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Optimization in Sustainable Energy

Methods and Applications

Edited Prasenjit Chatterjee, Anita Khosla, Ashwani Kumar and Gülay Demir
Series: Sustainable Computing and Optimization
Copyright: 2025   |   Expected Pub Date:2025/04/30
ISBN: 9781394242108  |  Hardcover  |  
512 pages

One Line Description
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.

Audience
Research scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.

Description
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 decisionmaking 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.

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Author / Editor Details
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.

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Table of Contents
Preface
Acknowledgment
Part I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy
1. Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective
Gülay Demir and Prasenjit Chatterjee
1.1 Introduction
1.1.1 Research Goals
1.1.1.1 Research Questions
1.1.1.2 Contributions and Novelty
1.1.1.3 Organization of the Chapter
1.2 Literature Review
1.2.1 MCDM Research on Renewable Energy
1.2.2 Studies Used WENSLO and RAWEC Methods
1.2.3 Research Gaps
1.3 Methodology for Research
1.3.1 WENSLO Method for Criteria Prioritization
1.3.2 RAWEC Method to Rank Alternatives
1.3.2.1 Case Study
1.4 Results
1.4.1 Application of WENSLO Method
1.4.2 Application of the RAWEC Method
1.4.3 Sensitivity Analysis
1.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights
1.4.3.2 Comparison With Other MCDM Methods
1.5 Discussion, Practical and Managerial Implications
1.6 Conclusions, Limitations, and Future Directions
References
2. A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources
Sanjib Biswas, Gülay Demir and Prasenjit Chatterjee
2.1 Introduction
2.2 Literature Review
2.2.1 Research Gaps
2.2.2 Research Objectives
2.3 Preliminary Concepts: p, q-QOFS
2.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation
2.5 Materials and Methods
2.5.1 Theoretical Framework: Selection of Criteria
2.5.2 Expert Group
2.5.3 Methodological Framework
2.5.3.1 Stages in the Methodological Framework
2.5.3.2 Procedural Steps
2.6 Findings
2.7 Discussions
2.8 Conclusion and Future Scope
References
Appendix A
3. Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach
Gülay Demir and Prasenjit Chatterjee
3.1 Introduction
3.1.1 Purpose and Importance of the Study
3.1.2 Research Questions
3.1.3 Contributions
3.1.4 Research Gaps
3.2 Literature Review
3.2.1 Carbon Footprint Assessment and MCDM Methods
3.2.2 Studies with WENSLO and RAWEC Methods
3.3 Research Methodology
3.3.1 Fundamentals of FST
3.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies
3.3.3 F-RAWEC Method for Ranking Strategies
3.4 Case Study
3.4.1 Identification and Explanation of Criteria
3.4.2 Carbon Footprint Reduction Strategies
3.4.3 Data Collection and Analysis
3.4.4 Determining Subjective Weights Using F-WENSLO Method
3.4.5 Results of F-RAWEC Application
3.5 Insights, Applications, and Managerial Implications
3.5.1 Analysis of Rankings
3.5.2 Application Implications
3.5.3 Managerial Implications
3.6 Conclusions, Limitations, and Future Directions
References
4. Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment
Ömer Faruk Görçün, Hande Küçükönder and Ahmet Çalık
4.1 Introduction
4.2 The Research Background
4.2.1 Common Findings in the Literature
4.2.2 Trends in the Literature
4.2.3 Current State of the Literature
4.2.4 Research and Theoretical Gaps
4.2.5 Motivations and Objectives of the Study
4.3 The Suggested Model
4.3.1 Preliminaries on Neutrosophic Sets
4.3.2 Identifying the Experts’ Reputation
4.3.3 Identifying the Criteria Weights
