Selection Of Renewable Energy Sources Research Articles

Decision making for renewable energy source selection using q-rung linear Diophantine fuzzy hypersoft aggregation operators

Decision making for renewable energy source selection using q-rung linear Diophantine fuzzy hypersoft aggregation operators

A strategic multi-criteria decision-making framework for renewable energy source selection in Saudi Arabia using AHP-TOPSIS

The Kingdom of Saudi Arabia (KSA) is targeting 50 % of its energy generation from renewable energy sources (RES) by 2030. However, a comprehensive approach is needed to prioritize the available RES in the kingdom. This study employs a survey-based approach which gathered insights from several academic and industrial experts and then utilizes analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods to determine the optimal RES. In this case, hybrid RES (solar-wind, solar-geothermal, solar-biomass) is evaluated against single-source alternatives based on different criteria including cost-effectiveness, land conservation, low intermittency, resource potential, design simplicity, and social acceptance. The results showed that cost-effectiveness emerged as the predominant criterion with importance of 31 % followed by low intermittency and resource potential contributing with 24 % and 22 % in importance, respectively. Based on these findings, solar energy is recognized as the best RES, with a priority level of 23 % followed by the hybrid solar-wind and hybrid solar-geothermal, which attained priority levels of 20 and 16 %, respectively. Subsequently, a sensitivity analysis was carried out to assess the impact of varying criteria weights. The findings offer the decision maker useful insights into the priority of different RES in KSA.

CARD: Comprehensive approach based on relative difference for decision-making problems with dual evaluation forms − Application to sustainable renewable energy selection

Numerous multi-attribute decision-making (MADM) methods exist, each aiming to improve decision-making in complex scenarios involving multiple criteria. However, challenges remain, particularly in integrating both quantitative and qualitative evaluations and addressing issues related to attribute weighting and normalization. This study introduces a novel MADM method called the CARD method, designed to address these challenges. The CARD method offers a unified framework that combines direct and pairwise evaluations using logarithmic relative difference measures to compare alternatives in terms of relative superiority. Key innovations of the CARD method include: 1) developing a new utility function; 2) proposing a new closeness coefficient measure; 3) the ability to handle joint quantitative and qualitative inputs; and 4) introducing a new criterion satisfaction coefficient, the so-called strictness degree. These features enable the CARD method to effectively solve MADM problems involving mixed criteria. To demonstrate its applicability, we apply the CARD method to a case study on renewable energy source selection in Spain. The results confirm that wind energy is the optimal renewable energy source for Spain, highlighting its economic, engineering, and sustainable advantages. This study underscores the potential of the CARD method to enhance decision-making processes and provide valuable insights for policymakers and practitioners.

Impact of Agricultural Technical Efficiency on Farm-Gate Emission: An Implementation of Environmental Kuznets Curve in Asian Developing Countries

<p>The Environmental Kuznets Curve (EKC) explains that economic activities in developing countries, including agriculture, increase environmental damage. Increased technical efficiency is one approach that is frequently suggested for reducing the negative impacts of farming practices. Unfortunately, there are no studies that investigate this at the macro (national) level. This study examines the impact of technical efficiency on farm-gate emissions in Asian developing countries. Data were collected from 25 developing countries in Asia from 1992 to 2021 and were analyzed using frontiers and the generalized method of moments. The findings demonstrate that technical efficiency is insufficient to prevent increases in CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions. This finding is consistent with the EKC. Gas emissions also rise because of gross domestic product (GDP) and population growth. Human development is the only way to prevent a rise in emissions of these 3 gases. Meanwhile, renewable energy and food prices have varying impacts on CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions reduction. Therefore, based on these findings, it is suggested that developing countries increase the utilization of natural production factors and organic farming, improve human capital development, and conduct a wise selection of renewable energy sources.</p>

Selection of Renewable Energy Sources for Modular and Mobile “Green Classroom” Facilities

