[Podcast] Optimizing wave energy converter benchmarking with a fuzzy based decision-making approach
18 November, 2024
Key words: Wave Energy; Wave Energy Converters; Renewable Energy; Benchmarking; Multi-Criteria Decision Making (MCDM); the MEthod based on the Removal Effects of Criteria (MEREC); the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo); Sustainability; Energy Technology Assessment; Grid Connectivity.
The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental in harnessing ocean waves for electricity generation.
The quest for sustainable and renewable energy sources has led to a growing interest in wave energy, recognized for its immense potential and significance (Clemente, Rosa-Santos, & Taveira-Pinto, 2021). Unlike other renewable sources, wave energy offers a consistent and powerful supply, largely untapped and capable of meeting global energy demands multiple times over (Gallutia, Fard, Soto, & He, 2022). Its exploitation promises a revolution in the energy sector, providing a clean, inexhaustible energy source that could significantly reduce our reliance on fossil fuels (IEA, 2022). The environmental benefits of wave energy are notable as well, offering a greener alternative that minimizes carbon footprint and ecological disruption, making it a pivotal element in the transition towards sustainable energy solutions (Quadrelli & Peterson, 2007). As wave energy technology evolves, the importance of benchmarking WEC technologies cannot be overstated. Benchmarking serves as a critical evaluative process to compare and contrast different WEC systems, aiming to identify the most effective and cost-efficient among them (Choupin, Pinheiro Andutta, Etemad-Shahidi, & Tomlinson, 2021). This process is vital for the continual improvement and innovation within the field of wave energy. It provides valuable insights for developers, investors, and policymakers, helping to shape future developments, allocate resources wisely, and establish industry standards (Gao, Ertugrul, Ding, & Negnevitsky, 2020). Effective benchmarking can accelerate the adoption of wave energy by highlighting successful technologies and practices, thereby paving the way for wider acceptance and implementation.
In addressing the intricate task of benchmarking WEC technologies, Multiple Criteria Decision-Making (MCDM) methods emerge as powerful tools. These methods enable a holistic and nuanced analysis by considering a wide range of criteria, from technical performance and economic feasibility to environmental impact and social acceptance (Nasrollahi, Kazemi, Jahangir, & Aryaee, 2023; Sahoo & Goswami, 2023). MCDM facilitates balanced evaluation, accommodating the multifaceted nature of decision-making in this context. This approach is especially pertinent given the diverse and sometimes conflicting criteria involved in assessing wave energy technologies, ensuring that decisions are well-rounded and robust (Stojčić, Zavadskas, Pamučar, Stević, & Mardani, 2019). In widely used MCDM methods, the MEthod based on the Removal Effects of Criteria (MEREC) stands out for its objective approach to determining the weight of various criteria. It systematically analyzes the impact of removing a criterion, thereby revealing its relative importance in the overall decision-making process (Keshavarz-Ghorabaee, Amiri, Zavadskas, Turskis, & Antucheviciene, 2021). This method ensures that each criterion’s contribution is accurately reflected, leading to more balanced and equitable decision-making. On the other hand, the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo) method introduces an advanced level of decision analysis by incorporating spherical fuzzy sets. Unlike conventional fuzzy numbers, which typically model uncertainty using a single membership function, spherical fuzzy numbers extend this concept by incorporating three-dimensional membership, non-membership, and hesitancy degrees (Farman, Khan, & Bibi, 2024). This richer representation allows SFNs to capture a more nuanced and accurate portrayal of uncertainty and vagueness inherent in human judgments. Traditional fuzzy sets and their extensions, such as intuitionistic and Pythagorean fuzzy sets, primarily focus on two dimensions, limiting their ability to fully encompass the complexities of decision-making scenarios. In contrast, SFNs offer enhanced flexibility and expressiveness, providing a comprehensive framework that improves the robustness and precision of MCDM analyses (Gül, 2020; Kutlu Gündoğdu & Kahraman, 2019b). This makes spherical fuzzy sets particularly useful in scenarios where decision data are highly uncertain and subject to multiple interpretations, thereby enhancing the overall reliability and effectiveness of the decision-making process (Wang, Nhieu, & Liu, 2024; Wang, Nhieu, & Wang, 2024). This approach allows for a more nuanced representation of uncertainty and vagueness inherent in human judgments (Le & Nhieu, 2022a). SF-CoCoSo synthesizes these fuzzy evaluations into a comprehensive compromise solution, skillfully balancing between the best and most feasible options. The integration of MEREC and SF-CoCoSo in benchmarking WEC technologies promises a more refined, accurate, and comprehensive assessment, paving the way for identifying the most promising and efficient wave energy converters. This innovative combination marks a significant advancement in the field, offering robust tools for tackling the complexities of technology assessment in renewable energy systems.
Despite comprehensive insights from the literature on WECs and MCDM, a research gap exists in integrating advanced fuzzy logic with objective weighting methods for WEC benchmarking. Specifically, studies leveraging spherical fuzzy sets with the MEREC method are scarce. This presents an opportunity to improve objectivity and precision in WEC assessments by addressing uncertainty and subjectivity. Furthermore, while methods like CoCoSo balance competing criteria, their application in WEC benchmarking, especially with spherical fuzzy logic, remains underexplored. This study aims to fill these gaps by developing a fuzzy based, objectively weighted decision-making approach, refining the methodology for sustainable energy decisions.
