Renewable Energy Resources Research Articles

A 2D VMD video image processing-based transfer learning approach for the detection and estimation of biofouling in tidal stream turbines

Harnessing the power of tidal streams is a sustainable way of exploiting renewable marine energy resources. It involves installing tidal stream turbines underwater to harness the energy. Nevertheless, these turbines are prone to the accumulation of biofouling, which significantly reduces their energy output and operational efficiency. It is therefore crucial to implement a condition-based monitoring system to detect biofouling promptly and ensure the continuous operation of a tidal stream turbine. In this context, this paper presents a data-centric approach that uses model submerged tidal stream turbine video images to detect and quantify biofouling. The relevance of a two-dimensional variational mode decomposition approach is investigated to extract relevant information from the potentially noisy collected images. While generative adversarial networks are used to address the data imbalance problem, a convolutional neural network is adopted to detect and assess the extent of biofouling. The performance of the proposed approach is assessed and validated using two experimental datasets obtained from the tidal stream turbine platforms of the Shanghai Maritime University and the Lehigh University.

Sustainable broiler chicken meat production: A three-layer model for emissions reduction and traceability

The world's major issues are climate change and global warming. The broiler chicken meat production industry plays a significant role in greenhouse gas emissions, which are responsible for global warming and climate change. The world urgently needs a new emission-free, eco-friendly, and sustainable meat production system. In this research, a three-layer broiler chicken meat production supply model is developed with greenhouse gas emissions reduction and an eco-friendly meat production system. The green technology investments and carbon tax policy are implemented to reduce greenhouse gas emissions from broiler chicken farmers and processors. The optimal cycle time, green investment, and number of shipments in the proposed model are computed in the Mathematica 9.0 software flatform by using the solution-finding algorithm. The concavity of the objective function is verified through two methods: mathematically, using the Hessian matrix concavity test, and graphically, by three-dimensional plotting of the objective function. The numerical examples, optimal solution table, results discussion, sensitivity analysis, and managerial implications are presented for the broiler chicken meat-producing industry. The emission cost of the processor and farmer are minimized by 5.29% and 15%, respectively, by the implementation of green technology investments. The AITP has increased by 1.01%. This study found that greenhouse gas emissions from the broiler chicken meat-producing industry can be significantly reduced by carbon tax implementation and utilizing renewable energy resources for energy requirements.

Improving environmental and economic sustainability of cutlery manufacturing in adeveloping nation through energy reduction and energy transition initiatives.

Manufacturing industries are vital for economic development, but they cause significant environmental damages. As there are scarce research studies for this industrial sector from developing countries, this article reports a comprehensive environmental and economic analyses for cutlery manufacturing in Pakistan. SimaPro 9.5 was used as a modelling software tool, while ReCiPe 2016 methods were used to evaluate various midpoint and endpoint environmental impacts. Various economic indicators were used to evaluate the economic performance of different alternative scenarios. The results revealed that injection molding process, due to its energy-intensive nature caused the most environmental impacts as compared to other manufacturing processes. Global warming and terrestrial ecotoxicity were the most affected impact categories with values of 11.8 kg CO2 eq and 12.0 kg 1,4-DCB, respectively. Meanwhile, at endpoint level, human health category was most damaged as compared to others. Based on technical process intervention and energy transition, four different alternative scenarios were developed. In comparison with baseline scenario, the alternative scenario with double-cavity mold resulted in a reduction of more than 30% for various impact categories. The other three alternatives were grounded on the use of solar energy (50% or 100%) and injection mold with double or single cavity. Overall, the alternative scenario with 50% solar energy and double-cavity mold was the best solution that showed more than 50% reduction in most of the impact categories, less than 3-year payback time, 2.12 million (Pakistani Rupees) net present value, and 36.3% of return on investment. This study clearly shows the importance of renewable energy resources and simple changes in process technology for improving sustainability performance. The relevant stakeholders can effectively use the results and methodology of this study as a reference and guide for future research and practical interventions, especially in developing countries.

