy Based Power Quality Evaluation Modules 384
14.4.1 Stage I: Fuzzy Logic Based Total Demand Distortion 385
14.4.1.1 Performance of FTDDF Under Sinusoidal Situations 388
14.4.1.2 Performance of FTDDF Under Nonsinusoidal Situations 389
14.4.2 Stage II-Fuzzy Representative Quality Power Factor (FRQPF) 390
14.4.2.1 Performance of FRQPF Under Sinusoidal and Nonsinusoidal Situations 393
14.4.3 Stage III-Fuzzy Power Quality Index (FPQI) Module 395
14.4.3.1 Performance of FPQI Under Sinusoidal and Nonsinusoidal Situations 397
14.5 Conclusion 401
References 401
15 Applications of Internet of Things for Microgrid 405
Vikram Kulkarni, Sarat Kumar Sahoo and Rejo Mathew
15.1 Introduction 405
15.2 Internet of Things 408
15.2.1 Architecture and Design 409
15.2.2 Analysis of Data Science 410
15.3 Smart Micro Grid: An IoT Perspective 410
15.4 Literature Survey on the IoT for SMG 411
15.4.1 Advanced Metering Infrastructure Based on IoT for SMG 414
15.4.2 Sub-Systems of AMI 414
15.4.3 Every Smart Meter Based on IoT has to Provide the Following Functionalities 416
15.4.4 Communication 417
15.4.5 Cloud Computing Applications for SMG 418
15.5 Cyber Security Challenges for SMG 419
15.6 Conclusion 421
References 423
16 Application of Artificial Intelligent Techniques in Microgrid 429
S. Anbarasi, S. Ramesh, S. Sivakumar and S. Manimaran
16.1 Introduction 430
16.2 Main Problems Faced in Microgrid 431
16.3 Application of AI Techniques in Microgrid 431
16.3.1 Power Quality Issues and Control 432
16.3.1.1 Preamble of Power Quality Problem 432
16.3.1.2 Issues with Control and Operation of MicroGrid Systems 433
16.3.1.3 AI Techniques for Improving Power Quality Issues 434
16.3.2 Energy Storage System With Economic Power Dispatch 438
16.3.2.1 Energy Storage System in Microgrid 438
16.3.2.2 Need for Intelligent Approaches in Energy Storage System 440
16.3.2.3 Intelligent Methodologies for ESS Integrated in Microgrid 441
16.3.3 Energy Management System 444
16.3.3.1 Description of Energy Management System 444
16.3.3.2 EMS and Distributed Energy Resources 445
16.3.3.3 Intelligent Energy Management for a Microgrid 446
16.4 Conclusion 448
References 449
17 Mathematical Modeling for Green Energy Smart Meter for Microgrids 451
Moloko Joseph Sebake and Meera K. Joseph
17.1 Introduction 451
17.1.1 Smart Meter 452
17.1.2 Green Energy 453
17.1.3 Microgrid 453
17.1.4 MPPT Solar Charge Controller 454
17.2 Related Work 454
17.3 Proposed Technical Architecture 456
17.3.1 Green Energy Smart Meter Architecture 456
17.3.2 Solar Panel 456
17.3.3 MPPT Controller 456
17.3.4 Battery 457
17.3.5 Solid-State Switch 457
17.3.6 Electrical Load 457
17.3.7 Solar Voltage Sensor 457
17.3.8 Batter Voltage Sensor 458
17.3.9 Current Sensor 458
17.3.10 Microcontroller 458
17.3.11 Wi-Fi Module 458
17.3.12 GSM/3G/LTE Module 459
17.3.13 LCD Display 459
17.4 Proposed Mathematical Model 459
17.5 Results 462
Conclusion 468
References 469
18 Microgrid Communication 471
R. Sandhya and Sharmeela Chenniappan
18.1 Introduction 471
18.2 Reasons for Microgrids 473
18.3 Microgrid Control 474
18.4 Control Including Communication 474
18.5 Control with No Communication 475
18.6 Requirements 478
18.7 Reliability 478
18.8 Microgrid Communication 479
18.9 Microgrid Communication Networks 481
18.9.1 Wi-Fi 481
18.9.2 WiMAX-Based Network 482
18.9.3 Wired and Wireless-Based Integrated Network 482
18.9.4 Smart Grids 482
18.10 Key Aspects of Communication Networks in Smart Grids 483
18.11 Customer Premises Network (CPN) 483
18.12 Architectures and Technologies Utilized in Communication Networks Within the Transmission Grid 485
References 487
19 Placement of Energy Exchange Centers and Bidding Strategies for Smartgrid Environment 491
Balaji, S. and Ayush, T.
