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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Sustainable Computing and Optimization
- Sprache: Englisch
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.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
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
1.1 Introduction
1.2 Literature Review
1.3 Methodology for Research
1.4 Results
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
2.1 Introduction
2.2 Literature Review
2.3 Preliminary Concepts: p, q-QOFS
2.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation
2.5 Materials and Methods
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
3.1 Introduction
3.2 Literature Review
3.3 Research Methodology
3.4 Case Study
3.5 Insights, Applications, and 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
4.1 Introduction
4.2 The Research Background
4.3 The Suggested Model
4.4 Implementing the Model to Identify the Best Sustainable Energy Strategy
4.5 Results and Discussions
4.6 Conclusions and Future Research Direction
References
5 ENTROPY-Based Evaluation of Global Renewable Energy Trends
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
5.7 Conclusions
References
Part II: OPTIMIZATION TECHNIQUES IN SUSTAINABLE ENERGY
6 Optimization in Sustainable Energy: A Bibliometric Analysis
6.1 Introduction
6.2 Optimization in Sustainable Energy
6.3 Materials and Methods
6.4 The Optimization Results in Sustainable Energy by Bibliometric 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
7.1 Introduction
7.2 Solar PV
7.3 Hybrid PV Panel
7.4 Optimization
7.5 Conventional Optimization Approaches
7.6 Proposed Optimization Algorithm
7.7 Conclusion
References
8 Multi-Objective Optimization in Sustainable Energy
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
9.1 Introduction
9.2 Literature Review
9.3 Renewable Energy Technologies
9.4 Statistical Modeling
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
9.10 Conclusion
References
10 Integration of Smart Grids in Energy Optimization
10.1 Introduction
10.2 Smart Grid Fundamentals
10.3 Demand-Side Management
10.4 Data Analytics in Smart Grid
10.5 Smart Grid Deployment Worldwide
10.6 Conclusion
References
11 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications
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
12.1 Introduction
12.2 Literature Review
12.3 Wind Energy
12.4 Markov Processes
12.5 Wind Energy Forecasting with Markov Chains
12.6 Conclusions and Recommendations
References
13 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems
13.1 Introduction
13.2 Renewable Energy Approaches: An Introductory Overview
13.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems
13.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods
13.5 Conclusions and Discussion
References
14 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques
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
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Alternatives and criteria for the study.
Table 1.2 Initial decision-making matrix.
Table 1.3 Normalized decision matrix.
Table 1.4 Size of the j-th criterion class interval.
Table 1.5 Criteria slope values.
Table 1.6 Criteria envelope values.
Table 1.7 Envelope–slope ratios.
Table 1.8 Final criteria weight values.
Table 1.9 Benefit normalization values.
Table 1.10 Cost normalization values.
Table 1.11 Final ranking order calculated using RAWEC method.
Chapter 2
Table 2.1 List of RE sources under comparison.
Table 2.2 List of criteria used for comparing RESs.
Table 2.3 Profile of the experts who participated in the assessment of RE sour...
Table 2.4 Linguistic rating scale and p, q-QOFNs.
Table 2.5 Crisp initial decision matrix for comparison.
Table 2.6 Standardized decision matrix (
α
= 1;
β
= 9.).
Table 2.7 Calculated criteria weights (PSI-NS method).
Table 2.8 Weighted standardized decision matrix (
τ
= 1).
Table 2.9 Calculation of appraisal scores and ranking of RE sources.
Table 2.10 Experimental cases for sensitivity analysis.
Table A1 Rating of RESs.
Table A2 Aggregated (using p, q-QOFWFA) response—p, q-QOFN-based decision matr...
Chapter 3
Table 3.1 Description of fuzzy linguistic descriptors.
Table 3.2 Structure of the decision-making group.
Table 3.3 Definition and importance of criteria.
Table 3.4 Carbon footprint reduction strategies.
Table 3.5 Expert evaluation of criteria.
Table 3.6 Expert evaluation of carbon footprint reduction strategies.
Table 3.7 Calculations according to F-WENSLO method for criteria.
Table 3.8 Ranking of carbon footprint reduction strategies.
Chapter 4
Table 4.1 The preceding studies in the relevant literature.
Table 4.2 A T2NFS-based scale for assessing the experience and expertise level...
Table 4.3 T2NFN rating scale for the criteria [43].
Table 4.4 T2NFN linguistic assessment scale for alternatives [47].
Table 4.5 Detailed information about the members of the board of experts.
Table 4.6 Strategies for transformation of the sustainable energy systems.
Table 4.7 The assessment criteria to evaluate the sustainable energy strategie...
Table 4.8 Linguistic assessments of experts’ reputations using T2NFN.
Table 4.9 Weights of the experts.
Table 4.10 Linguistic appraisal of the criteria.
Table 4.11 Subjective weights of the criteria.
Table 4.12 Aggregated T2NFN decision matrix for the alternatives.
Table 4.13 Score value matrix.
Table 4.14 The normalized matrix.
Table 4.15 The final weight coefficients of the criteria.
Table 4.16 Initial decision matrix with ideal and anti-ideal values.
Table 4.17 Standardized decision matrix.
Table 4.18 Normalized decision matrix.
Table 4.19 The weighted decision matrix and final significance values of each ...
Appendix Table A1.
Chapter 5
Table 5.1 Renewable energy installed power development (MW) (TEİAS, 2024).
Tablo 5.2 Renewable energy installed power percentages (MW).
Table 5.3 Initial matrix.
Table 5.4 Normalized decision matrix.
Table 5.5 Calculation of
E
j
values.
Table 5.6 Difference values with significance levels.
Table 5.7 Renewable energy installed power of world countries (MW).
Table 5.8 Renewable energy installed power percentages of world countries (MW)...
Table 5.9 ENTROPY decision matrix.
Table 5.10 Normalized matrix.
Table 5.11 Calculation of
E
j
values.
Table 5.12 Difference values with significance levels.
Chapter 6
Table 6.1 Information filtration in the WoS database.
Table 6.2 The most often cited sources for sustainable energy optimization.