Green Marketing, Business Sustainability and Marketing Practices of Selected Small and Medium Enterprises (SMEs) in Muntinlupa City

This descriptive-correlational study aimed to determine the green marketing, business sustainability, marketing practices and profitability of selected Small and Medium Enterprises (SMEs) in Muntinlupa City, Philippines and compare the significance of each variable in the context of business profitability. The findings of the study revealed that Small and Medium Enterprises (SMEs) in Muntinlupa City are engaged in green marketing initiatives and business sustainability practices while moderately involved in the whole gamut of marketing activities. SMEs in Muntinlupa City, are likewise earning a generous income making their business profitable. The results of the study showed that Small and Medium Enterprises (SMEs) engaged in green marketing initiatives using marketing strategies have a better chance of improving their business sustainability practices which can result in business profitability. Further results revealed that promotion of products and services, engaging in green technology, social approval and support, using renewable energy and recyclable resources were significant predictors of business profitability. Three hundred one (301) Small and Medium Enterprises (SMEs) from Muntinlupa City have completed the survey. The study calls for Small and Medium Enterprises (SMEs) to continuously adapt marketing strategies and engage in green marketing initiatives and sustainability business practices that contribute to long-term business profitability. It was noteworthy to mention that green marketing initiatives and business sustainability practices are becoming a norm among Small and Medium Enterprises (SMEs) in Muntinlupa City not only in contributing to the economic development of the city at the same time, preserving the environment for future generations of entrepreneurs.

Design and Manufacturing of Thermosyphon Solar Water Heater Using Locally Available Materials in Nsanke Village

The weather conditions of Nsanke village starting from April to October emphasis the need for judicious utilization of warm water. We designed, constructed and tested a Solar Water Heater based on Thermosyphon principle in Nsanke village using locally available materials. Solar energy is received by two flat-plate collectors of 1.68 m2 each, consisting of absorber plate carrying copper tubes and placed in an insulated casing with a transparent glass cover of thickness 4 mm. The collectors are connected in series and are assumed to operate on the same efficiency. The resultant collector is connected to a storage tank, which is an association of two barrel nested overall and separated with an insulator. The radiation emitted by the absorber plates cannot escape through the glass, thus increasing its temperature. The system’s performance evaluation has shown that it can heat water from 23°C to at least 51°C on sunny days. The water heated flows into a storage tank through Thermosyphon principle. The Solar Water Heating System uses the Thermosyphon principle and can therefore be called Thermosyphon Solar Water Heater (TSWH) for simplicity. This system finds useful application in serving as a renewable energy resource in Nsanke village and neighboring villages.

Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models

This paper presents a geospatial artificial intelligence (GeoAI) approach for generating wind power projection maps employing various Machine Learning (ML) models. These models include Artificial Neural Network (ANN), Decision Tree (DT), Gaussian Process Regression (GPR), and Support Vector Regression (SVR), which collectively aim to provide insightful wind power forecasts under the effects of climate change. The framework considers different influential parameters affecting wind speed, including pressure gradient, temperature gradient, humidity, and topography. The study’s geographic focus is Cork City, Ireland. The investigation covers a historical period from 2000 to 2014 and extends to encompass two future climate scenarios, between 2015 and 2050. A comprehensive set of statistical skill scores is computed to gauge the models’ performance. The study’s findings underscore the efficacy of the ML models in generating dependable estimates of wind power fluctuations. Notably, the SVR model emerges as the frontrunner in performance across most pixels examined. Despite the inherent complexity of wind power dynamics, this research highlights that the SVR model can produce accurate wind power maps, even when operating with limited input data. The results emphasize the importance of considering influential factors in wind speed projections. This approach opens up promising avenues for improving the management of renewable energy resources.

Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

Techno-Economic Analysis of Combined Production of Wind Energy and Green Hydrogen on the Northern Coast of Mauritania

Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H2. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania’s north coast has potential for wind energy, aiding green hydrogen production for energy goals.

Analysis of renewable energy resources based on frank power aggregation operators and EDAS method for circular bipolar complex fuzzy uncertainty