19.1 Introduction 491
19.1.1 Overview 491
19.1.2 Energy Exchange Centers 492
19.1.3 Energy Markets 493
19.2 Local Energy Centers and Optimal Placement 495
19.2.1 Problem Formulation (Clustering of Local Energy Market) 496
19.2.2 Clustering Algorithm 496
19.2.3 Test Cases 497
19.2.4 Results and Discussions 498
19.2.5 Conclusions for Simulations Based on Modified K Means Clustering for Optimal Location of EEC 501
19.3 Local Energy Markets and Bidding Strategies 503
19.3.1 Prosumer Centric Retail Electricity Market 504
19.3.2 System Modeling 505
19.3.2.1 Prosumer Centric Framework 505
19.3.2.2 Electricity Prosumers 505
19.3.2.3 Modeling of Utility Companies 507
19.3.2.4 Modeling of Distribution System Operator (DSO) 507
19.3.2.5 Supply Function Equilibrium 507
19.3.2.6 Constraints 508
19.3.3 Solution Methodology 509
19.3.3.1 Game Theory Approach 509
19.3.3.2 Relaxation Algorithm 511
19.3.3.3 Bi-Level Algorithm 511
19.3.3.4 Simulation Results 512
19.3.3.5 Nikaido-Isoda Formulation 513
19.3.4 Case Study 513
19.3.4.1 Plots 514
19.3.4.2 Anti-Dumping 514
19.3.4.3 Macro-Control 514
19.3.4.4 Sensitivity Analysis 514
Conclusion 517
References 518
Index 521
1
A Comprehensive Review on Energy Management in Micro-Grid System
Sanjay Kumar1*, R. K. Saket2, P. Sanjeevikumar3 and Jens Bo Holm-Nielsen3
1Electrical & Electronics Engineering Department, Centurion University of Technology and Management, Odisha, India
2Electrical Engineering Department, IIT (BHU) Varanasi, India
3Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, Esbjerg, Denmark
Abstract
Since the last decades there has been a huge exploitation of non-conventional (renewable) energy resources which has increased significantly. A Micro-Grid (MG) presents an appropriate concept to integrate local resources of renewable energy with Storage System of Energy (SSE) and are synchronized to supply the customers' requirement in different circumstances. Apart from this, the Management System of Energy (MSE) has been analyzed for allocating the output power from the Distributed Generator (DG) units optimally. It also satisfies the local demand, regulates the voltage and frequency of the smart MG, and secures a flat switching between modes, connected to grid and mode of islanded operation. In this article the authors have made an outmost effort about the area of MG-MSE, evolved in the current framework, discussing the storage units and generation of MG, the importance of Electric Vehicles (EVs) as a backup unit, combined heat and power for supplying the thermal requirements, objectives function of MG-MSE and constraints of the systems and current algorithms of optimization.
Keywords: Management System of Energy (MSE), Micro-Grid (MG), Electric Vehicles (EVs), Heat and Power Combination (HPC), optimization algorithms
1.1 Introduction
Previously, fossil fuel represents the popular source of electricity, creating emissions of gaseous pollutant in our environment. This led to the advent of renewable energy sources, which provided clean energy and have become the most suitable choice for mitigating the dependency on fossil fuel as it also decreases the Greenhouse effect from the atmosphere. The other natural resources included solar, wind, ocean, biomass, hydro, and geothermal, etc. and were capable of producing electrical energy.
As per the renewable global status report of 2017, the power sector is mainly concerned about the renewable technologies policy, which gives a facility of integration of it into the existing energy systems. Figure 1.1 shows the universal electricity creation until the end of 2016, and also shows that about 30.28% of the entire power is depicted by means of the renewable sources. The estimation is about 2,017 GW out of 6,660 GW the total global electrical energy capacity. Hydropower takes the 1st position in the production of RE.
Clearly, it is shown in Figure 1.2 that about 16.46% of the total power is supplied by the hydropower. It is 54.34% of the RE. The wind power follows it, which is about 7.3% of the total and 24.15% of renewable sources. Then comes Photovoltaic (PV) power (15.02% of RE), about 4.55% of total power capacity, bio-power (5.55% of RE) is about 1.7%, and others are 0.9%. But in 2016, the newly implemented renewable energy is increased by solar (PV) power (47%), wind power (15.5%) and hydropower (9) compared to 2015 [1].