This article aims to demonstrate the technical capabilities and effectiveness of an energy production and management system for school facilities using a modular solution. The system is assumed to generate electricity from renewable sources, such as wind or sun. The potential of renewable energy sources in Cracow, Poland, was assessed, with a focus on solar energy (photovoltaic panels, PV). Taking into account the installation of heating and other equipment, an analysis of the facility’s electricity demand was carried out. The study recommended the use of a heat pump system to heat and cool the facility. Renewable energy sources will meet 81% of the facility’s projected annual demand, according to the study. An analysis of the energy consumption and production profiles shows that almost 69% of the energy produced by the PV panels is consumed on site. Of the remaining energy, 31% is fed back into the grid and sold to the grid operator or used by other facilities within the shared settlement. The overall balance results in a small electricity deficit that must be covered by the grid. If suitable sites are available, the facilities under study could consider installing a wind turbine as a potential supplement to the energy deficit.

Cosine and cotangent similarity measures for intuitionistic fuzzy hypersoft sets with application in MADM problem

Intuitionistic fuzzy hypersoft sets (IFHSSs) are a novel model that is projected to address the limitations of Intuitionistic fuzzy soft sets (IFSSs) regarding the entitlement of a multi-argument domain for the approximation of parameters under consideration. It is more flexible and reliable as it considers the further classification of parameters into their relevant parametric valued sets. In this paper, we proposed some trigonometric (cosine and cotangent) similarity measures and their weighted trigonometric similarity measures (SMs). Trigonometric Similarity measures (SMs) for intuitionistic fuzzy hypersoft sets (IFHSSs) are significantly implied to check the similarity measures and help to determine the similarity between different factors. Also, in order to evaluate the validity of the significant study and apply the results to a daily life problem. We use them to solve problems involving the selection of renewable energy sources. According to several technical contributing factors, the analysis identifies the ideal location for the implementation of the energy production units. Future case studies with many features and additional bifurcation along with multiple decision-makers can use the suggested methodologies. Also, several existing structures, such as fuzzy, Pythagorean fuzzy, Neutrosophic theories, etc., can be utilized with the suggested method.

Effective hydropower renewable energy source selection using fuzzy neutrosophic boundary approximate area

Small hydropower plants in rural areas are contributing to marginal economic growth and social progress, but their social sustainability assessment is under-studied. Previous studies have shown that no single renewable energy source (RES) can fully meet all sustainability criteria, despite their focus on single-user systems. Scaled renewable energy systems have varying impacts across different systems, involving technical, environmental, social, and economic aspects. This paper proposes a Triangular Fuzzy Neutrosophic Numbers (TFNNs) as an extension of the multi-attributive border approximation area comparison (MABAC) method for hybrid multi-criteria decision-making (MCDM). The proposed method is utilized to assess the selection of hydropower as the most efficient and sustainable renewable energy source for society. Pairwise comparison matrices were used to determine priority vectors and weighted averages of indicators for seven criteria. The evaluation framework analyzed five key dimensions of hydropower plants. The most stable renewable energy source, pumped storage hydropower (60.78%), was found to be followed by run-of-river hydropower (56.39%), impoundment hydropower (50.03%), storage hydropower (39.78%), and offshore hydropower (07.95%). The study offers valuable insights for policymakers and implementer on sustainable electricity solutions for hydropower. Pumped storage hydropower (PSH) improves facility efficiency by ensuring optimal operation of coal and nuclear generators without energy loss. The system can provide a stable and reliable power storage solution for renewable energy sources like wind and solar.

A novel voltage lifting technique of switched-inductor cell based modified LUO converter topology for water pumping system

The persistent exhaustion of traditional energy sources with their impacts on the environment has been initiating a significant interest in a selection of renewable-energy sources (RES) based water-pumping system. Among several renewable sources, solar-PV is the most promising and practical source for water-pumping applications that can be easily installed on a building's roof. The available solar-PV energy is integrated to AC electric motor through front-end DC-DC boost converter topology. Among the various DC-DC converter topologies, a novel switched-inductor type modified LUO converter has been proposed for solar-PV powered water pumping system. The proposed switched-inductor type modified LUO (SI-MLUO) converter have simple structure which delivers high voltage gain with low leakage currents, low current ripples, reduced voltage spikes, low dv/dt stress and high efficiency over the several conventional DC-DC converters. The operating modes and performance of proposed SI-MLUO converter topology is verified by using MATLAB/Simulink tool, simulation results are validated with conventional topologies.