The model of circular bipolar complex fuzzy (Cir-BCF) sets computed based on the membership function, non-membership function, and radius among both functions for each value of the universal set. The technique of the Cir-BCF set is the modified or extended form of fuzzy sets, complex fuzzy sets, bipolar fuzzy sets, bipolar complex fuzzy sets, and simple circular bipolar fuzzy sets to cope with uncertain and vague information. In this manuscript, we describe the novel technique of frank operational laws based on Cir-BCF values for frank t-norm and frank t-conorm. Further, we simplify the model of Cir-BCF frank power averaging (Cir-BCFFPA), Cir-BCF frank power weighted averaging (Cir-BCFFPWA), Cir-BCF frank power geometric (Cir-BCFFPG), Cir-BCF frank power weighted geometric (Cir-BCFFPWG) operators, and highlighted their valuable properties, called idempotency, monotonicity, and boundedness. Moreover, analysis of renewable energy resources is very awkward and complicated, but it is natural sources of energy that are refilled constantly or relativeness quickly on a human timescale. Further, solar energy, hydroelectric energy, geothermal energy, biomass energy, and wind energy are five major sources of energy, but it is complex to find which one is the best and which one is worst. For evaluating the above problems, we construct the technique of evaluation based on the distance from the average solution (EDAS) method under the presence of the Cir-BCF numbers (Cir-BCFNs). At the end, we arrange the comparison between proposed and existing techniques based on some numerical examples to describe the validity and proficiency of the initiated information.

Deep learning analysis of green ammonia synthesis: Evaluating techno-economic feasibility for sustainable production

This research presents a comprehensive optimization of green ammonia synthesis in Morocco by leveraging advanced deep learning techniques to maximize the utilization of the country's abundant renewable energy resources. By employing deep learning models such as LSTM and LSTM_adv, we forecast ammonia production over the next decade, improving strategies for production and energy storage. Our findings confirmed the viability of sustainable ammonia production in Dakhla and highlighted its transformative potential for global sustainability goals. Under the optimal scenario of 20 % photovoltaic and 80 % wind energy, the production cost was US$575 per tNH3, delivering an energy output of 8916.64 GWh and a daily ammonia production of 2503.36 tNH3. Shifting to 100 % wind energy further enhances the potential, increasing daily ammonia production to a maximum of 3090 tNH3 while reducing the production cost to US$376 per tNH3. These results demonstrate the significant economic and environmental benefits of using renewable energy sources for ammonia production in Morocco. By leveraging a country's abundant wind and solar resources, we can contribute to a sustainable and energy-independent future.

A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources

A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources

The effect of geopolitical risk on the clean energy metals’ prices: the evidence of BRICS countries

ABSTRACT This study examines the effect of geopolitical risk on clean energy metal prices for Brazil, Russia, India, China and South Africa (BRICS) countries. Aluminum, cobalt, copper, nickel and zinc have been used as clean energy metals. The research period was determined as January 1990 to August 2021. Hatemi-J and Roca (2014) asymmetric causality test was used as the research method. According to the results, there is no causality between geopolitical risk of South Africa and cobalt prices for different cases. However, in general, there is causality between other clean energy metal prices and geopolitical risk in BRICS countries. Accordingly, the geopolitical risks of the BRICS countries both affect and are affected by these metal prices. This result suggests that changes in geopolitical risk of BRICS countries can provide useful information in explaining changes in clean energy metal prices. Likewise, changes in clean energy metal prices provide useful information in explaining the changes in geopolitical risk of BRICS countries. Therefore, policy makers need to develop new policies and strategies that can protect BRICS countries from clean energy metal price fluctuations in order to effectively use their renewable energy resources in their economic development.

A model fitting approach for the investigation of thermo-kinetic parameters of rice straw: a viable renewable energy resources in Bangladesh

This study investigates the potential of renewable energy sources to address the growing energy demands of developing nations while ensuring environmental sustainability. Here, a model-based analysis to assess the feasibility of utilizing rice straw pyrolysis as a renewable energy source is investigated. The thermogravimetric analysis identified the optimal temperature range for pyrolysis (230–400 °C) and the Coats–Redfern model was employed for kinetic analysis. Diffusion models, particularly D3, exhibited the best fit with an activation energy of 16–18 kJ/mol. Thermodynamic analysis confirmed the endothermic nature of the process, with positive enthalpy and negative entropy changes indicating an increase in energy and a more ordered product. These findings suggest that rice straw pyrolysis holds promise as a viable renewable energy option. This study contributes to understanding the rice straw pyrolysis mechanism which could be helpful for its effective utilization at commercial scale.