Figure 1.3 shows the consumption of energy sources like natural gas, coal, renewable sources, nuclear, petroleum and other liquid fuels from the year 1090 to 2040 [2]. According to this, consumption of renewable sources is going to increase by 30% in the duration from 2015 to 2040.
Figure 1.1 The renewable global status report.
Figure 1.2 Consumption of renewable energy sources.
Figure 1.3 Intensification of various energy sources from 1990 to 2040.
Nowadays, maximum research is going on the utility of renewable energy. Micro-grid integration has taken the maximum concentration of researchers. A micro-grid (MG) is a distributed network that includes different possible distributed resources of energy, which can be considered as a single system, that can be optimizable, means can be balanced with generations and loads with a suitable storage system. It is also capable of doing work independently with or without connection with the utility power grid [3].
A micro-grid is a network or an association of local electrical generation resources connected through a common point known as PCC. The micro-grid has one or more renewable generation units, conventional power generation system, the electrical storage system (ESS), the thermal storage system (TSS), electrical loads, and thermal loads. The conventional power generation system can consist of the micro turbine (MT), diesel generator set, etc. Similarly, in renewable generation units, we can make a photovoltaic (PV) generation system, wind turbines, biomass and hydrokinetic system, etc.There are two manners of operation of a micro-grid namely, connected to grid manner, and the other one is island manner. In GC mode a microgrid is in link with the conventional power grid. It is capable of participating in the energy market as a seller or buyer that can exchange energy with the utility. However, in the islanded manner, micro-grid functions as an autonomous body, as disconnected from the utility network. The island condition occurs due to several reasons such as geographical position on earth, brownout and economic issues.
Generally, the microgrids face a problem of supplying demand properly due to insufficient energy generation. This problem is because of the irregularity of loads and the RE sources [4]. So, a management system is required to deal with the problem. This management system is called a management system of energy (MSE).
The management system of energy (MSE) is the collection of different control approaches and different active, ongoing exercises along with hardware and software to mitigate the energy management objectives problems. It is a multiple functionary system. It is a system which distributes the generated power optimally and economically to the load with proper voltage regulation and frequency regulation of the micro-grid. So, we can get a smooth changeover between connected to grid manner and isolated manner of micro-grid systems, according to micro-grid components and real-time load conditions of operation. This system can be used to get high-quality power, sustainability, reliability and environmentally friendly power source.
Due to this, researchers started focusing extensively on MSE strategies for various micro-grids ranging from small home (small scale) to cities or towns (large scale) as shown in the different scales of MG of Figure 1.4.
Exploration has started on a study of the practicability of the parts of micro-grid, and it is modeled by scheduling the DGs, and forecasting the data of environmental conditions such as irradiance of sunray, speed of wind flow and water speed, forecast of energy demand and on availability optimization algorithms in the intention of cost-effective benefits with minimum impact on the atmosphere. Figure 1.5 shows the central micro-grid arrangement and components related to the energy management system. It shows that the research is mainly focused on the resolution of different problems regarding the micro-grid by means of an energy management system.
Figure 1.4 Various ranges of MG.
Figure 1.5 Components of micro-grid.
This is a review of the different features of micro-grid and management of energy that are presented in various current topics, starting from various units of MG, synchronization of components to the recent optimization methodology with system limitations and objective functions for MSE.
Afterwards, the different configuration and components of micro-grid are deployed in Section 1.2. Here the different structures used in an energy management system with various distributed energy sources, various storage systems and different type of load or the consumer behavior are illustrated. Followed by Section 1.3, it depicts the MG-MSE and different modes of operation. Further, Section 1.4 discusses about the recent techniques and algorithms used for optimization in energy management systems. Finally, Section 1.5 concludes the different sections and further deliberates the strategy for further research in this area.
1.2 Generation and Storage System in MicroGrid
1.2.1 Distributed Generation of Electrical Power
In recent years, a great number of researchers are discussing the issues in micro-grids. Nowadays, the verities of micro-grid structures have been presented in the literature. In micro-grid the different distributed generations are utilized because of its geographical characteristic, economic benefits, environmental condition and impact on the surrounding. In Ref. [5], there is a discussion on predictive MSE with its control strategies and communication of micro-grid to the independent utility grid. A 1.5 MW wind energy associated with the battery as a storage system is analyzed. In Ref. [6] a hybrid hydrokinetic system HKT battery is used in GC mode. The surplus power of HKT is utilized for charging a battery or trade to the grid. Whether the power will be sold to the main grid or not is determined by the charge condition of the battery and the principle of the utility grid network. The power requirement of the load is provided by...