Table 6.3 Relevant institutions that have supported studies on energy optimiza...
Table 6.4 The most prolific author citations and publications.
Table 6.5 Countries by number of publications.
Table 6.6 Cooperation nations and quantity of publications.
Table 6.7 Most effective documents.
Table 6.8 Yearly trending topics.
Chapter 8
Table 8.1 Common inputs, constraints, and objectives in renewable energy.
Chapter 11
Table 11.1 Failure rate of different components.
Table 11.2 Temperature factor (π
T
) for different components.
Table 11.3 Thermal calculations for the MIMO converter.
Table 11.4 Genetically optimized repair rates.
Chapter 12
Table 12.1 Statistical values of wind speed and energy generation values.
Table 12.2 Lower and upper limit values for the cases.
Table 12.3 Transition frequencies matrix.
Table 12.4 Transition probability matrix.
Table 12.5 Cumulative transition matrix.
Table 12.6 Statistical values of real and generated synthetic data.
Table 12.7 Transition probability matrix for synthetic data.
Chapter 13
Table 13.1 Top countries’ renewable power capacity (in 2024) [31].
Table 13.2 Renewable power installed capacity by Indian states (includes off-g...
List of Illustrations
Chapter 1
Figure 1.1 Flowchart of WENSLO–RAWEC multi-criteria model.
Figure 1.2 Weight modifications based on various scenarios.
Figure 1.3 Alternatives are ranked based on the new criteria weights.
Figure 1.4 Comparison between ranking performances of MCDM methods.
Chapter 2
Figure 2.1 Timeline for FSs.
Figure 2.2 Comparison of results of various MCDM methods (Series 1 to 6 denote...
Figure 2.3 Result of sensitivity analysis (ranking of RE sources under various...
Figure 2.4 Result of sensitivity analysis (appraisal scores of RESs under vari...
Chapter 3
Figure 3.1 Carbon footprint components.
Figure 3.2 Criteria weights.
Chapter 5
Figure 5.1 Renewable energy installed power percentages.
Chapter 6
Figure 6.1 Data collection process flow diagram using PRISMA.
Figure 6.2 Main information.
Figure 6.3 Year-wise distribution of sustainable energy optimization researche...
Figure 6.4 Experiments using sustainable energy optimization: annual average c...
Figure 6.5 Three-Field Plot
Figure 6.6 Country scientific production.
Figure 6.7 Country collaboration map.
Figure 6.8 Conceptual structure map.
Figure 6.9 Map with themes based on keywords.
Figure 6.10 TreeMap of keywords.
Figure 6.11 Word cloud of the most frequently used keywords in optimization in...
Figure 6.12 Co-occurrence keywords.
Chapter 7
Figure 7.1 Layout of heat absorber used in a solar cooling system.
Figure 7.2 Typical characteristics of a solar PV.
Figure 7.3 Types of cooling techniques.
Figure 7.4 Workflow model of GA.
Figure 7.5 Work flow model of the PSO algorithm.
Figure 7.6 Work flow model of FF optimization algorithm.
Figure 7.7 Work flow model of CS optimization algorithm.
Figure 7.8 Work flow model of bat optimization algorithm.
Figure 7.9 Jelly fish optimizer.
Figure 7.10 Workflow of the HHO algorithm.
Chapter 8
Figure 8.1 The elements of sustainable development.
Figure 8.2 Classification of energy system models.
Figure 8.3 Pareto optimization approach for a multi-objective evolutionary alg...
Chapter 9
Figure 9.1 Optimizing performance through advanced data analytics.
Figure 9.2 Process in energy forecasting.
Figure 9.3 Flowchart of analytic process.
Figure 9.4 Solar energy predictive model.
Figure 9.5 Solar energy data analysis techniques.
Figure 9.6 Wind energy.
Figure 9.7 Hydroelectric power.
Figure 9.8 Geothermal energy.
Figure 9.9 Integrated system.
Figure 9.10 Isoshading diagram.
Figure 9.11 Single line diagram.
Figure 9.12 (a) Normalized production, (b) performance ratio.
Chapter 10
Figure 10.1 Features of a smart grid.
Figure 10.2 Smart grid fundamentals.
Figure 10.3 Renewable energy integration into smart grid.
Figure 10.4 Benefits of renewable energy integration into smart grid.
Figure 10.5 Demand-side management techniques.
Figure 10.6 Artificial intelligence and machine learning applications in smart...
Figure 10.7 Energy storage systems in smart grid.
Figure 10.8 Worldwide share of households with smart meters.
Figure 10.9 Investment in digital infrastructure in transmission and distribut...
Figure 10.10 Investment spending on electricity grids, 2015–2022.
Figure 10.11 Average annual investment spending on electricity grids in the Ne...
Chapter 11
Figure 11.1 Bathtub curve.
Figure 11.2 Circuit diagram of a MIMO converter.
Figure 11.3 MIMO fed three-phase induction motor drive.
Figure 11.4 Motor phase connected to mid-point of MIMO’s output capacitor thro...
Figure 11.5 Markov chain diagram for the converter system.
Figure 11.6 Flow chart of Genetic Algorithm.
Figure 11.7 Effectiveness of Genetic Algorithm.
Figure 11.8 Reliability as a function of time.
Chapter 12
Figure 12.1 Distribution of installed wind power in the world by country.
Figure 12.2 Türkiye wind atlas.
Figure 12.3 Annual installed wind power capacity, Türkiye, MW.
Figure 12.4 Wind turbine power generation capacity factor, Cp [16].
Figure 12.5 Weibull distribution [17].
Figure 12.6 Frequency diagram of wind speed data in the Hatay region.
Figure 12.7 Vestas V90-3.0 MW wind turbine power curve [25].
Figure 12.8 Real data frequency diagram.
Figure 12.9 Synthetic data frequency diagram.
Chapter 13
Figure 13.1 Five major types of renewable energy sources [58].
Figure 13.2 (a) Convex set. (b) Concave set [65].
Figure 13.3 Convex function [66].
Figure 13.4 Overview of particle swarm optimization flowchart.
