Renewable and Sustainable Energy Reviews最新文献

This study presents a comprehensive review of state-of-the-art energy systems and spatially explicit modelling approaches aimed at identifying approaches suitable for planning hybrid renewable energy systems integration in rural areas of developing countries. A systematic comparative approach based on an analytical framework is proposed with defined criteria to identify potential tools, evaluate their modelling attributes and select suitable tools. Out of 27 potentially identified suitable models, 10 models that fulfilled key filtering criteria were studied in more detail. Although differences in capabilities were identified, two conventional models (HOMER, iHOGA) and three geographical information system (GIS)-based models (GeoSIM, IntiGIS, and OnSSET) show the most potential for supporting HRES integration in rural areas, despite presenting significant gaps. Conventional models especially lack detailed network analysis capacities but have the potentials to model different hybrid technologies. Alternatively, GIS-based models show more capacity to incorporate spatially sensitive data including network analyses, but show technological and capacity limitations for addressing future trends. In response, this study presents a modelling framework strategy for upgrading individual models and using them complementary. While being open source-based models and scoring well on the proposed assessment criteria, OnSSET and IntiGIS show the highest potential for use in developing countries. In addition, HOMER and iHOGA as well-established models show high scores and are notably useful if complemented with capabilities for analysing spatial parameters. The proposed methodological framework and strategy steps will provide researchers and practitioners with a solid basis for selecting and/or developing modelling tools for application and assessment.

This review investigates the two-stage anaerobic digestion (TSAD) of organic waste to produce high-value products according to circular economy principles. The novelty of the study is the coupling of energy-carrying production including H₂ and CH₄, with the digestate treatments through closed-loop valorization.

The review's key findings highlight that in TSAD, the energy and digestate qualities can be improved through pre-treatments and co-digestion. Pre-treatments allow for the increase of soluble organic matter available for microorganisms. Co-digestion controls the optimal ranges of carbon-nitrogen ratios, nutrient balances, pH, and water contents, by increasing the TSAD efficiency without consuming resources like water or mineral nutrients. Digestate management is investigated for the whole, liquid, and solid fraction of the residue demonstrating a high yield in nutrient recovery and char production.

In this study, TSAD is not only considered a biochemical process but a sequential biorefinery that requires optimization to be scaled up at full scale.

The bottlenecks of TSAD emerged in the explorative energetic, environmental, and economic assessments, which point out the significant economic and environmental costs associated with transporting, storing, pre-treating biomasses, and dewatering and managing digestate.

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Failure to mitigate thermal runaway (TR) early can result in TR propagation due to cell-to-cell heat interactions, resulting in module/pack fires. In this study, a comparative investigation is conducted on the TR cell position prediction performance by different machine learning (ML) algorithms for 32-cell cylindrical air-cooled LiB modules in aligned, staggered, and cross arrangement cells. The ML models are trained on temperature data from the sensors optimized using the Pearson Correlation Coefficient approach with a threshold of 0.85. The air temperature data in the battery module under different operating and faulty conditions were generated from experimentally validated numerical models and recorded by the sensors initially dispersed in single mid-plane and multiple arbitrary planes of the battery domain. The base ML algorithms chosen for the study comprise the k-Nearest Neighbors, Random forest, Gradient boosting, and Long short-term memory classification algorithms. The developed models are further subjected to 5-fold cross-validation and external testing using random test cases and subsequently compared for prediction accuracy with the error metrics, training, and prediction times. A Stacked Ensemble learning model is built and tested for accuracy based on the base models to improve the overall predictive accuracy. The study concludes that the RF model outperformed other base models owing to its 100% accuracy across all cell arrangements, high consistency across validation folds, and the lowest training and prediction time of 22 s and 0.59 s, respectively. The study identifies the best-fit ML model for early fault detection and preventing catastrophic accidents.

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods for LIBs in advanced BMSs. This paper provides a comprehensive review on these methods. Different from the existing reviews focusing on the minute details of the methods, this review systematically explores the model-based fault diagnosis framework along with an in-depth examination of its critical components. Based on a general state-space battery model, the study elaborates on the formulation of state vectors, the identification of model parameters, the analysis of fault mechanisms, and the evaluation of modeling uncertainties. Following this foundational work, various state observers and their algorithm implementations are designed for fault diagnosis, with a focus on design characteristics, the importance of selecting appropriate observers for specific applications, and highlighting the advantages and limitations of different fault diagnosis methods in practical applications. Finally, the paper discusses the challenges and outlook in model-based fault diagnosis methods, envisioning their possible future research directions.