A novel consensus model with probabilistic linguistic preference relation for the utilization mode selection of renewable energy sources

A novel consensus model with probabilistic linguistic preference relation for the utilization mode selection of renewable energy sources

Evaulation of renewable energy source alternatives prioritization

Today the renewable energy is an alternative energy source being more important. The reasons for its importance are minimizing the risks at environmental, economic and social areas caused by the traditional energy sources and reducing the need for energy importation. Because of these it is important to determine the affecting criteria of the renewable energy sources for the investors of this industry as a guide. In modelling and analysis of the criteria and the sub-criteria MCDM (Multi Criteria Decision Making Model) can be used. In this study, the ANP model is used to propose evaluation of the renewable energy source selection criteria. In this study, an application of the recommended basis for the renewable energy source selection problem criteria and the ANP model for the optimum renewable energy source project is proposed. The expert opinions are employed to gather data in the proposed model. The technical, environmental, economic, and social risk criteria and the importance levels of the sub-criteria are analyzed under the light of the expert opinions. The Hazardous Waste, Effects to Climate Changes, Noise, Reduction in Gas Emission and National Energy Security are found as the superior criteria.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat, sunlight, tides, rain, and wind. Energy resources are vital for all countries in terms of their economies and politics. As a result, selecting the optimal option for any country is critical in terms of energy investments. Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming. In the present work, the authors suggest fuzzy multi-characteristic decision-making approaches for renewable energy source selection, and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data. This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique, which agrees with professional assessment scores to be linguistic phrases, fuzzy numbers, or crisp numbers. The hybrid methodology is based on fuzzy set ideologies, which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics. The best-suited renewable energy alternative is discovered using the approach presented.

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection

Selecting the optimal renewable energy source (RES) is a complex multi-criteria decision-making (MCDM) problem due to the association of diverse conflicting criteria with uncertain information. The utilization of Fermatean fuzzy numbers is successfully treated with the qualitative data and uncertain information that often occur in realistic MCDM problems. In this paper, an extended complex proportional assessment (COPRAS) approach is developed to treat the decision-making problems in a Fermatean fuzzy set (FFS) context. First, to aggregate the Fermatean fuzzy information, a new Fermatean fuzzy Archimedean copula-based Maclaurin symmetric mean operator is introduced with its desirable characteristics. This proposed operator not only considers the interrelationships between multiple numbers of criteria, but also associates more than one marginal distribution, thus avoiding information loss in the process of aggregation. Second, new similarity measures are developed to quantify the degree of similarity between Fermatean fuzzy perspectives more effectively and are further utilized to compute the weights of the criteria. Third, an integrated Fermatean fuzzy-COPRAS approach using the Archimedean copula-based Maclaurin symmetric mean operator and similarity measure has been developed to assess and rank the alternatives under the FFS perspective. Furthermore, a case study of RES selection is presented to validate the feasibility and practicality of the developed model. Comparative and sensitivity analyses are used to check the reliability and strength of the proposed method.

Selection of renewable energy sources: a novel VIKOR approach in an intuitionistic fuzzy linguistic environment

Selection of renewable energy sources: a novel VIKOR approach in an intuitionistic fuzzy linguistic environment

An extended interval-valued Pythagorean fuzzy WASPAS method based on new similarity measures to evaluate the renewable energy sources