A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Accurate wind power forecasting is pivotal in facilitating the efficient integration of renewable energy sources into existing power grids. This study proposes a novel hybrid framework, termed BWO-BiLSTM-ATT, which synergistically combines the strengths of a bidirectional long short-term memory (BiLSTM) network, a self-attention mechanism, and the Beluga Whale Optimization (BWO) algorithm. The BiLSTM architecture, with its unique ability to capture bidirectional temporal dependencies, effectively models the intricate dynamics present in wind power time series data. Integrating the self-attention mechanism further enhances the model's performance by discerning and emphasizing the most salient features within the input data. Furthermore, employing the BWO algorithm optimizes the model's hyperparameters, ensuring optimal configuration and enhancing its predictive accuracy and generalization capabilities. The proposed BWO-BiLSTM-ATT framework was rigorously evaluated using real-world data from an offshore wind farm in Yangjiang City, China. Comparative analyses were conducted against several baseline algorithms, including persistence, ARIMA, SVR, and vanilla LSTM models. The results demonstrate the superior performance of the BWO-BiLSTM-ATT framework, achieving higher R-squared and explained variance scores, coupled with lower error metrics such as MSE, RMSE, MedAE, and MAE. Statistical significance tests further corroborated the substantial performance improvements offered by the proposed framework. The combination of advanced deep learning techniques and metaheuristic optimization algorithms embedded in the BWO-BiLSTM-ATT framework provides a robust and accurate solution for wind power forecasting, enabling efficient management and seamless integration of renewable energy resources into existing power grids.

Research constituent, intellectual structure and current trends in environmental sustainability-an analytical retrospective

Climate change is a paramount problem for humanity, representing a substantial danger to all living organisms. Industrialization, a vital factor for economic progress, has resulted in global warming, posing a threat to the long-term viability of our ecosystem. Currently, a wide range of techniques and technologies are being used to guarantee the preservation of the environment for future generations. This study employed data from the Scopus database to do topic modeling. Authors used latent Dirichlet allocation to extract research themes related to environmental sustainability from a corpus of 4023 research articles published between 1976 and 2022. By utilizing clustering methodologies to analyze the collection of words, Authors successfully forecasted two, five, and ten study subjects, emphasizing specific domains that necessitate additional investigation by scholars. Based on coherence ratings, five subjects have been identified as prospective study areas requiring further scientific exploration. The results of our research emphasize the significance of incorporating environmentally-friendly technologies in different industries to promote a long-lasting and eco-friendly ecosystem. In addition, authors recommend prioritizing implementing sustainable and environmentally friendly technologies, improving the management of ecosystems, encouraging water conservation, promoting agricultural advancements, and advancing renewable energy resources as crucial strategies for protecting the environment and enhancing ecological conditions. This analysis illuminates current research trends in environmental sustainability and potential pathways for future investigation and intervention.

Harnessing solar energy for sustainable green hydrogen production in Türkiye: Opportunities, and economic viability

Hydrogen emerges as a promising solution for Türkiye, offering opportunities to enhance renewable energy production and reduce greenhouse gas emissions. Türkiye has significant potential for green hydrogen production, leveraging its abundant renewable energy resources and lower installation costs for renewable energy-based power plants.This study assesses the impact of capital expenditure (CAPEX) on the Levelized Cost of Hydrogen (LCOH) in Türkiye, focusing on both current and projected costs. The LCOH ranges from $3.79/kgH2 to $5.11/kgH2 for low and high CAPEX scenarios, given Türkiye's solar electricity cost of $40.32/MWh in 2024. Looking ahead to 2035, achieving Türkiye's hydrogen cost target of $2.4/kgH2 will require reducing electricity costs to $31/MWh for low CAPEX and $15.3/MWh for high CAPEX. The current electricity cost of $40.3/MWh is significantly above these targets, highlighting the need for substantial technological advancements and cost-reduction strategies to meet future economic objectives for sustainable hydrogen production.

Optimal design and three-level stochastic energy management for an interconnected microgrid with hydrogen production and storage for fuel cell electric vehicle refueling stations

A new trend in the transportation sector globally can be observed in a shift away from gasoline-powered vehicles to Hydrogen-based fuel-cell electric vehicles (FCEVs), aimed at reducing harmful emissions. However, there are challenges in producing hydrogen for vehicle stations related to microgrid design and energy management under uncertain conditions. This research sought to identify the optimum design of an electric microgrid to provide the required energy for electric loads, together with a hydrogen refueling station. The microgrid under study consists of various renewable energy resources (RERs), such as photovoltaic (PV) devices, wind power systems, and hydrogen storage systems. The energy management strategy (EMS) aims to reduce the total costs (investment, operation, replacement, procurement energy costs) considering four uncertain parameters associated with PV panels, FCEVs, wind turbines, and power demand. A three-level EMS is proposed based on testing various solutions: without RERs or a hydrogen energy storage system (Level 1); with RERs and a hydrogen energy storage system (Level 2), with RERs and hydrogen energy storage that includes demand side response (DSR) (Level 3). The results indicate annual cost savings of 1.946 E+06 $ for Level 2 and 2.001 E+06 $ for Level 3, compared to Level 1.