The research on the dynamics analysis-based energy-saving technology is significant to reduce ship energy consumption and greenhouse gas emissions. The adoption of dynamics analysis theory and Computational Fluid Dynamics (CFD) approaches can achieve the optimal design and energy efficiency improvement of the ship. This research focuses on the ship energy efficiency improvement technology through CFD-based dynamics analysis, including the hull optimization design, drag reduction technology, navigation state optimization, efficient propulsion devices, energy-saving equipment, and the coupled dynamics analysis for comprehensive performance optimization. The current research and application status of ship performance optimization based on CFD approaches for energy-efficient shipping are systematically analyzed. On this basis, the challenges and problems in the application of the CFD-based energy-saving technology are discussed, and the future research works are proposed, aiming to provide references for the development of ship energy-saving technology based on CFD approaches. The analysis results show that the adoption of CFD-based dynamics analysis methods can effectively optimize the ship dynamics performance, thus reducing ship energy consumption and pollution gas emissions. In the future, the CFD-based coupled dynamics analysis should be further studied to achieve the overall performance optimization of the integrated ship-engine-propeller-appendages system under the influence of multiple complex factors, to continuously improve the ship energy efficiency, thus promoting the low-carbon development of the shipping industry.

The extensive construction of high-speed rail (HSR) has led to considerable embodied carbon emissions, threatening carbon reduction targets and necessitating effective governance. Previous studies have focused on the carbon accounting of HSR, providing only pre-evaluation information for management. To measure the carbon emission level of HSR and guide improvements, it is essential to establish quantified carbon emission benchmarks as “yardsticks” for decision-making comparisons. Hence, this study proposes a data-driven method for determining the embodied carbon emission benchmarks of the HSR system, involving dataset construction, carbon accounting, statistical analysis, validation, and uncertainty assessment. Based on a dataset comprising 1226 HSR subprojects in China, a classified and graded HSR embodied carbon emission benchmark system is constructed, and the underlying causes for the emission differences among categories of HSR subsystems are explored. In addition to providing various benchmark values, the research results also demonstrate the decarbonization potential of advanced HSR construction technologies such as ballastless tracks, long-span box girders, and shield tunneling methods. Overall, this study can provide a decision basis for evaluating the HSR emission performance and offer insights into the selection of emission reduction technologies, thereby facilitating the low-carbon sustainable development of HSR construction.

Carbon Capture and Storage (CCS) technology can effectively reduce carbon dioxide emissions from industrial and energy production processes. Yet the commercialization of CCS technology is hampered by financial requirements. Existing research compares the costs and benefits of business models for individual CCS projects, no studies assess the industry and national levels. Here we use a dynamic computable general equilibrium model, constructure the vertical integration and joint venture business models for CCS. This study assesses the emission and economic impacts of joint ventures between China's high-emission industries and their upstream and downstream sectors. We find that adopting the joint venture model in the coal power sector, compared to the vertical integration model, initially exerts a negative economic impact but can mitigate 0.04 % of GDP by 2060. The chemical industry consistently benefits from the joint venture model both economically and in emission reductions. Downstream sectors of the steel and cement industries are unsuitable for participation in joint ventures. In addition, the joint venture model has significant industry linkage effects, affecting energy consumption, the scale and cost of CCS deployment. This work provides important information for the large-scale commercialization of CCS technology.

Biodiesel, a biodegradable and non-toxic diesel alternative from the esterification and transesterification processes, is a viable bioenergy option. However, the presence of impurities in crude biodiesel necessitates purification to meet the standard specifications for high engine performance. Several techniques for biodiesel purification have been proposed, and they can be conveniently categorized based on the following: equilibrium, affinity, membrane, and reaction. Dry-washing methods have proven to be effective, though they generate a significant amount of waste. Additionally, water-based purification methods generate a large volume of wastewater with adverse environmental effects. The oxygen content in biodiesel has led to problems such as moisture absorption, corrosion and high viscosity. More recent research works focusing on membrane-based biodiesel purification techniques appear to be a promising alternative that offers high-quality fuel with lower volumes of wastewater discharges. This paper reviews the conventional and emerging liquid membrane technologies such as the emulsion liquid membranes and the bulk liquid membranes for biodiesel purification, highlighting their technical merits and environmental benefits. Over and above that, liquid membrane technology offers a promising solution for the efficient purification of crude biodiesel while minimizing the environmental impacts.

Mean radiant temperature (MRT) has attracted growing interest over the decades both indoors and outdoors, leading to the development of various measurement techniques and technologies. This review provides a comprehensive technical analysis of current MRT measurement methods and identifies future technologies required for effective monitoring in occupied buildings. Current sensors face notable limitations, including accuracy issues, calibration challenges and high costs associated with remote sensing techniques. While infrared sensors offer advantages in building applications, their metrological performance needs systematic validation. An analysis of 94 cases in investigated studies reveals that MRT shows significant temperature differences compared to air temperature (up to 36.8 °C) and varies distinctly to solar radiation, convection, and radiant system controls. These findings highlight the critical role of precise MRT monitoring for optimal thermal control in buildings. Recent advancements have led to the development of prototype infrared sensors for real-time application; however, challenges in device installation and continuous monitoring persist. Addressing these challenges is crucial for improving the accuracy and feasibility of MRT monitoring, ultimately enhancing thermal comfort management in occupied building environments. This review underscores the potential impact of advanced MRT monitoring technologies on building environmental control and occupant comfort.