4.3.3.1 Determining the Subjective Weights of the Criteria
4.3.3.2 Identifying the Objective Weights of the Criteria
4.3.3.3 Associating the Subjective and Objective Weights
4.3.4 Ranking the Alternatives
4.4 Implementing the Model to Identify the Best Sustainable Energy Strategy
4.4.1 The Preparation Process
4.4.1.1 Description of the Problem
4.4.1.2 Forming the Board of Experts
4.4.1.3 Identifying the Criteria and Alternatives
4.4.2 Determining the Weights of the Criteria
4.4.3 Ranking the Alternatives
4.5 Results and Discussions
4.5.1 Rank and Influence of the Criteria
4.5.2 Sustainable Energy Strategies and Their Ranking
4.5.3 Importance, Influence, and Impacts of Results
4.5.4 Novelties, Managerial, and Policy Implications
4.5.5 Theoretical Contributions of the Decision-Making Model
4.6 Conclusions and Future Research Direction
References
5. ENTROPY-Based Evaluation of Global Renewable Energy Trends
Rahim Arslan
5.1 Introduction
5.2 Renewable Energy Concepts
5.3 World Countries and Türkiye in Clean Energy
5.4 Evaluation of Renewable Energy Resources Using MCDM Methods
5.5 ENTROPY Method
5.6 Case Study
5.6.1 Renewable Energy Weights According to Installed Capacity
5.7 Conclusions
References
Part II: Optimization Techniques in Sustainable Energy
6. Optimization in Sustainable Energy: A Bibliometric Analysis
Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan
6.1 Introduction
6.1.1 Types of Sustainable Energy
6.2 Optimization in Sustainable Energy
6.2.1 Role of Optimization in Sustainable Energy
6.2.2 Bibliometric Analysis
6.2.3 Research Gaps and Research Questions
6.3 Materials and Methods
6.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis
6.4.1 Performance Analysis
6.4.1.1 Overall Review of the Database
6.4.1.2 Annual Publication Increase
6.4.1.3 Average Annual Citations
6.4.1.4 Sankey Diagram
6.4.1.5 Most Cited and Most Published Journals
6.4.1.6 The Affiliations that Matter Most
6.4.1.7 Frequently Cited Authors
6.4.1.8 The Most Productive Countries
6.4.1.9 Most Cited Document
6.4.2 Analysis of Science Mapping
6.4.2.1 Conceptual Structure Map
6.4.2.2 Thematic Map
6.4.2.3 Trend Topics
6.4.2.4 Word Cloud
6.4.2.5 Keyword Co-Occurrence Analysis
6.5 Discussions
6.6 Conclusions
References
7. A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions
J. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan
7.1 Introduction
7.2 Solar PV
7.2.1 Cooling Technologies
7.3 Hybrid PV Panel
7.4 Optimization
7.5 Conventional Optimization Approaches
7.5.1 Genetic Algorithm (GA)
7.5.2 Particle Swarm Optimization (PSO)
7.5.3 Firefly Optimization (FF)
7.5.4 Cuckoo Search (CS) Optimization
7.5.5 Bat Optimization Algorithm
7.5.6 Jelly Fish Optimization
7.5.7 Other Meta-Heuristic Models
7.6 Proposed Optimization Algorithm
7.7 Conclusion
References
8. Multi-Objective Optimization in Sustainable Energy
Sevtap Tırınk
8.1 Introduction
8.2 Sustainable Development and Energy Sustainability
8.3 Sustainable Energy System Models
8.4 Foundations of Multi-Objective Optimization
8.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy
8.6 Conclusions
References
9. Data Analytics for Performance Optimization in Renewable Energy
Aparna Unni and Harpreet Kaur Channi
9.1 Introduction
9.2 Literature Review
9.2.1 Scope and Objectives
9.3 Renewable Energy Technologies
9.3.1 Challenges in Renewable Energy Performance
9.3.2 Role of Data Analytics in Renewable Energy
9.3.3 Machine Learning Techniques
9.4 Statistical Modeling
9.4.1 Predictive Analytics
9.5 Methodology
9.6 Challenges and Opportunities
9.7 Application Areas of Data Analytics in Renewable Energy
9.8 Real-Time Implementation Using PVsyst
9.9 Top World-Level Case Studies
9.9.1 Wind Farm Optimization in Denmark