Figure 13.5 Overview of genetic algorithm flowchart.
Figure 13.6 Overview of simulated annealing flowchart.
Figure 13.7 Overview of ant colony optimization flowchart.
Figure 13.8 Overview of firefly optimization flowchart.
Figure 13.9 Overview of artificial bee colony optimization flowchart.
Figure 13.10 Overview of gray wolf optimization flowchart.
Figure 13.11 Overview of red fox optimization flowchart.
Figure 13.12 Overview of jaya algorithm.
Chapter 14
Figure 14.1 Training and testing for ML model for energy optimization.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgment
Begin Reading
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Sustainable Computing and Optimization
Editors: Prasenjit Chatterjee, Morteza Yazdani and Dilbagh Panchal
The objective of the series is to bring together global research scholars, experts, and scientists in the research areas of sustainable computing and optimization to share their knowledge and experiences on current research achievements in these fields. Since the series was launched in 2021, it has provided a golden opportunity for the research community to share their novel research results, findings, and innovations to a wide range of topics and applications. The series promotes sustainable computing and optimization methodologies to solve real-life problems mainly from engineering and management systems domains.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Optimization in Sustainable Energy
Methods and Applications
Edited by
Prasenjit Chatterjee
MCKV Institute of Engineering, Howrah, West Bengal, India
Anita Khosla
Dept. of Electrical and Electronics Engineering, Manav Rachna International Institute of Research and Studies, Haryana, India
Ashwani Kumar
College of Engineering and Technology, Bathinda, Punjab, India
and
Gülay Demir
Sivas Cumhuriyet University, Sivas, Turkey
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-24210-8
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
The Editors would like to dedicate this book to their parents, life partners, children, students, scholars, friends and colleagues.
Preface
Sustainable energy plays a crucial role in addressing global environmental challenges, driving efforts to reduce carbon emissions and create more efficient energy systems. The growing complexity of these systems necessitates advanced optimization techniques to ensure they operate efficiently and effectively. This book offers an in-depth exploration of the optimization approaches and strategic planning tools that support sustainable energy systems.
This book compiles valuable knowledge, methods, and practical examples to assist scholars, researchers, professionals, and policymakers in tackling the growing challenges of optimizing sustainable energy. It covers a variety of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, offering both theoretical insights and practical guidance.
The book is divided into two parts. Part I, “Multi-Criteria Optimization and Strategic Planning in Sustainable Energy,” examines the challenges and opportunities related to integrating multi-criteria decision-making techniques in energy planning. This part highlights key approaches that balance environmental, economic, and social factors, offering insights into how decision-makers can strategically allocate resources while promoting sustainability.
Part II, “Optimization Techniques in Sustainable Energy,” focuses on advanced algorithms and methods designed to improve energy system performance. This part highlights the application of cutting-edge techniques, such as evolutionary algorithms, machine learning, and metaheuristics, to optimize processes like energy storage, distribution, and consumption. The chapters offer actionable solutions to real-world energy challenges, ensuring that optimization efforts deliver tangible benefits for sustainability.
Chapter 1 explores the impact of renewable energy sources on Turkey’s energy future using the Weights by ENvelope and SLOpe (WENSLO) and Ranking of Alternatives with Weights of Criterion (RAWEC) methods. WENSLO is applied to weight criteria such as installed capacity, number of facilities, installation cost, levelized cost of electricity, share in electricity generation, and power plant lifetimes. RAWEC ranks renewable energy sources, including hydro, geothermal, wind, solar, and biomass. The chapter also offers a strategic roadmap for Turkey’s sustainable energy transformation, emphasizing the importance of economic and sustainability criteria. Biomass and geothermal energy rank high due to their economic and sustainable benefits, while solar and wind are gaining importance. This research provides valuable guidance for policymakers in developing effective strategies and investment decisions for a sustainable energy future in Turkey.
Chapter 2 proposes a multi-criteria decision-making (MCDM) framework using an extended Technology-Organization-Environment (ToE) model to compare renewable energy sources (RESs). By introducing novel methodologies such as p, q Quasirung Orthopair fuzzy sets and modified preference selection index (PSI) methods, the study identifies solar cells and waste-to-energy as the top renewable energy sources, emphasizing the significance of criteria like environmental impact and technological expertise. Chapter 3 explores strategies for reducing carbon footprints using fuzzy MCDM methods, specifically F-WENSLO and F-RAWEC, to assess and rank the feasibility of energy efficiency and renewable energy sources. The study highlights the value of fuzzy MCDM in evaluating complex and uncertain scenarios, offering insights for policymakers in developing effective energy management and sustainability strategies.
Chapter 4 focuses on developing sustainable energy strategies by addressing the complex multi-criteria decision-making processes that shape energy policy. It introduces a Type-2 Neutrosophic Fuzzy Set-based model, combined with Logarithmic Percentage Change based on Objective Weights (LOPCOW) and Ranking of Alternatives through Functional Mapping of Criterion Sub-Intervals into Single Intervals (RAFSI) methods, to rank energy strategies across ecological, economic, technological, and societal dimensions. Chapter 5 evaluates global renewable energy trends by analyzing usage rates and year-over-year changes in leading countries. Using the Entropy method, it quantifies the significance of these changes, offering insights into how major renewable energy producers prioritize different energy types over time.
Chapter 6 presents a bibliometric analysis of optimization in sustainable energy, addressing global concerns such as energy demand, global warming, and rising oil prices. Using tools like R, Biblioshiny, and RStudio, the study analyzes 933 articles from the Web of Science (2010-2024). Chapter 7 explores advancements in cooling methods for solar photovoltaic (PV) systems to improve electrical efficiency by reducing panel temperatures. By investigating various cooling strategies and optimizing parameters like thermal performance and heat-transfer layouts, the research aims to develop a new optimization methodology for solar system cooling, while also evaluating existing meta-heuristic models for temperature management.
