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Rong, Z., Ding, J., Lu, J., Wang, W. & Yan, J. (2022). Experimental and theoretical investigation of an innovative composite nanofluid for solar energy photothermal conversion and storage. Journal of Energy Storage, 52, 104800, Article ID 104800.
Open this publication in new window or tab >>Experimental and theoretical investigation of an innovative composite nanofluid for solar energy photothermal conversion and storage
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2022 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 52, p. 104800-, article id 104800Article in journal (Refereed) Published
Abstract [en]

Molten salts play a key role in the heat transfer and thermal energy storage processes of concentrated solar power plants. A novel composite material was prepared in this work by adding micron-sized magnesium particles into Li2CO3-Na2CO3-K2CO3 molten salt, the heat transfer and thermal energy storage properties of the composites were studied experimentally. A stable composite nanofluid can be obtained, and a thermal conductivity of 0.728 W/(m.K) at 973 K with an enhancement of 31% is achieved for the Mg/molten carbonate nanofluid. And the strengthening mechanism of thermal conductivity was revealed by using ab-initio molecular dynamics method. It is found that the main bonding interactions exist between Mg and O atoms at the surface of Mg particles. A compressed ion layer with a more compact and ordered ionic structure is formed around Mg particles, and the Brownian motions of Mg particles lead to the micro-convections of carbonate ions around them. These factors are helpful to the enhancement of thermal conduction with the improved probability and frequency of ion collisions. This work can provide a guidance for further studies and applications on metal/molten salt composites with enhanced heat transfer and thermal energy storage capacity.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Mg/molten carbonate nanofluid, Ab-initio molecular dynamics, Compressed ion layer, Thermal conductivity, Heat transfer and storage
National Category
Materials Chemistry Energy Systems
Identifiers
urn:nbn:se:kth:diva-316444 (URN)10.1016/j.est.2022.104800 (DOI)000832874000004 ()2-s2.0-85129606304 (Scopus ID)
Note

QC 20220818

Available from: 2022-08-18 Created: 2022-08-18 Last updated: 2023-08-28Bibliographically approved
Xu, N., Kong, Y., Yan, J., Zhang, Y., Sui, Y., Ju, H., . . . Xu, Z. (2022). Global optimization energy management for multi-energy source vehicles based on "Information layer - Physical layer - Energy layer- Dynamic programming" (IPE-DP). Applied Energy, 312, Article ID 118668.
Open this publication in new window or tab >>Global optimization energy management for multi-energy source vehicles based on "Information layer - Physical layer - Energy layer- Dynamic programming" (IPE-DP)
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2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 312, article id 118668Article in journal (Refereed) Published
Abstract [en]

To reveal the energy-saving mechanisms of global energy management, we propose a global optimization framework of "information layer-physical layer-energy layer-dynamic programming " (IPE-DP), which can realize the unity of different information scenarios, different vehicle configurations and energy conversions. The deterministic dynamic programing (DP) and adaptive dynamic programming (ADP) are taken as the core algorithms. As a benchmark for assessing the optimality, DP strategy has four main challenges: standardization, real-time application, accuracy, and satisfactory drivability. To solve the above problems, the IPE-DP optimization framework is established, which consists of three main layers, two interface layers and an application layer. To be specific, the full-factor trip information is acquired from three scenarios in the information layer, and then the feasible work modes of the vehicle are determined in the physical layer based on the proposed conservation framework of "kinetic/potential energy & onboard energy ". The above lays a foundation for the optimal energy distribution in the energy layer. Then, a global domain-searching algorithm and action dependent heuristic dynamic programming (ADHDP) model are developed for different information acquisition scenarios to obtain the optimal solution. To improve the computational efficiency under the deterministic information, a fast DP is developed based on the statistical rules of DP behavior, the core of which is to restrict the exploring region based on a reference SOC trajectory. Regarding the stochastic trip information, the ADHDP model is established, including determining the utility function, network design and training process. Finally, two case studies are given to compare the economic performance of the vehicle under different information acquisition scenarios, which lays a foundation for analyzing the relationship between the amount of information input and energy-saving potential of the vehicle. Simulation results demonstrate that the proposed method gains a better performance in both real-time performance and global optimality.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Information-physical-energy, Dynamic programming, Information acquisition scenario, Action-dependent heuristic dynamic&nbsp, programming, Practical application, Energy management
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-310631 (URN)10.1016/j.apenergy.2022.118668 (DOI)000772043900005 ()2-s2.0-85124819541 (Scopus ID)
Note

