Renewable and Sustainable Energy Reviews最新文献

The decomposition of global horizontal irradiance into its direct and diffuse components is critical in many applications. To guarantee accurate results in practice, the existing separation techniques need to be validated against reference ground measurements from a variety of stations. Here, four versions of the recent GISPLIT model are compared to a strong benchmark constituted from nine leading models of the literature. The validation database includes ≈24 million data points and is constituted of one calendar year of 1-min high-quality data from 118 research-class world stations covering all continents and all five major Köppen-Geiger climates. The results are analyzed with various statistical metrics to be as generalizable and explicative as possible. It is found that even the simpler GISPLIT version reduces the mean site RMSE of the best benchmark model by ≈11 % for the direct component and ≈17 % for the diffuse component. The improvement reaches ≈17 % and ≈25 %, respectively, when using the best GISPLIT version. The improvements are more important in cases of highly variable sky cloudiness, per the CAELUS sky classification scheme. A ranking analysis shows that all four versions of GISPLIT ranked higher than the benchmark models, and that the use of machine learning significantly improves the separation performance. In contrast, only marginal improvements are obtained through preliminary conditioning by Köppen-Geiger climate class. Overall, it is concluded that GISPLITv3, which is not dependent on climate class but makes use of machine learning for the most challenging sky conditions, can be asserted as the new high-performance quasi-universal separation model.

Accurate and efficient characterisation techniques are essential to ensure the optimal performance and reliability of photovoltaic devices, especially given the large number of silicon solar cells produced each day. To unlock valuable insights from the amount of data generated during the characterisation process, researchers have increasingly turned to different machine learning (ML) techniques. In this review, advances in ML applications for silicon photovoltaic (PV) characterisation from 2018 to 2023, including device investigation, process optimisation, and manufacturing line assessment are examined. Additionally, studies on deep learning techniques for luminescence-based measurements, such as defect classification, detection, and segmentation, which can help manufacturers identify potential reliability issues are explored. Despite the abundance of ML applications, it is emphasised that the lack of both publicly available datasets and the uniform use of ML metrics poses a significant challenge for researchers to benchmark their frameworks and achieve consistent and accurate results. In advancing ML applications in PV, future research should focus on improving model interpretability, balancing speed and accuracy, understanding computational demands, and integrating niche applications into a unified framework. Lastly, industry involvement and interdisciplinary collaboration among experts in solar energy, data science, and engineering are vital in tailoring ML solutions and enhancing innovation in addressing various challenges in the PV field.

To facilitate a successful integration of distributed energy resources into the electricity generation mix, new forms of energy markets must be considered. Concepts such as Peer-to-peer energy trading (P2P), transactive energy (TE) and community/collective self-consumption (CSC) are frequently mentioned as solutions to this challenge. Despite increasing interest from industry, policy, and academia, the field lacks a shared understanding of this class of models. This need is addressed by presenting sets of shared and distinct characteristics which define P2P, TE and CSC. Our analysis is based on a series of expert group interviews with regulators, industry, and academics across 13 countries, and a systematic and targeted literature review of 133 papers. Findings show that P2P/TE/CSC models can be described as sub-markets that operate within or alongside traditional energy markets and enable trading or sharing of energy using an automated approach. They focus on promoting and supporting local energy generation and consumption using price negotiation mechanisms that reflect the aims of the market. The paper also presents sets of characteristics which differentiate P2P, TE, and CSC from one another and sets out guiding definitions to be used as a reference point. The main differences between these models stem from the goal they are trying to achieve and the contexts they are deployed in. Findings from this analysis can support development of a shared understanding of this class of models across multiple disciplinary perspectives and applications.

Eco-labels are one potential tool to facilitate communication between producers and consumers and to promote environmental and social policies. The main objective of this study is to investigate the successes and limitations of eco-labels through a comprehensive review of the academic literature and the label programs themselves, and to discuss the potential and directions for future academic research and eco-labels. The initial literature review examined the definition, characteristics, objectives, successes, and challenges of eco-labels and identified essential elements of successful labeling, such as consumer awareness and acceptance. The subsequent review of label programs examined the characteristics and trends of 456 label programs in 199 countries based on a large eco-label database. Several additional analyses comprehensively synthesized the results of these two studies and provided specific suggestions for future academic research and label programs. In conclusion, at this time there is limited evidence that eco-labels can serve as effective communication and policy tools. However, there also remain significant improvement opportunities for many label programs to realize their potential.

Dimethyl carbonate, a pivotal organic solvent, has experienced significant growth in consumption and an expansion of production capacity in China in recent years. The primary industrial production methods, including transesterification, carbonylation, and urea alcoholysis, are accompanied by dedicated production facilities. This study conducts a comparative assessment of these processes, scrutinizing their technical merits and associated challenges to provide strategic guidance for dimethyl carbonate production within the nation. The review provides a comprehensive summary of dimethyl carbonate synthesis methods. Focusing on the separation of azeotropes during dimethyl carbonate synthesis via transesterification, it suggests the potential integration of conventional energy-saving technology with pervaporation separation to separate dimethyl carbonate and methanol. The review culminates in a concise summary and analysis of forthcoming prospects and obstacles inherent to this hybrid strategy. Realizing the effective integration of pervaporation technology with established energy-saving techniques for the efficient and ecologically sustainable separation necessitates further exploration and practical implementation.

Design

Design of metal hydride-based hydrogen storage reactors is often performed using numerical/experimental modelling which is computationally/economically difficult. This paper investigates the applicability of Response Surface Methodology (RSM) coupled Local/Global Sensitivity Analysis (L/GSA) to investigate – i) the applicability of advanced RSMs in predicting the responses for storage systems efficiently, ii) the applicability of advanced RSMs to perform L/GSA to identify the sensitive input design parameters based on their effect on the Outputs of Interests (OIs), i.e., reaction fraction (i.e., $C$) and bed temperature (i.e., $T$), and iii) the dependence of importance ranking of design parameters on the employed L/GSA methodology. The study is conducted in two stages. In the first stage, the most accurate RSM was identified among fourteen traditional and advanced RSMs, i.e., radial basis, kriging, quadratic, moving least square, support vector machine etc., employing a measure of precision, i.e., Nash–Sutcliffe Efficiency (NSE). RSMs were constructed based on the values of OIs estimated using finite element simulation using COMSOL software for random realizations of inputs generated via Latin Hypercube Sampling (LHS). In the second stage, the importance ranking of design parameters was estimated for both OIs using six different L/GSAs based on the input-output relationships estimated in stage one. All the codes of RSMs and L/GSAs were written and validated in MATLAB. Finite element simulations of the random realizations were performed using COMSOL software. For the present study, NSEs of the considered RSMs were ranging between 0.6262-0.8544 and 0.4652–0.8081 for $C$ and $T$ respectively, indicating the importance of selection of appropriate RSM. RBF-augmented Compact-I and kriging were the most accurate RSMs with NSEs approximately 10%–20 % higher to those of frequently used polynomial RSM. Time ($t$) and mass of hydrogen to be stored ($M_H$) were the most; and external temperature ($T_{ext}$) and porosity ($E$) were the least sensitive inputs corresponding to $C$ and $T$, with differences of 80–90 % in the sensitivity indices respectively. The ranking prediction was highly dependent upon the employed L/GSA methodology, with Morris's screening observed to be the least accurate. The RSM methods described in this study help to design and investigate the metal hydride reactors for various a