Chapter 8 explores multi-objective optimization (MOO) techniques used to balance competing objectives in sustainable energy systems, focusing on strategies that enhance system reliability and environmental sustainability while reducing energy costs. Chapter 9 examines how data analytics is transforming the optimization of renewable energy systems, addressing challenges like intermittency and grid integration through techniques such as machine learning and predictive analytics. By analyzing real-world case studies, the research highlights the potential of data-driven approaches to improve system reliability, energy forecasting, and sustainability.
Chapter 10 explores the integration of smart grids into existing power systems, emphasizing their role in enhancing energy efficiency, incorporating renewable energy, and enabling real-time management. By leveraging advanced technology and data, smart grids promote sustainable energy practices and improve grid reliability. Chapter 11 calculates the reliability of a multi-port power train for electric vehicles by constructing a Markov chain model and determining failure rates using thermal models and genetic algorithms. Key metrics such as Mean Time to Failure (MTTF) and system availability are also evaluated. Chapter 12 introduces a wind energy forecasting model using Markov processes, based on hourly wind speed and energy production data from the Belen region of Hatay, Türkiye. The model improves the accuracy of wind power generation predictions by employing transition probability matrices and synthetic data to maximize energy output and income.
Chapter 13 provides a comprehensive overview of computational optimization techniques used in sustainable and renewable energy, addressing challenges related to the intermittency of renewable sources. It highlights recent advancements in software and hardware that enable more effective optimization and planning for renewable energy systems. Chapter 14 tackles the key challenges of energy optimization, focusing on the integration of renewable energy sources, real-time energy demand management, and loss minimization. It emphasizes the transformative role of machine learning in addressing these issues, showcasing applications in predictive maintenance, load forecasting, and energy distribution, while also discussing technical considerations and emerging trends like IoT and edge computing.
The editors are grateful to everyone who has supported their work and research, and also wish to thank Martin Scrivener and Scrivener Publishing for their support and publication.
Prof. Dr. Prasenjit ChatterjeeProf. Dr. Anita KhoslaProf. Dr. Ashwani Kumar
Assoc. Prof. Dr. Gülay Demir
Acknowledgment
The editors wish to express their warm thanks and deep appreciation to those who provided valuable inputs, support, constructive suggestions, and assistance in editing and proofreading this book.
The editors would like to thank all the authors for their valuable contributions in enriching the scholarly content of the book.
Mere words cannot express the editors’ deep gratitude to the entire editorial and production teams of Scrivener Publishing, particularly Martin Scrivener and Linda Mohr, for their great support, encouragement, and guidance all through the publication process. This book would not have been possible without their significant contributions.
The editors would like to sincerely thank the reviewers who kindly volunteered their time and expertise for shaping such a high-quality book on a very timely topic.
The editors wish to acknowledge the love, understanding, and support of their family members during the book’s preparation.
Finally, the editors use this opportunity to thank all the readers and expect that this book will continue to inspire and guide them for their future endeavor.
The Editors
Part IMULTI-CRITERIA OPTIMIZATION AND STRATEGIC PLANNING IN SUSTAINABLE ENERGY
1Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective
Gülay Demir1 and Prasenjit Chatterjee2*
1Vocational School of Health Services, Sivas Cumhuriyet University, Sivas, Türkiye
2Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India
Abstract
This chapter explores the potential impact of renewable energy sources (RESs) on Turkey’s energy future by employing two analytical methods, namely, Weights by ENvelope and SLOpe (WENSLO) and Ranking of Alternatives with Weights of Criterion (RAWEC). The WENSLO method is used to weight criteria such as installed capacity, number of plants/facilities, installation cost, levelized cost of electricity, share in electricity generation, and lifetime of power plants. In contrast, the RAWEC technique was recommended for selecting RESs such as hydro, geothermal, wind, solar, and biomass. In this framework, the effectiveness and applicability of WENSLO and RAWEC methods have been analyzed to identify RESs suitable for meeting Turkey’s energy needs. This study aims to provide a strategic roadmap for Turkey’s sustainable energy transformation. Economic and sustainability criteria, such as the levelized cost of electricity and the lifetime of power plants, should be prioritized in determining the preferred energy sources. In this context, biomass and geothermal energy sources are advantageous in terms of both economic and sustainability and rank high in the ranking of preference, while technologically developing sources, such as solar and wind energy, are gaining more and more importance. This study provides guidance to decision makers in the energy sector to develop effective policies and make the right investment decisions, thus contributing to a more sustainable energy future.
Keywords: Renewable energy sources, MCDM, WENSLO, RAWEC, Performance analysis
1.1 Introduction
Increasing environmental concerns, rapidly depleting reserves, and high energy prices have popularized renewable energy options. Because renewable energy sources (RESs) are abundant in nature, cost less than traditional sources, and cause less environmental harm, many nations have implemented laws to promote renewable energy development and have activated several renewable energy applications [1–3]. RESs are continuously replenished by natural processes, and their environmental impacts are generally less compared to fossil fuels. They also have the potential to improve energy security and lower greenhouse gas emissions [4]. There are many different sources of renewable energy. Among these, the most popular ones used for energy production are the following [5]:
Solar energy: Energy obtained by direct or indirect conversion of sunlight into electrical energy or heat. It is captured and utilized by technologies such as solar panels or thermal collectors.
Wind energy: Wind energy is transformed into mechanical or electrical power by wind turbines. Wind turbines produce electricity by harnessing the kinetic energy of the wind.
Hydroelectric energy: It is the energy obtained by converting water flow or water pressure into mechanical energy. It is obtained using structures such as dams or river turbines.
Geothermal energy: It is the energy obtained by extracting and using hot water and steam from geothermal sources. It is used with systems such as heat pumps or geothermal power plants.
Biomass energy: It is the energy obtained through the combustion of organic materials (plants, trees, waste, etc.) or biochemical processes. Biomass energy can be used with different technologies such as biogas, biodiesel, and biomass boilers.