The recent decade has arisen a significant issue in the energy sector, which is how to select proper sources of renewable energy as a sustainable substitution for conventional forms of fossil fuels. The way of solving this problem will meaningfully affect the environmental development and economic growth. To handle the issue, various scholars have concentrated on preferring the desirable energy source by employing the decision-making model based on the different fuzzy sets methods. Therefore, the aim of this paper is two folds. Firstly, various renewable resources potential are reviewed, and secondly, an assessment model is developed for prioritizing renewable options. Five major resources, hydropower, solar, wind, biomass, geothermal are considered. The present paper attempted to propose an integrated method on the basis of the Weighted Aggregated Sum Product Assessment (WASPAS) method in a way to provide an effective solution to decision-making problems on interval-valued Pythagorean fuzzy sets (IVPFSs). For the aim of calculating the criteria weights, the subjective weights offered by decision-makers were combined with objective weights achieved by means of the similarity measure method. This combination helped to attain more realistic weights. In the case of objective and subjective weights, new similarity measures and enhanced score functions were applied to IVPFSs. In addition, a renewable energy source selection problem is addressed in order to demonstrate the developed method is completely applicable to the real-world Multi-Criteria Decision-Making (MCDM) problems. This study also involves a sensitivity analysis using various weights of criteria as well as various values of the method’s parameters in a way to approve the developed method stability. As revealed by the performed analysis, the integration of the subjective and objective weights improved the developed method stability with various weights of criteria. To reliably evaluate the performance of the method developed here, its results were compared with those of different methods formerly proposed in the literature. The evaluation results showed that the wind energy with a maximum assessment score degree (0.6259) using the proposed method was found the best option for selecting renewable energy sources over diverse criteria.

Renewable energy source selection for a green port with AHP

In view of the need to reduce the greenhouse gas emission causing global climate change coupled with the necessity to protect the environment around port vicinities, the green port concept receives considerable attention. This concept’s motivation is to balance environmental challenges with economic demands such that seaports improve their financial, commercial, and operational performances. In this regard, the use of renewable energy is one of the viable solutions. Based on the analytical hierarchy process (AHP) method, this paper presents a systematic approach for selecting the most suitable alternative of renewable energy sources. The results show that for the case of Kuala Tanjung Port, solar energy is the most favored alternative, followed by wind energy, biomass, and wave energy. It is hoped that this paper provides a starting point to plan for a long-term renewable energy strategy in the context of a green port.

Multi-Criteria Method for the Selection of Renewable Energy Sources in the Polish Industrial Sector

Rational decision-making requires assessing the advantages and disadvantages of options, including nonmarket effects (such as environmental effects). This also applies to strategic decision-making in the industrial sector to select alternative renewable energy source (RES). Often, a variety of criteria can be used to select a renewable energy source, whereas no ideal family of criteria for renewable energy selection for industry has been defined in the literature. It was concluded that there is a need to support the actions of industrial development based on RES, which will contribute significantly to overcoming the limitations of the negative effect on the environment in terms of greenhouse gas emissions. There is a clear need for a systematic and polyvalent multicriteria approach to planning in industry. Therefore, a method for choosing the preferred renewable source of electricity for industry has been developed, which considers key criteria of RES choice: Expert opinions, the costs of obtaining the energy and maintaining energy installations, and the volume of electricity from RES. This article offers a modified multicriteria selection method based on a fuzzy analytic hierarchy process (fuzzy AHP) and the technique for preference by similarity to an ideal solution (TOPSIS), integrated with a qualitative price analysis (ACJ). This new method was tested through a case study on selecting a preferred RES in Polish industrial conditions. The research results indicate that the proposed method of choosing the preferred renewable energy source can be used in industrial enterprises that strive to meet their energy needs in accordance with the principles of social responsibility.

Trigonometric Similarity Measures for Neutrosophic Hypersoft Sets With Application to Renewable Energy Source Selection

Cosine and cotangent similarity measurements are critical in applications for determining degrees of difference and similarity between objects. In the literature, numerous similarity measures for various extensions of fuzzy set, soft set, Intuitionistic Fuzzy Sets (IFSs), Pythagorean Fuzzy Sets (PFSs) and HyperSoft Sets (HSSs) have been explored. Neutrosophic HyperSoft Sets (NHSSs), on the other hand, has fewer cosine and cotangent similarity measures. In this paper, we propose the trigonometric similarity measures of NHSSs. We further investigate the basic operators, theorems, and propositions for the proposed similarity measures. We know that global warming causes environmental problems. One of applications for solving global warming is the concept of renewable energy. To show the effectiveness of the proposed similarity measures, we apply them to renewable energy source selection problems. The study reveals the best geographical area to install the energy production units, under some technical attributive factors. To check the validity and superiority of the proposed work, it is compared with some existing techniques which reveal that, decision-making problems with further bifurcated attributes, have more accurate and precise results and can only be solved with this technique. In the future, the proposed techniques can be applied to case studies, in which attributes are more than one and further bifurcated along with more than one decision-maker. Also, this proposed work can be extended for several existing hybrids of hypersoft sets, intuitionistic hypersoft, neutrosophic hypersoft set, bi-polar hypersoft, m-polar hypersoft sets, and Pythagorean hypersoft set to solve Multi-Criteria Decision Making (MCDM) problems.