QC 20220411

Available from: 2022-04-11 Created: 2022-04-11 Last updated: 2023-07-17Bibliographically approved
Ye, Y., Lu, J., Ding, J., Wang, W. & Yan, J. (2022). Performance improvement of metal hydride hydrogen storage tanks by using phase change materials. Applied Energy, 320, 119290, Article ID 119290.
Open this publication in new window or tab >>Performance improvement of metal hydride hydrogen storage tanks by using phase change materials
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2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 320, p. 119290-, article id 119290Article in journal (Refereed) Published
Abstract [en]

In metal hydride based hydrogen storage tanks, heat transfer fluid (HTF) has been extensively used to continuously transfer the reaction heat for promoting the reaction via heat exchangers. In this study, the phase change material (PCM) is integrated with the tank to enhance heat transfer and recycle the reaction heat. A novel storage tank with a simple concentric straight-tube heat exchanger surrounded by PCM is put forward to improve the hydrogen storage performance. A numerical model is built to track the transfer and reaction process. By comparison, the new tank shows better heat transfer and storage performance, and the hydrogen absorption time is shortened by 60.2% than that of the tank without PCM. For the new tank, the optimal amount of PCM is obtained, based on which the increased absorption pressure could effectively accelerate the heat discharge and reaction rate during the absorption process. However, the increased inlet velocity of HTF has a limited improvement effect on heat transfer and reaction performance. Furthermore, on the PCM side of the tank, the addition of fins and increasing the thermal conductivity of PCM had little effect on the performance of the tank.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Metal hydride, Phase change materials, Heat transfer fluid, Hydrogen storage, Heat transfer
National Category
Applied Mechanics
Identifiers
urn:nbn:se:kth:diva-314864 (URN)10.1016/j.apenergy.2022.119290 (DOI)000806860700004 ()2-s2.0-85130553934 (Scopus ID)
Note

QC 20220627

Available from: 2022-06-27 Created: 2022-06-27 Last updated: 2022-07-06Bibliographically approved
Campana, P. E., Papic, I., Jakobsson, S. & Yan, J. (2022). Photovoltaic water pumping systems for irrigation: Principles and advances. In: Solar Energy Advancements in Agriculture and Food Production Systems: (pp. 113-157). Elsevier BV
Open this publication in new window or tab >>Photovoltaic water pumping systems for irrigation: Principles and advances
2022 (English)In: Solar Energy Advancements in Agriculture and Food Production Systems, Elsevier BV , 2022, p. 113-157Chapter in book (Other academic)
Abstract [en]

Agriculture is one of the most water- and energy-intensive sectors of the economy, consuming about 70% of global freshwater withdrawals. Access to clean and affordable water for irrigation is an essential step towards guaranteeing water and food security, improving incomes and living standards, decarbonizing an energy-intensive sector and attaining the United Nations Sustainable Development Goals (SDGs), in particular SDGs 2 (Zero Hunger), 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), and 13 (Climate Action). Ensuring access to water for irrigation, as well as for other agricultural (i.e., livestock watering), domestic, and industrial purposes is a global challenge, and it is more challenging in remote areas where the grid connection is often not available. Solar-powered pumping systems represent a renewable solution for the decarbonization of the irrigation sector worldwide. While solar water pumping systems were used in the past to supply water for irrigation, livestock, and domestic purposes only in remote locations without access to the electric grid, the drastic drop in photovoltaic (PV) modules prices has made the technology also competitive for on-grid applications. This chapter reviews the configurations of solar water pumping systems for irrigation, highlighting the water–food–energy nexus aspects and recent advances, reviewing case studies, and analyzing the economics and current and future challenges.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Irrigation, optimization, solar water pumping, sustainable agriculture, water–food–energy nexus
National Category
Energy Systems Energy Engineering
Identifiers
urn:nbn:se:kth:diva-329088 (URN)10.1016/B978-0-323-89866-9.00007-9 (DOI)2-s2.0-85143953288 (Scopus ID)
Note