For a rapidly developing country like Turkey, whose energy needs are constantly increasing, RESs are the cornerstone of a sustainable and environmentally friendly energy policy. However, deciding which RE source should be preferred is a complex process. Because the advantages, disadvantages and impacts of each source are different. This study will address the application of the multi-criteria decision making (MCDM) technique to identify the best RES in Turkey’s energy industry [6, 7]. The reasons for using MCDM techniques in the RE decision-making process can be summarized as follows:
Complex decision process: The selection of acceptable RESs to satisfy Turkey’s energy demands is a complicated decision-making process that takes into account a variety of parameters. In this process, the advantages and disadvantages of different energy sources should be considered.
Evaluation of various criteria: Several variables should be considered while selecting energy sources, including economic sustainability, environmental implications, technical maturity, installation cost, operating cost, and energy security.
Determination of the optimum energy mix: Determining the most suitable RESs to meet Turkey’s energy needs is related to determining the optimum energy mix. This is important for diversifying energy supply, increasing energy security, and reducing environmental impacts.
1.1.1 Research Goals
The goal of this study is to offer a framework for deciding which RESs are best suitable for meeting Turkey’s energy needs. To this end, we aim to shed light on the strategic decision-making process in Turkey’s energy transition through the use of MCDM methods, Weight by Envelope and Slope (WENSLO) and Ranking of Alternatives with Weights of Criterion (RAWEC). The focus of the study is to evaluate different RESs and determine the most suitable combination for Turkey’s energy mix. In this direction, it is aimed to make strategic decisions to guide energy policies by taking into account factors such as installed capacity, number of power plants/facilities, installation cost, levelized electricity cost, share in electricity generation, and lifetime of power plants. This study aims to contribute to Turkey’s sustainable energy transformation and increase energy security.
1.1.1.1 Research Questions
Aiming to fill the gap in the existing literature, this study aims to investigate the following research questions:
RQ1. Which RESs are best suited to meet Turkey’s energy needs?
RQ2. How can MCDM methods, such as WENSLO and RAWEC, be used in Turkey’s energy transition?
RQ3. How can RESs, such as solar, wind, hydroelectric, geothermal, and biomass, be integrated into Turkey’s energy mix?
These research questions will provide guidance to better understand the main objectives and focal points of the study.
1.1.1.2 Contributions and Novelty
The major contributions of this study include offering a new perspective on strategic decision making in Turkey’s energy transformation process. Specifically, it introduces a novel framework for the application of MCDM methods aimed at identifying RESs that are most suitable for meeting Turkey’s energy needs. This approach provides valuable insights and guidance for policymakers and stakeholders in the energy sector. The use of MCDM methods, such as WENSLO and RAWEC, provides a contribution to the existing literature in this field. This study also introduces an innovative approach by applying these methods to evaluate various RESs in the context of formulating and implementing Turkey’s energy policies so that stakeholders can make more accurate and well-informed decisions. In conclusion, this study aims to contribute to Turkey’s sustainable energy transformation and shed light on the effective formulation and implementation of energy policies.
1.1.1.3 Organization of the Chapter
The chapter is organized as follows: Section 1.2 provides a complete literature assessment and highlights research needs. Section 1.3 describes the MCDM framework utilized in the analysis. The fourth section introduces the data and displays the suggested methodology’s outcomes. Section 1.5 focuses on the practical and managerial ramifications. Finally, Section 1.6 examines the results, limits, and future directions.
1.2 Literature Review
There are two sub-sections in the literature review section. The first subsection summarizes renewable energy studies using MCDM methods to provide some background information. The second sub-section discusses the literature on studies using WENSLO and RAWEC methods.
1.2.1 MCDM Research on Renewable Energy
Although there is a large body of research that has utilized MCDM approaches in renewable energy, this chapter provides a quick overview of a few of these studies. Alizadeh et al. [8] used benefit, opportunity, cost, risk (BOCR) along with analytic network process (ANP) methods for Iran, Zhou et al. [9] used geographical information system (GIS), bestworst method (BWM) and Tomada de Decisão Interativa Multicritério (TODIM) methods for China, Salameh et al. [10] used entropy, criteria importance through intercriteria correlation (CRITIC) and technique for order of preference by similarity to ideal solution (TOPSIS) methods for Saudi Arabia, Troldborg et al. [11] used preference ranking organization method for enrichment evaluations (PROMETHEE) method and stated that solar energy is the most suitable RES for Scotland. Štreimikienė et al. [12] stated that nuclear power is the most suitable RES for Lithuania with analytic hierarchy process (AHP). Streimikiene et al. [13] used TOPSIS and multi-objective optimization by ratio analysis with full multiplicative form (MULTIMOORA) methods and concluded that the most suitable resources for the EU are water and solar. Li et al. [14], used TOPSIS, PROMETHEE, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), ELimination Et Choix Traduisant la REalité (ELECTRE), and weighted sum method (WSM) methods for China, Singh et al. [15] used step-wise weight assessment ratio analysis (SWARA), MOORA, and TOPSIS methods for India and said that hydropower is the most suitable RES. Yu et al. [6] stated that wind is the most suitable RES for China with SWARA II and MEREC methods.
The subject of RES on a Turkey scale has been investigated using several MCDM methodologies in the literature. Buyukozkan et al. [16] showed that wind and solar are the most suitable RESs with spherical fuzzy decision-making trial and evaluation laboratory (DEMATEL), ANP, and VIKOR methods. Dumrul et al. [17] showed that solar is the most suitable RES with intuitionistic–fuzzy evaluation based on distance from average solution (EDAS) method. Yontar [18] showed that solar is the most suitable RES with structural equation modeling (SEM)-Complex proportional assessment (COPRAS) methods.. Bilgili et al. [19], using intuitionistic fuzzy– TOPSIS method, and said that solar energy is the most suitable source. Karatop et al. [20] showed that the most suitable source is hydro and wind with fuzzy AHP, EDAS, fuzzy FMEA methods. Alkan & Albayrak [21] evaluated wind and hydro resources with fuzzy Entropy, fuzzy COPRAS, and fuzzy MULTIMOORA methods. Deveci et al. [22] concluded that wind and solar are the most suitable resources with interval-valued intuitionistic fuzzy combinative distance-based assessment (CODAS) methods. Karakaş & Yıldıran [23] stated that solar and wind is the most suitable RES with fuzzy AHP. Colak & Kaya [24] stated that wind and solar are the most suitable RESs with interval type-2 fuzzy AHP and hesitant fuzzy TOPSIS methods. Buyukozkan & Guleryuz [25] stated that wind and solar are the most suitable RESs with fuzzy AHP with linguistic interval fuzzy preference relations and TOPSIS.
