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This state-of-the-art book offers cutting-edge optimization techniques and practical decision-making frameworks essential for enhancing the efficiency and reliability of sustainable energy systems, making it an invaluable resource for researchers, policymakers, and energy professionals.
Optimization in Sustainable Energy: Methods and Applications brings together valuable knowledge, methods, and practical examples to help scholars, researchers, professionals, and policymakers address the growing challenges of optimizing sustainable energy. This volume covers a range of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, providing both theoretical understanding and practical guidance.
The volume explores challenges and opportunities surrounding the integration of multi-criteria decision-making techniques in energy planning, highlighting insights on environmental, economic, and social factors influencing the strategic allocation of resources. The use of evolutionary algorithms, machine learning, and metaheuristics to optimize energy storage, distribution, and optimization are also discussed.
The transition towards sustainable energy is at the forefront of global priorities, driven by the urgent need to mitigate climate change, reduce carbon emissions, and enhance energy security. As countries and industries increasingly prioritize renewable sources like wind, solar, and hydroelectric power, the complexity of optimizing these systems becomes a critical challenge. Optimization in Sustainable Energy: Methods and Applications, is a comprehensive exploration of cutting-edge methodologies used to enhance the efficiency, reliability, and performance of sustainable energy systems.
Audience
Research scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.
Prasenjit Chatterjee, PhD, is a professor and the Dean of Research and Consultancy at the MCKV Institute of Engineering. He has published 135 research papers and 43 books and serves as a lead series editor for several international book series. He is known for his work developing the MARCOS and RAFSI decision-making methods. His research interests include energy optimization, intelligent decision-making, fuzzy computing, sustainability modeling, and supply chain management.
Anita Khosla, PhD, is a professor at Manav Rachna International Institute of Research and Studies with over 27 years of teaching experience. She has published three books and over 50 papers in international journals and conferences and served as a speaker and organizer for numerous conferences and seminars. She is known for her coordination in establishing the Factory Automation Lab in conjunction with Mitsubishi Electric India.
Ashwani Kumar, PhD, is an associate professor in the Department of Electrical and Instrumentation Engineering at the Sant Longowal Institute of Engineering and Technology, Longowal, India with over 26 years of experience. He has over 70 publications in book chapters and international journals and conferences. He is the recipient of the Monbukagakusho and Quality Improvement Programme scholarships. His research interests include computer vision, artificial intelligence, and remote sensing.
Gülay Demir, PhD, is an associate professor at the School of Health Services at Sivas Cumhuriyet University with over 10 years of academic experience. She is the author of three books and 50 scientific articles, and the editor of two books. Her research interests include smart grids, renewable energy, and fuzzy logic.
Preface xvii
Acknowledgment xxi
Part I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy 1
1 Strategic Roadmap for Turkey's Sustainable Energy Transition: A Multi-Criteria Perspective 3Gülay Demir and Prasenjit Chatterjee