Part of book ISBN 978-032389866-9 978-032388625-3

QC 20230615

Available from: 2023-06-15 Created: 2023-06-15 Last updated: 2023-06-15Bibliographically approved
Zhang, K., Chen, M., Yang, Y., Zhong, T., Zhu, R., Zhang, F., . . . Yan, J. (2022). Quantifying the photovoltaic potential of highways in China. Applied Energy, 324, 119600, Article ID 119600.
Open this publication in new window or tab >>Quantifying the photovoltaic potential of highways in China
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2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 324, p. 119600-, article id 119600Article in journal (Refereed) Published
Abstract [en]

Installing photovoltaic (PV) modules on highways is considered a promising way to support carbon neutrality in China. However, collecting the area of the highway, and precisely assessing the shadow area of the highway under complex terrain remain challenges. That severely hinders the assessment of highway PV potential. To address these challenges, a spatiotemporal model is developed in this study to estimate the annual solar PV potential on highways over the whole Chinese territory. First, the areas of different highway segments are calculated based on highway network and highway toll stations. Second, hourly shadow area on highways created by nearby terrain is estimated based on a digital elevation model (DEM). When calculating the highway PV potential, the solar irradiation received in these shadow areas is regarded as zero. Finally, the PV potential of all lanes and emergency lanes was estimated at the prefecture-level city scale using surface radiation data and radiation assessment models. Based on the highway data with a total mileage of 143,684 km at the end of 2020, the results show that the annual PV potential is 3,932 TW and that the corresponding installed capacity is 700.85 GW, which can generate clean electricity at a rate of up to 629.06 TWh. The annual PV potential of highways in the southeast is greater than that in the northwest owing to the higher highway density in the southeast. This study provides a reference basis for highway PV construction planning and suitably assessment in each region of China for PV highway development.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Photovoltaic highway, Hillshade, Carbon neutrality, Intelligent transportation
National Category
Energy Systems Civil Engineering
Identifiers
urn:nbn:se:kth:diva-319756 (URN)10.1016/j.apenergy.2022.119600 (DOI)000858743200005 ()2-s2.0-85135701268 (Scopus ID)
Note

QC 20221007

Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2022-10-07Bibliographically approved
Lo Zupone, G., Liu, C., Barbarelli, S., Yan, J. & Liang, B. (2022). Understanding the development and interaction of wake induced by an open centre turbine and its array design implications. Applied Ocean Research, 129, Article ID 103358.
Open this publication in new window or tab >>Understanding the development and interaction of wake induced by an open centre turbine and its array design implications
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2022 (English)In: Applied Ocean Research, ISSN 0141-1187, E-ISSN 1879-1549, Vol. 129, article id 103358Article in journal (Refereed) Published
Abstract [en]

The open centre turbine can be easily deployed with a kite-like mooring system, which is promising to harvest renewable marine resources due to its higher energy conversion efficiency, and lower cost, as well as the min-imum impacts on the submarine environment. However, the multi-turbines deployment is still a great challenge due to the interaction between the wake flow generated by each device. Understanding the wake development is critical to implementing a strategy for multi-turbine deployment and minimizing the impact of the array on the submarine environment, the shore bed and the coast.This work aims to define the wake morphology and the multi-device configuration for the open centre turbines by applying the computational fluid dynamic (CFD) analysis, and the Jensen model, validated by scaled ex-periments for traditional turbines.The first step of the research deals with a stand-alone fully resolved turbine geometry. The annular rotor works like a Venturi channel: the flow passing through the central hole rises its velocity and reduces the pressure behind the rotor plane. The induced suction effect reduces the tangential flow velocity components, containing either the wake radial expansion to 1.6R (turbine radius) and axial extension to 6D (turbine diameter). The wake takes a cylindrical shape and the flow field outside this cylinder can be assumed as undisturbed.The second step of research deals with the study of an optimal multi-device layout. The parameters to be found are the distance between rotors' rows and turbines' wheelbase, under the condition that the power of each turbine is almost equal. For a 2 staggered turbines layout, a wheelbase of 3D and a distance between rows of 5D allow for keeping the devices' performances constant, being, in both cases, the array Cp = 0.414. For 3 turbines in two staggered rows, the optimal configuration is characterized by a wheelbase of 1.5D and a distance between rows of 3D, with an array Cp = 0.413.The key to this performance is the cylindrical wake generated by the open centre rotor geometry: in the multi -device configuration any turbine is decoupled, so there is no mutual disturbance even at reduced inter-device clearances.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Open centre turbine, Wake phenomena, Tidal, Multi -device farm, Tidal turbine farm
National Category
Fluid Mechanics and Acoustics Energy Engineering
Identifiers
urn:nbn:se:kth:diva-322200 (URN)10.1016/j.apor.2022.103358 (DOI)000883888800006 ()2-s2.0-85139592425 (Scopus ID)
Note