1.2.2 Studies Used WENSLO and RAWEC Methods
There are limited number of publications with WENSLO and RAWEC methods, which are newly introduced to the literature. Pamucar et al. [26] suggested two multi-criteria decision support methods, WENSLO and Aczel–Alsina Weighted Assessment (ALWAS), to assess nations’ green growth performance. WENSLO method was designed to objectively determine the weight values of the criteria, whereas ALWAS method was developed to rank the available alternatives in a decision-making problem. Puška et al. [27] used RAWEC, a novel MCDM approach, to determine locations for agricultural distribution centers.
1.2.3 Research Gaps
When the past researches in the literature are evaluated, it is seen that the researches conducted with MCDM methods in the energy sector are mostly concentrated in different branches using different weighting and ranking methodologies. Therefore, the limited number of studies on the selection of renewable energy resources, which is an indispensable part of the energy sector, constitutes the first motivation of this study. In addition, when the literature is examined, a generally applied and accepted set of criteria for the selection of RESs has not been identified. In addition, considering the existing studies, it can be concluded that there is no generally accepted decision support tool or mathematical model for the evaluation of alternatives in the evaluation process for real world decision makers and managers in the energy sector.
1.3 Methodology for Research
This chapter presents a hybrid mathematical framework for multi-criteria evaluation of renewable energy sources. The flowchart of the proposed framework, the WENSLO method for weighting the criteria and RAWEC method for selecting the optimum renewable energy source, is given in Figure 1.1.
Figure 1.1 Flowchart of WENSLO–RAWEC multi-criteria model.
Within the scope of the suggested model for selecting the best RES, acceptable criteria were first established in accordance with published research and expert comments. As a result, six distinct criteria were established. In the following step, the weight values of the assessment criteria were determined using WENSLO method. After the weights were calculated, RESs were rated using RAWEC method. Finally, the validity and applicability of the proposed model were examined using various sensitivity analyses.
1.3.1 WENSLO Method for Criteria Prioritization
The weight coefficients of the criterion are determined using WENSLO method. This method was first presented by Pamučar et al. [26] in 2023. It has a seven-step process framework as follows:
Step 1. Preparing the initial decision matrix
A1, A2,..., Am are a vector space of alternatives that represents a collection of alternatives (m alternative). C1, C2,..., Cn represent a vector space of criteria (n criteria). The combined decision matrix (Z) is obtained using Equation 1.1.
zij is criterion j. in alternative i.
Step 2. Obtaining normalization matrix (T).
Equation 1.2 is used to normalize the combined decision matrix.
Step 3. Calculate the criteria class interval (ρj).
The size of the j-th criteria class interval is computed using Sturges’ rule, Equation 1.3:
Step 4. Calculate the criteria slope (tanφj).
The slope of the criterion is calculated by Equation 1.4.
Step 5. Determination of the criterion envelope (εj)
The sum of the partial Euclidean distances between two successive criteria is determined using Equation 1.5.
Step 6. Describe the envelope–slope ratio (δj)
Equation 1.6 calculates the ratio of the entire Euclidean distance to the criteria slope.
Step 7. Obtain weights (Wj) of each of the criteria
Equation 1.7 calculates the weights depending on the criteria’s significance coefficient.
1.3.2 RAWEC Method to Rank Alternatives
For the ranking of alternatives, RAWEC method is used. This method was first presented by Puška et al. [27] in 2024. It has a four-step process framework as follows:
Step 1. Construction of the initial decision matrix
The combined decision matrix (X) is obtained using Equation 1.8.
xij is criterion j. in alternative i .
Step 2. Normalization of the decision matrix
When normalizing the initial decision matrix, double normalization is performed with Equations 1.9 and 1.10.
Step 3. Calculation of deviation from criterion weight
Equations 1.11 and 1.12 provide the weighting of the normalized decision matrix as well as the computation of the deviation from the weighted criterion.
Step 4. Calculation of the value of RAWEC method
It is calculated according to Equation 1.13.
RAWEC method returns a number ranging from −1 to 1. The superiority of an alternative is decided by how valuable the alternative’s strategy is. The alternative with the greatest value is the optimal choice.
1.3.2.1 Case Study
In terms of both environmental sustainability and economic and energy security, Turkey places an importance on the use of RESs. These requirements are as follows:
Turkey’s geographical location and climatic conditions provide a favorable environment for the utilization of different RESs. Various sources, such as solar, wind, hydroelectric, geothermal, and biomass, increase energy security by diversifying energy supply.
Turkey is turning toward RE resources to diversify its energy supply and reduce external dependence. RESs can reduce foreign policy risks by increasing the country’s energy supply security.
The renewable energy sector offers significant economic development and employment opportunities for Turkey. The development and utilization of solar, wind, hydroelectric, and other RESs can stimulate the local economy and create new job opportunities.
Table 1.1 Alternatives and criteria for the study.
Alternative
Criteria
Hydropower (A1)
Installed power (Mv) (C1) (max)
Geothermal (A2)
Number of power plants (C2) (max)
Wind (A3)
Installation cost (USD/kW) (C3) (min)
Solar (A4)
Levelized cost of electricity (USD/kWh) (C4) (min)
Biomass (A5)
Share in electricity generation (%) (C5) (max)
Lifespan of power plants (years) (C6) (max)
In this case study, five RESs are evaluated as the optimum solution and given in Table 1.1 together with the six criteria used.
These criteria can be categorized in terms of environmental, economic, and technological dimensions as follows:
Environmental:
C6: The lifetime of power plants is important for environmental sustainability. Long-lived power plants can help minimize environmental impacts and conserve natural resources.
Economic:
C3: Installation cost is important for economic sustainability. Lower installation costs can facilitate the financing of projects and make energy resources more accessible.