1.1 Introduction 4
1.1.1 Research Goals 5
1.1.1.1 Research Questions 5
1.1.1.2 Contributions and Novelty 6
1.1.1.3 Organization of the Chapter 6
1.2 Literature Review 6
1.2.1 MCDM Research on Renewable Energy 7
1.2.2 Studies Used WENSLO and RAWEC Methods 8
1.2.3 Research Gaps 8
1.3 Methodology for Research 8
1.3.1 WENSLO Method for Criteria Prioritization 9
1.3.2 RAWEC Method to Rank Alternatives 11
1.3.2.1 Case Study 12
1.4 Results 14
1.4.1 Application of WENSLO Method 14
1.4.2 Application of the RAWEC Method 17
1.4.3 Sensitivity Analysis 17
1.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights 17
1.4.3.2 Comparison With Other MCDM Methods 20
1.5 Discussion, Practical and Managerial Implications 21
1.6 Conclusions, Limitations, and Future Directions 21
References 23
2 A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources 27Sanjib Biswas, Gülay Demir and Prasenjit Chatterjee
2.1 Introduction 28
2.2 Literature Review 30
2.2.1 Research Gaps 31
2.2.2 Research Objectives 31
2.3 Preliminary Concepts: p, q-QOFS 32
2.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation 35
2.5 Materials and Methods 42
2.5.1 Theoretical Framework: Selection of Criteria 43
2.5.2 Expert Group 44
2.5.3 Methodological Framework 45
2.5.3.1 Stages in the Methodological Framework 45
2.5.3.2 Procedural Steps 45
2.6 Findings 50
2.7 Discussions 56
2.8 Conclusion and Future Scope 58
References 59
Appendix A 64
3 Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach 69Gülay Demir and Prasenjit Chatterjee
3.1 Introduction 70
3.1.1 Purpose and Importance of the Study 72
3.1.2 Research Questions 73
3.1.3 Contributions 74
3.1.4 Research Gaps 76
3.2 Literature Review 78
3.2.1 Carbon Footprint Assessment and MCDM Methods 78
3.2.2 Studies with WENSLO and RAWEC Methods 80
3.3 Research Methodology 81
3.3.1 Fundamentals of FST 81
3.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies 82
3.3.3 F-RAWEC Method for Ranking Strategies 85
3.4 Case Study 87
3.4.1 Identification and Explanation of Criteria 87
3.4.2 Carbon Footprint Reduction Strategies 87
3.4.3 Data Collection and Analysis 87
3.4.4 Determining Subjective Weights Using F-WENSLO Method 93
3.4.5 Results of F-RAWEC Application 103
3.5 Insights, Applications, and Managerial Implications 105
3.5.1 Analysis of Rankings 105
3.5.2 Application Implications 106
3.5.3 Managerial Implications 107
3.6 Conclusions, Limitations, and Future Directions 108
References 110
4 Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment 113Ömer Faruk Görçün, Hande Küçükönder and Ahmet Çalik
4.1 Introduction 114
4.2 The Research Background 116
4.2.1 Common Findings in the Literature 124
4.2.2 Trends in the Literature 125
4.2.3 Current State of the Literature 125
4.2.4 Research and Theoretical Gaps 126
4.2.5 Motivations and Objectives of the Study 128
4.3 The Suggested Model 129
4.3.1 Preliminaries on Neutrosophic Sets 129
4.3.2 Identifying the Experts' Reputation 132
4.3.3 Identifying the Criteria Weights 135
4.3.3.1 Determining the Subjective Weights of the Criteria 135
4.3.3.2 Identifying the Objective Weights of the Criteria 136
4.3.3.3 Associating the Subjective and Objective Weights 139
4.3.4 Ranking the Alternatives 139
4.4 Implementing the Model to Identify the Best Sustainable Energy Strategy 142
4.4.1 The Preparation Process 142
4.4.1.1 Description of the Problem 142
4.4.1.2 Forming the Board of Experts 143
4.4.1.3 Identifying the Criteria and Alternatives 145
4.4.2 Determining the Weights of the Criteria 153
4.4.3 Ranking the Alternatives 167
4.5 Results and Discussions 167
4.5.1 Rank and Influence of the Criteria 168
4.5.2 Sustainable Energy Strategies and Their Ranking 168
4.5.3 Importance, Influence, and Impacts of Results 170