Double-hierarchy hesitant fuzzy linguistic term set-based decision framework for multi-attribute group decision-making

With massive growth in decision-making theory, representation of preference information plays an indispensable role. To rationally handle uncertainty, scholars presented different ideas of which hesitant fuzzy linguistic term set (HFLTS) is a good choice to represent hesitancy in decision makers’ (DMs) preferences. The challenge with HFLTS is that it cannot be used for representing complex linguistic terms. To better circumvent this challenge, double-hierarchy HFLTS (DHHFLTS) is presented. Motivated by the power of DHHFLTS in expressing complex linguistic terms by using two linguistic hierarchies, a decision framework is proposed under the DHHFLTS context. Initially, the framework presents a new aggregation operator called simple double-hierarchy frequency match aggregation operator for sensible aggregation of DMs’ preference information. Later, the mathematical programming model is extended under the DHHLTS context for rational estimation of attribute weight with partially known information. Also, the popular Vise Kriterijumska Optimizacija Kompromisno Resenje ranking method is extended under the DHHFLTS context for the selection of a suitable object from the set of objects. Finally, the proposed decision framework is validated for its practicality by demonstrating two numerical examples viz., green supplier selection problem and renewable energy source selection problem. Also, the strengths and weaknesses of the proposed framework are realized by comparison with other methods.

Evaluating and Selecting Renewable Energy Sources for a Microgrid: A Bi-Capacity-Based Multi-Criteria Decision Making Approach

Renewable energy (RE) microgrids are considered one solution for solving the increasing electricity demand and environmental pollution problem. Selecting RE sources for a microgrid plays a crucial role in determining the performance of the microgrid. Illustrated with an empirical study on a city's microgrid project, this article proposes a new evaluation approach with a bi-capacity based multi-criteria decision making method called Bi-ELECTRE to evaluate and select RE source alternatives based on interacting performance indicators with bipolar measurement. With the novelty of the Bi-ELECTRE method, this article addresses the questions of (a) how to measure interactions between bipolar-scaled indicators when evaluating the RE source alternatives of a microgrid, and (b) how to determine the optimal combination of RE sources for a microgrid. The results from the empirical study suggest that a microgrid with a single RE source has the worst performance, compared with multi-source alternatives. In the empirical study conducted, the microgrid using a RE source combination of 20% wind, 30% solar, and 50% biomass has the best overall performance. By considering interactions between bipolar-scaled indicators and various combinations of RE sources, the proposed approach contributes to MCDM research methodologically and to the RE source selection problem for a microgrid practically.

Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach

This paper proposes an integrated decision-making framework for the systematic selection of a renewable energy source (RES) from a set of RESs based on sustainability attributes. A real case study of RES selection in Karnataka, India, using the framework is demonstrated, and the results are compared with state-of-the-art methods. The main reason for developing this framework is to handle uncertainty and vagueness effectively by reducing human intervention. Systematic selection of RESs also reduces inaccuracies and promotes rational decision-making. In this paper, q-rung orthopair fuzzy information is adopted to minimize subjective randomness by providing a flexible and generalized preference style. Further, the study found systematic approaches for imputing missing values, calculating attributes’ and decision-makers’ weights, aggregation or preferences, and prioritizing RESs, which are integrated into the framework. . Comparing the proposed framework with state-of-the-art-methods shows that (i) biomass and solar are suitable RESs for the process under consideration in Karnataka, (ii) the proposed framework is consistent with state-of-the-art methods, (iii) the proposed framework is sufficiently stable even after weights of attributes and decision makers are altered, and (iv) the proposed framework produces broad and sensible rank values for efficient backup management. These results validate the significance of the proposed framework.