C4: The levelized cost of electricity determines the economic competitiveness of the energy source. Low-cost electricity generation provides cheaper energy to consumers and increases competition in energy markets.
C5: The share in electricity generation is economically important because the contribution of an energy source to total electricity generation indicates its share in the energy market.
Technological:
C1: Installed capacity refers to technological capacity. Higher installed capacity can increase the capacity and efficiency of a power plant.
C2: The number of power plants determines the technological distribution of an energy source. More power plants can spread the energy source over a wide geographical area.
1.4 Results
For the primary stage, as in shown in Figure 1.1, alternatives and criteria are identified in Table 1.1. Then, the evaluation of six criteria was presented using WENSLO method. Then, the evaluation of five alternatives, which were assigned codes A1 to A5, was carried out. The alternatives were evaluated against the six criteria assigned to codes C1 to C6. Based on the information described, the following section presents the detailed applications of WENSLO and RAWEC methods.
1.4.1 Application of WENSLO Method
WENSLO method is offered for determining the weight coefficients of the criterion. Table 1.2 shows the criteria and alternatives utilized in the study.
Table 1.2 Initial decision-making matrix.
RES
C1
C2
C3
C4
C5
C6
A1
31,582.68
757
1,870
0.044
10.4
25
A2
1,691.34
63
4,468
0.071
3.2
30
A3
11,426.32
364
1,355
0.039
5.7
20
A4
9,820.34
16,144
883
0.057
3.4
30
A5
1,990.29
73
2,543
0.076
3.2
50
Sources: [28–30].
The normalized decision matrix obtained using Equation 1.2 is given in Table 1.3.
The calculation is exemplified as follows:
Then, the criterion class range is obtained according to Sturges rule with Equation 1.3, as given in Table 1.4.
The slope of the criterion is obtained by Equation 1.4 and presented in Table 1.5.
Criteria envelope is obtained with Equation 1.5, as given in Table 1.6.
Table 1.3 Normalized decision matrix.
C1
C2
C3
C4
C5
C6
A1
0.5589
0.0435
0.1682
0.1533
0.4015
0.1613
A2
0.0299
0.0036
0.4018
0.2474
0.1236
0.1935
A3
0.2022
0.0209
0.1219
0.1359
0.2201
0.1290
A4
0.1738
0.9278
0.0794
0.1986
0.1313
0.1935
A5
0.0352
0.0042
0.2287
0.2648
0.1236
0.3226
Table 1.4 Size of the j-th criterion class interval.
Size
C1
C2
C3
C4
C5
C6
ρ
j
0.1592
0.2782
0.0971
0.0388
0.0837
0.0583
Table 1.5 Criteria slope values.
Criteria slope
C1
C2
C3
C4
C5
C6
tanφ
j
1.5701
0.8987
2.5758
6.4419
2.9875
4.2909
Table 1.6 Criteria envelope values.
Envelope value
C1
C2
C3
C4
C5
C6
ε
j
1.1598
2.4729
0.8333
0.3703
0.6241
0.3820
The envelope–slope ratios are obtained with Equation 1.6 and shown in Table 1.7.
The criteria weights are calculated by Equation 1.7, as shown in Table 1.8.
Table 1.7 Envelope–slope ratios.
Ratio
C1
C2
C3
C4
C5
C6
δ
j
1.3538
0.3634
3.0910
17.3957
4.7867
11.2317
Table 1.8 Final criteria weight values.
Criteria
C1
C2
C3
C4
C5
C6
w
j
0.0354
0.0095
0.0809
0.4551
0.1252
0.2939
Levelized cost of electricity (C4)> Lifespan of power plants (C6)> Share in electricity generation (C5) are the three most important criteria. The fact that these criteria have been identified as the most important as a result of the analysis highlights the key elements needed to ensure the successful selection of renewable energy sources.
1.4.2 Application of the RAWEC Method
Table 1.2 was used as the initial decision matrix. Two normalized decision matrices were obtained using Equations 1.9 and 1.10 and presented in Tables 1.9 and 1.10.
Then, deviations from the criteria weights were calculated using Equations 1.11 and 1.12. Finally, RAWEC values were calculated with Equation 1.13 and given in Table 1.11.
Biomass (A5) > geothermal (A2) > hydropower (A1) > solar (A4) > wind (A5) is the order of preference for renewable energy sources. Biomass, geothermal, and hydroelectric energy are prioritized due to their local availability and continuous resource. Solar and wind energy have increasing importance due to technological developments.
1.4.3 Sensitivity Analysis
This section presents a two-stage sensitivity analysis to show that the proposed technique works. In this case, the first step was to see if changing the criterion weights impacted the ranking results. The suggested hybrid model is then compared to current MCDM methods.
1.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights
Following the identification of the “most important criteria (Cn)” using the weight values provided by the criterion weighting technique, a sensitivity analysis can be performed by modifying the weight of the “most important criteria (Cn)” to investigate how the proposed model affects ranking performance. This strategy begins by generating a new weight factor vector. In each case, the weight of the most essential criterion is lowered by 15%,
Table 1.9 Benefit normalization values.
RES
C1
C2
C3
C4
C5
C6
A1
1.0000
0.0469
0.4185
0.5789
1.0000
0.5000
A2
0.0536
0.0039
1.0000
0.9342
0.3077
0.6000
A3
0.3618
0.0225
0.3033
0.5132
0.5481
0.4000
A4
0.3109
1.0000
0.1976
0.7500
0.3269
0.6000
A5
0.0630
0.0045
0.5692
1.0000
0.3077
1.0000
Table 1.10 Cost normalization values.
RES
C1
C2
C3
C4
C5
C6
A1
0.0536
0.0832
0.4722
0.8864
0.3077
0.8000
A2
1.0000
1.0000
0.1976
0.5493
1.0000
0.6667
A3
0.1480
0.1731
0.6517
1.0000
0.5614
1.0000
A4
0.1722
0.0039
1.0000
0.6842
0.9412
0.6667
A5
0.8498
0.8630
0.3472
0.5132
1.0000
0.4000
Table 1.11 Final ranking order calculated using RAWEC method.