Technology Gaps for Monitoring Birds and Marine Mammals at Offshore Wind Facilities

Abstract With increased focus on offshore wind (OSW) as a renewable energy resource in the United States and elsewhere, there are concerns about OSW impacts to wildlife, particularly birds and marine mammals. This study identifies technology gaps and technological research and development (R&D) priorities for monitoring marine mammals and birds for fixed and floating OSW. A synthesis of current monitoring technologies generated two databases (with over 100 technologies) that can be integrated in current technology repositories for renewable energy projects. Generally, the key technology R&D needs are similar for birds and marine mammals. The main exception is that some types of bird technologies are more likely to require direct integration with OSW infrastructure, whereas marine mammal systems tend to operate independently. Priorities to advance wildlife monitoring include improved early communication, harmonization of technologies and data collection for monitoring systems on OSW structures, battery/power access improvements, remote data transfer improvements, and advancements in automated collection and analysis of data. The successful integration of wildlife monitoring systems into OSW infrastructure and operations is dependent on remote access mechanisms for data collection, system maintenance, and data transfer, in order to minimize risks to worker safety in the offshore environment, as well as minimizing costs and disruption to normal operational activities. Application of the results of this study to prioritize and fund technology R&D will help to support statistically robust data collection and practicable integration of monitoring systems into OSW operations and infrastructure.

Fabrication of nickel oxide decorated CNTs/GO nanohybrid: A multifunctional electrocatalyst for overall electrochemical water splitting

Hydrogen production from water as renewable energy resource is vital to fulfil the huge energy demands without any hazardous environmental impact. Pursuing the efficient, durable and economical electrocatalyst other than benchmark expensive materials such as Pt, Ru, and Ir, for water electrolysis is a big challenge to produce the hydrogen as clean fuels. Here, we have successfully decorated nickel oxides nanoparticles over the carbon nanotubes covered by the graphene oxide layers (GO/NiO@CNTs/GO) using a facile hydrothermal method and utilized as electrocatalyst for electrochemical water splitting. The surface morphology and structure was assessed using a variety of analytical techniques, including scanning electron microscopy (SEM), energy dispersive X-rays spectroscopy (EDX) and X-ray diffraction (XRD). As prepared nanohybrid (GO/NiO@CNTs/GO) was utilized as multifunctional electrocatalyst to investigate the water electrolysis potential via different electrochemical techniques including linear sweep voltammetry (LSV), and cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry. The fabricated electrode exhibited a lower overpotential of 236 mV and 208 mV at the standard current density of 10 mAcm−2 under alkaline and acidic conditions, respectively. Enhanced double layer capacitance (Cdl) and reduced charge transfer resistance (Rct) also showed the boosted performance for the hybrid materials with long term stability. The carbon based nanohybrid (GO/NiO@CNTs/GO) showed the promising potential having multifunctional characteristics including oxygen and hydrogen evolution reactions along with overall electrochemical water splitting.

A Bibliometric Review to Understanding the Supply Chain of Renewable Energy

This bibliometric analysis investigates the evolving research landscape in renewable energy supply chains. The study uses VOSviewer software to examine influential articles, authors, countries, journals, and keywords from 2015 to 2023. Notably, 2019 marked a significant shift with a surge in publications and citations, suggesting a transition toward more impactful and higher-quality research. In subsequent years, they have witnessed fluctuations in citation counts, potentially reflecting changes in research focus and publication quality. Geographically, the United States and China stand out as leading contributors, with the United Kingdom, Germany, Italy, and Iran also making substantial contributions. The analysis of journals and publishers reveals variations in research impact and influence, with Elsevier emerging as a significant publisher. The keyword analysis underscores the prevalence of "Supply chains," "renewable energy resources," and "biomass" as central themes. This study offers valuable insights into the research trends and dynamics in the renewable energy supply chain field.