9.9.2 Solar Energy Grid Management in Germany
9.9.3 Hydroelectric Power Plant Efficiency in Canada
9.9.4 Energy Storage Optimization in California
9.9.5 Smart Grid Implementation in South Korea
9.9.6 Future Directions
9.10 Conclusion
References
10. Integration of Smart Grids in Energy Optimization
Harpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand
10.1 Introduction
10.1.1 Literature Survey
10.1.2 Scope and Significance of the Study
10.2 Smart Grid Fundamentals
10.2.1 Renewable Energy Integration
10.3 Demand-Side Management
10.3.1 Demand-Side Management Techniques
10.4 Data Analytics in Smart Grid
10.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid
10.4.2 Energy Storage Systems in Smart Grid
10.5 Smart Grid Deployment Worldwide
10.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids
10.6 Conclusion
References
11. Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications
Manas Taneja and Dheeraj Joshi
11.1 Introduction
11.2 Markov’s Modeling
11.3 Thermal Model
11.4 Transition Rate Evaluation
11.5 Genetic Algorithm
11.6 Reliability Calculations
11.7 Conclusion
References
12. Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach
Yasin Atci and Sibel Atan
12.1 Introduction
12.2 Literature Review
12.3 Wind Energy
12.3.1 Wind Energy Potential
12.3.2 Wind Theorems
12.3.2.1 Betz Theorem
12.3.2.2 Weibull Distribution
12.3.3 Stochastic Structure of Wind Power
12.4 Markov Processes
12.4.1 Stochastic Processes
12.4.1.1 Index Set
12.4.1.2 State Spaces
12.4.2 Markov Processes
12.4.3 Markov Chains
12.4.3.1 Markov Transition Probabilities Matrix
12.4.3.2 Equilibrium Distributions
12.4.3.3 Multi-Step Transition Probabilities
12.4.3.4 Limit Behavior of Markov Chains
12.5 Wind Energy Forecasting with Markov Chains
12.5.1 Purpose and Content of the Study
12.5.2 Data Set and Data Properties
12.5.2.1 Characteristics of Wind Turbines in Hatay Province
12.5.3 Constructing the Markov Transition Matrix
12.5.4 Cumulative Transition Matrix
12.5.5 Generation of Synthetic Data
12.6 Conclusions and Recommendations
References
13. Efficient Optimization Techniques for Renewable and Sustainable Energy Systems
Swati Sharma and Ikbal Ali
13.1 Introduction
13.2 Renewable Energy Approaches: An Introductory Overview
13.2.1 Renewable Energy Technologies: Types, Applications, and Advancements
13.2.1.1 Solar Energy and Wind Energy
13.2.1.2 Hydro and Ocean Power
13.2.1.3 Geothermal and Bioenergy
13.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems
13.3.1 Common Replicas of Unconstrained Optimization Problems
13.3.2 Convex Optimization
13.3.2.1 Duality
13.3.2.2 Simplex Method
13.3.3 Optimization Strategies for Unconstrained Problems
13.3.3.1 Nelder–Mead Method
13.3.3.2 Golden Section Search Method (GSS)
13.3.3.3 Fibonacci Search
13.3.3.4 Hookes’ and Jeeves’ Method
13.3.3.5 Gradient Descent Method
13.3.3.6 Coordinate Descent Method
13.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods
13.4.1 Particle Swarm Optimization
13.4.2 Genetic Algorithm
13.4.3 Simulated Annealing
13.4.4 Ant Colony Optimization
13.4.5 Firefly Optimization
13.4.6 Artificial Bee Colony Optimization
13.4.7 Gray Wolf Optimization
13.4.8 Red Fox Optimization
13.4.9 Jaya Algorithm
13.4.10 Teaching–Learning-Based Optimization (TLBO)
13.4.11 Artificial Immune System
13.4.12 Game Theory
13.4.13 Mixed Integer Linear Programming
13.5 Conclusions and Discussion
References
14. Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques
Anshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla
14.1 Introduction
14.2 Challenges in Energy Optimization
14.3 Energy Optimization Methods
14.4 Role of Machine Learning Methods
14.5 Machine Learning Models
14.6 Conclusions
References
Index

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