RES
ν
ij
Q
i
Rank
A1
0.3946
0.2821
−0,1663
3
A2
0.2772
0.3680
0.1407
2
A3
0.5427
0.1211
−0.6351
5
A4
0.4049
0.2878
−0.1690
4
A5
0.1642
0.4573
0.4716
1
yielding a new weight factor vector. Ref. [31] suggest using Equation 1.14 to calculate the new weight value of the criteria as shown below.
where wnβ indicates the new weight values calculated for the criteria. wnα denotes the reduced value of the criteria. While the original value of the criteria is denoted by wβ, the original value of the reduced criteria is denoted by wn.
According to WENSLO method, C4 has the highest weight (0.4541). Equation 1.14 yielded 15 situations. Scenario 0 (S0) was chosen from among these 15 possibilities because it corresponds to the original weight value of criteria C4. According to these situations, C5 = 0.3868 in Scenario 1 (S1), and C4 = 0.0398 in Scenario 15. Following each change of C4,
Figure 1.2 Weight modifications based on various scenarios.
Figure 1.3 Alternatives are ranked based on the new criteria weights.
Equation 1.14 was used to calculate the weights of the remaining criterion. For instance, the computation for the first Scenario (S1) was done as follows:
After getting the new criteria weights, as shown in Figure 1.2, RAWEC utility function values were calculated once more for ranking the alternatives according to the 15 scenarios, and the ranking values are displayed in Figure 1.3.
Figure 1.3 shows that changing the weight values assigned to the criterion based on the generated scenarios has no substantial influence on the ranking of any alternative indicating that the model is sensitive to weight coefficient changes. Finally, the ranking results show that the proposed hybrid model is consistent and stable on a large scale demonstrating the MCDM framework’s effectiveness, robustness, and adaptability.
1.4.3.2 Comparison With Other MCDM Methods
The study compared the outcomes of RAWEC method to those of other methods such as WASPAS, MARA, MARCOS, and CRADIS.
Figure 1.4 shows that the ranking positions for alternatives other than hydropower, wind, and solar exhibit minimal variation across all MCDM methods applied. This consistency indicates that the results obtained are robust and reliable. In this study, the Spearman correlation coefficient was employed to statistically justify the ranking of the suggested model. The analysis conducted using SPSS 28 software showed an average correlation of 0.975 between RAWEC method and the other four MCDM methods, confirming the reliability of the proposed model.
Figure 1.4 Comparison between ranking performances of MCDM methods.
1.5 Discussion, Practical and Managerial Implications
This priority ranking reflects the prioritization of certain factors in the choice of renewable energy sources. Locally available and continuous sources, such as biomass, geothermal, and hydroelectric energy, play an important role in terms of energy supply security and sustainability. Solar and wind energy are becoming increasingly important due to technological developments. This prioritization can serve as an important guide to be taken into account in determining energy policies and investments.
The practical implications of this prioritization provide a valuable tool for decision makers in the selection and integration of renewable energy sources. In particular, it can help make the right energy decisions, taking into account factors such as local availability, cost effectiveness, and environmental impacts. This can contribute to taking strategic steps to minimize environmental impacts and ensure economic sustainability while increasing security of energy supply.
By using this priority ranking for managerial implications, energy policies and strategies of countries can be shaped. In particular, it is important to consider these priorities when making strategic decisions such as the integration of RESs and infrastructure investments. Furthermore, it is necessary to continuously assess and update strategies by taking into account technological innovations and market trends. In this way, companies and organizations can gain a competitive advantage in the energy transition process and be prepared for a sustainable future.
1.6 Conclusions, Limitations, and Future Directions
Six criteria were used to prioritize the choice of renewable energy sources. A lower levelized cost of electricity indicates that the energy source is more economical and a more competitive option in energy production. Therefore, the priority of this criterion is high. Power plants with a long lifetime ensure a stable energy supply and increase investment returns. Therefore, it is important for the sustainability of energy resources and ranks second. Sources with a high share in electricity generation usually meet the country’s energy needs to a large extent and play a priority role in energy policies. However, this criterion is not as critical as others and ranked third. Sources with a low installation cost can reduce investment costs and make projects more attractive. However, installation cost ranked fourth in the ranking. It determines the total installed capacity of an energy source. Large-scale power plants generally generate more electricity and meet energy needs more efficiently. However, it is not as important as other criteria and ranks fifth. In most cases, more power plants or facilities allow energy supply to be spread over a wider geographical area and increase energy security. However, this criterion has the lowest priority and ranks sixth. With this research, an important step has been taken in clarifying the priorities in the choice of renewable energy sources.
When the ranking of RESs is analyzed, it is seen that biomass and geothermal energy sources are prioritized. First, biomass and geothermal energy sources are locally available and continuously renewable. This can contribute to local economies and increase energy supply security. Furthermore, biomass sources can provide solutions to waste management problems, while geothermal energy sources can reduce environmental impacts with low carbon emissions. Hydropower comes third on the list as it has a long history and is a reliable energy source with large-scale power plants. However, the environmental impacts of hydroelectric power projects and their effects on local ecosystems should be taken into consideration. Solar and wind energy are RESs that are rapidly developing technologically and are of increasing importance. However, considering their installation costs and their share in electricity generation, biomass and geothermal energy sources seem to be more prioritized at present. Overall, this ranking reflects the different characteristics and advantages of renewable energy sources. It emphasizes that energy policies and investments should be made taking into account various factors.
This study has some limitations. First, the choice of methods and criteria used in determining the priority ranking may limit the general validity of the results. In addition, the scope of the data sets used in the study may also affect the results. In addition, it should be kept in mind that energy policies and preferences may change over time, which may limit the validity of the results.
Future research could examine the priority ranking in the preference of RESs in more detail. In particular, studies conducted in different geographical regions and time periods can increase the general validity of the results. In addition, taking into account new technological developments and market trends can contribute to shaping future energy policies and strategies more effectively. Research in this direction can make a significant contribution to a sustainable energy future.
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