4.5.4 Novelties, Managerial, and Policy Implications 170
4.5.5 Theoretical Contributions of the Decision-Making Model 171
4.6 Conclusions and Future Research Direction 171
References 172
5 ENTROPY-Based Evaluation of Global Renewable Energy Trends 183Rahim Arslan
5.1 Introduction 183
5.2 Renewable Energy Concepts 185
5.3 World Countries and Türkiye in Clean Energy 187
5.4 Evaluation of Renewable Energy Resources Using MCDM Methods 189
5.5 ENTROPY Method 189
5.6 Case Study 192
5.6.1 Renewable Energy Weights According to Installed Capacity 193
5.7 Conclusions 204
References 205
Part II: Optimization Techniques in Sustainable Energy 207
6 Optimization in Sustainable Energy: A Bibliometric Analysis 209Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan
6.1 Introduction 210
6.1.1 Types of Sustainable Energy 211
6.2 Optimization in Sustainable Energy 212
6.2.1 Role of Optimization in Sustainable Energy 213
6.2.2 Bibliometric Analysis 214
6.2.3 Research Gaps and Research Questions 216
6.3 Materials and Methods 217
6.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis 219
6.4.1 Performance Analysis 219
6.4.1.1 Overall Review of the Database 219
6.4.1.2 Annual Publication Increase 220
6.4.1.3 Average Annual Citations 220
6.4.1.4 Sankey Diagram 221
6.4.1.5 Most Cited and Most Published Journals 221
6.4.1.6 The Affiliations that Matter Most 223
6.4.1.7 Frequently Cited Authors 223
6.4.1.8 The Most Productive Countries 224
6.4.1.9 Most Cited Document 227
6.4.2 Analysis of Science Mapping 227
6.4.2.1 Conceptual Structure Map 227
6.4.2.2 Thematic Map 230
6.4.2.3 Trend Topics 230
6.4.2.4 Word Cloud 232
6.4.2.5 Keyword Co-Occurrence Analysis 232
6.5 Discussions 233
6.6 Conclusions 235
References 236
7 A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions 241J. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan
7.1 Introduction 242
7.2 Solar PV 242
7.2.1 Cooling Technologies 245
7.3 Hybrid PV Panel 247
7.4 Optimization 248
7.5 Conventional Optimization Approaches 249
7.5.1 Genetic Algorithm (GA) 249
7.5.2 Particle Swarm Optimization (PSO) 250
7.5.3 Firefly Optimization (FF) 252
7.5.4 Cuckoo Search (CS) Optimization 252
7.5.5 Bat Optimization Algorithm 253
7.5.6 Jelly Fish Optimization 255
7.5.7 Other Meta-Heuristic Models 257
7.6 Proposed Optimization Algorithm 258
7.7 Conclusion 260
References 261
8 Multi-Objective Optimization in Sustainable Energy 267Sevtap Tirink
8.1 Introduction 268
8.2 Sustainable Development and Energy Sustainability 269
8.3 Sustainable Energy System Models 271
8.4 Foundations of Multi-Objective Optimization 276
8.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy 281
8.6 Conclusions 282
References 283
9 Data Analytics for Performance Optimization in Renewable Energy 291Aparna Unni and Harpreet Kaur Channi
9.1 Introduction 292
9.2 Literature Review 294
9.2.1 Scope and Objectives 295
9.3 Renewable Energy Technologies 296
9.3.1 Challenges in Renewable Energy Performance 297
9.3.2 Role of Data Analytics in Renewable Energy 297
9.3.3 Machine Learning Techniques 298
9.4 Statistical Modeling 300
9.4.1 Predictive Analytics 301
9.5 Methodology 302
9.6 Challenges and Opportunities 305
9.7 Application Areas of Data Analytics in Renewable Energy 309
9.8 Real-Time Implementation Using PVsyst 314
9.9 Top World-Level Case Studies 316
9.9.1 Wind Farm Optimization in Denmark 316
9.9.2 Solar Energy Grid Management in Germany 317
9.9.3 Hydroelectric Power Plant Efficiency in Canada 318
9.9.4 Energy Storage Optimization in California 318
9.9.5 Smart Grid Implementation in South Korea 319
9.9.6 Future Directions 321
9.10 Conclusion 323
References 324
10 Integration of Smart Grids in Energy Optimization 329Harpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand
10.1 Introduction 330
10.1.1 Literature Survey 331
10.1.2 Scope and Significance of the Study 332
10.2 Smart Grid Fundamentals 333
10.2.1 Renewable Energy Integration 334
10.3 Demand-Side Management 337
10.3.1 Demand-Side Management Techniques 339
10.4 Data Analytics in Smart Grid 341
10.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid 343
10.4.2 Energy Storage Systems in Smart Grid 345
10.5 Smart Grid Deployment Worldwide 346
10.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids 347
10.6 Conclusion 352
References 353
11 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications 357Manas Taneja and Dheeraj Joshi
11.1 Introduction 357
11.2 Markov's Modeling 359
11.3 Thermal Model 361
11.4 Transition Rate Evaluation 362
11.5 Genetic Algorithm 364
11.6 Reliability Calculations 365
11.7 Conclusion 369
References 369
12 Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach 373Yasin Atci and Sibel Atan
12.1 Introduction 373
12.2 Literature Review 375
12.3 Wind Energy 376
12.3.1 Wind Energy Potential 377
12.3.2 Wind Theorems 379
12.3.2.1 Betz Theorem 379
12.3.2.2 Weibull Distribution 380
12.3.3 Stochastic Structure of Wind Power 381
12.4 Markov Processes 383
12.4.1 Stochastic Processes 383
12.4.1.1 Index Set 384
12.4.1.2 State Spaces 384
12.4.2 Markov Processes 384
12.4.3 Markov Chains 385
12.4.3.1 Markov Transition Probabilities Matrix 385
12.4.3.2 Equilibrium Distributions 386
12.4.3.3 Multi-Step Transition Probabilities 387
12.4.3.4 Limit Behavior of Markov Chains 387
12.5 Wind Energy Forecasting with Markov Chains 388
12.5.1 Purpose and Content of the Study 389
12.5.2 Data Set and Data Properties 389
12.5.2.1 Characteristics of Wind Turbines in Hatay Province 391
12.5.3 Constructing the Markov Transition Matrix 392
12.5.4 Cumulative Transition Matrix 395
12.5.5 Generation of Synthetic Data 396
12.6 Conclusions and Recommendations 399
References 402
13 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems 405Swati Sharma and Ikbal Ali
13.1 Introduction 406
13.2 Renewable Energy Approaches: An Introductory Overview 407
13.2.1 Renewable Energy Technologies: Types, Applications, and Advancements 410
13.2.1.1 Solar Energy and Wind Energy 412
13.2.1.2 Hydro and Ocean Power 417
13.2.1.3 Geothermal and Bioenergy 418
13.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems 420
13.3.1 Common Replicas of Unconstrained Optimization Problems 421
13.3.2 Convex Optimization 422
13.3.2.1 Duality 423
13.3.2.2 Simplex Method 425
13.3.3 Optimization Strategies for Unconstrained Problems 427
13.3.3.1 Nelder-Mead Method 428
13.3.3.2 Golden Section Search Method (GSS) 429
13.3.3.3 Fibonacci Search 430
13.3.3.4 Hookes' and Jeeves' Method 430
13.3.3.5 Gradient Descent Method 432
13.3.3.6 Coordinate Descent Method 432
13.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods 433
13.4.1 Particle Swarm Optimization 433
13.4.2 Genetic Algorithm 435
13.4.3 Simulated Annealing 439
13.4.4 Ant Colony Optimization 441
13.4.5 Firefly Optimization 442
13.4.6 Artificial Bee Colony Optimization 444
13.4.7 Gray Wolf Optimization 446
13.4.8 Red Fox Optimization 448
13.4.9 Jaya Algorithm 450
13.4.10 Teaching-Learning-Based Optimization (TLBO) 451
13.4.11 Artificial Immune System 452
13.4.12 Game Theory 453
13.4.13 Mixed Integer Linear Programming 454
13.5 Conclusions and Discussion 455
References 456
14 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques 465Anshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla
14.1 Introduction 466
14.2 Challenges in Energy Optimization 468
14.3 Energy Optimization Methods 470
14.4 Role of Machine Learning Methods 473
14.5 Machine Learning Models 475
14.6 Conclusions 478
References 479
Index 487
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