The motivation behind employing an integrated MCDM approach in this study stems from the recognition of the complex and multi-dimensional challenges inherent in benchmarking WEC technologies. By combining various MCDM methodologies, this approach seeks to address the diverse set of criteria involved in evaluating WEC technologies. This integration aims to refine the decision-making process, enhancing its accuracy, comprehensiveness, and reliability. It represents an innovative step forward in tackling the intricate task of benchmarking in the wave energy sector, potentially leading to more informed and effective decisions.
This study is primarily aimed at advancing the benchmarking process of WEC technologies through the integration of two distinct methodologies: the objective weighting capabilities of the MEREC and the nuanced decision analysis afforded by the SF-CoCoSo method. By fusing these approaches, the research endeavors to provide a comprehensive and balanced evaluation of WEC technologies. The pivotal role of benchmarking WEC technologies for advancing wave energy as a viable and sustainable energy source is underscored. It not only identifies leading technologies but also informs policy, guides research and development efforts, and encourages industry-wide standards and best practices (Gao et al., 2020).
An innovative, integrated MCDM approach to the benchmarking of WEC technologies is contributed by this study, promising to enhance the clarity, accuracy, and effectiveness of technology assessments. Through this pioneering methodology, the strategic development and deployment of wave energy converters are aimed to be supported, marking a crucial step forward in the sustainable harnessing of wave energy.
The study commenced with a focus on the burgeoning field of wave energy, recognizing the substantial untapped potential of ocean waves as a renewable energy source. Given the centrality of WECs in transforming wave power into electricity, the study aimed to evaluate and benchmark WEC technologies to determine the most efficient and viable solutions. To achieve this objective, the study employed an integrated approach, combining the MEREC and the SF-CoCoSo methods. MEREC was utilized to objectively weigh the various criteria crucial for evaluating WEC technologies, while SF-CoCoSo aided in aggregating and analyzing the complex decision-making data to derive a final evaluation score for each technology.
The study’s contributions are manifold. It provides a nuanced framework for benchmarking WEC technologies, thereby assisting stakeholders in making informed decisions. Additionally, the study advances the application of integrated MCDM approaches within the renewable energy sector, demonstrating the effectiveness of combining MEREC and SF-CoCoSo in a complex decision-making landscape. Our findings present a clear hierarchy of WEC technologies based on their performance across multiple criteria, including efficiency, cost, environmental impact, and grid connectivity. The study highlights the PAB technology as the front runner, with its superior overall performance, followed by the OWC and TST technologies as strong alternatives. Notably, it also emphasizes the importance of grid connection and adaptability to different wave conditions as critical factors in the benchmarking process.
Despite the valuable insights provided by this study, it recognizes a number of limitations that highlight areas for future exploration and development. The selected criteria for evaluating WEC technologies, while comprehensive, may not fully capture all the dimensions that influence their performance. This limitation opens up an avenue for future research to broaden the scope of evaluative criteria, incorporating emerging factors that could affect WEC technologies as advancements continue and new challenges arise in the field of renewable energy. Furthermore, the objectivity of the criteria weightings, despite being a strength of the current approach, might be subject to the shifting landscapes of the wave energy market and technological evolution. This suggests a need for adaptive methodologies that can dynamically adjust to the changing priorities and innovations within the sector. Future studies could focus on developing more flexible weighting mechanisms that respond to real-time market and technological data, thereby enhancing the relevance and timeliness of the benchmarking process. The assumption that the chosen MCDM methods adequately encapsulate the complexity inherent in the decision-making process for WEC technology assessment may not be universally held true. This indicates a promising research direction in exploring alternative MCDM methods that might offer different perspectives or handle specific aspects of the decision-making process more effectively. The exploration of these alternative methods could reveal new insights and possibly more efficient approaches to benchmarking WEC technologies. In the spirit of continuous improvement, this study serves as a pivotal step towards the systematic and rigorous benchmarking of WEC technologies. It emphasizes the importance of ongoing refinement of the assessment methodologies to align with technological advancements and market developments. Future research is thus encouraged not only to expand the criteria and explore alternative MCDM methods but also to implement strategies for validating the benchmarking process against real-world performance data. Such validation is crucial for ensuring the robustness and relevance of the findings, providing stakeholders with reliable and actionable insights. Moreover, there is an opportunity to integrate advancements in data analytics and artificial intelligence to enhance the benchmarking process. Future work could investigate the application of machine learning algorithms for predictive analysis and trend forecasting in the wave energy domain, offering a forward-looking component to the benchmarking process.
Please refer to the full research titled Optimizing wave energy converter benchmarking with a fuzzy based decision-making approach HERE.
Author: Dr. Nhieu Nhat Luong – University of Economics Ho Chi Minh City (UEH).
This article is part of the series spreading research and applied knowledge from UEH with the message “Research Contribution For All”. UEH cordially invites readers the upcoming UEH Research Insights issue.
News, photos: Author, UEH Department of Communications and Partnerships

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