QC 20221206

Available from: 2022-12-06 Created: 2022-12-06 Last updated: 2022-12-06Bibliographically approved
Zhang, K., Qian, Z., Yang, Y., Chen, M., Zhong, T., Zhu, R., . . . Yan, J. (2022). Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis. Sustainable cities and society, 78, Article ID 103598.
Open this publication in new window or tab >>Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis
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2022 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 78, article id 103598Article in journal (Refereed) Published
Abstract [en]

Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-theart deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs).

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Ensemble learning, Street view image, Image classification model, Road noise barrier, Sustainable Transport Infrastructure
National Category
Social and Economic Geography
Identifiers
urn:nbn:se:kth:diva-307299 (URN)10.1016/j.scs.2021.103598 (DOI)000734475700003 ()2-s2.0-85121804318 (Scopus ID)
Note

QC 20220121

Available from: 2022-01-21 Created: 2022-01-21 Last updated: 2023-07-17Bibliographically approved
Qian, Z., Chen, M., Yang, Y., Zhong, T., Zhang, F., Zhu, R., . . . Yan, J. (2022). Vectorized dataset of roadside noise barriers in China using street view imagery. Earth System Science Data, 14(9), 4057-4076
Open this publication in new window or tab >>Vectorized dataset of roadside noise barriers in China using street view imagery
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2022 (English)In: Earth System Science Data, ISSN 1866-3508, E-ISSN 1866-3516, Vol. 14, no 9, p. 4057-4076Article in journal (Refereed) Published
Abstract [en]

Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).

Place, publisher, year, edition, pages
Copernicus GmbH, 2022
National Category
Physical Chemistry
Identifiers
urn:nbn:se:kth:diva-318188 (URN)10.5194/essd-14-4057-2022 (DOI)000850260100001 ()2-s2.0-85139936467 (Scopus ID)
Note

QC 20220916

Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2023-06-08Bibliographically approved
Zhang, Z., Qian, Z., Zhong, T., Chen, M., Zhang, K., Yang, Y., . . . Yan, J. (2022). Vectorized rooftop area data for 90 cities in China. Scientific Data, 9(1), Article ID 66.
Open this publication in new window or tab >>Vectorized rooftop area data for 90 cities in China
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2022 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 9, no 1, article id 66Article in journal (Refereed) Published
Abstract [en]

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km(2) across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

Place, publisher, year, edition, pages
Springer Nature, 2022
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-310191 (URN)10.1038/s41597-022-01168-x (DOI)000763431100003 ()35236863 (PubMedID)2-s2.0-85125612675 (Scopus ID)
Note

QC 20220404

Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2023-09-25Bibliographically approved
Zhong, T., Zhang, Z., Chen, M., Zhang, K., Zhou, Z., Zhu, R., . . . Yan, J. (2021). A city-scale estimation of rooftop solar photovoltaic potential based on deep learning. Applied Energy, 298, Article ID 117132.
Open this publication in new window or tab >>A city-scale estimation of rooftop solar photovoltaic potential based on deep learning
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2021 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 298, article id 117132Article in journal (Refereed) Published
Abstract [en]

The estimation of rooftop solar photovoltaic (PV) potential is crucial for policymaking around sustainable energy plans. But it is difficult to accurately estimate the availability of rooftop area for solar radiation on a city-scale. In this study, a generic framework for estimating the rooftop solar PV potential on a city-scale using publicly available high-resolution satellite images is proposed. A deep learning-based method is developed to extract the rooftop area with image semantic segmentation automatically. A spatial optimization sampling strategy is developed to solve the labor-intensive problem when training the rooftop extraction model based on prior knowledge of urban and rural spatial layout and land use. In the case study of Nanjing, China, the labor cost on preparing the dataset for training the rooftop extraction model has been reduced by about 80% with the proposed spatial optimization sampling strategy. Meanwhile, the robustness of the rooftop extraction model in districts with different architectural styles and land use has been improved. The total rooftop area extracted was 330.36 km(2), and the overall accuracy reached 0.92. The estimation results show that Nanjing has significant potential for rooftop-mounted PV installations, and the potential installed capacity reached 66 GW. The annual rooftop solar PV potential was approximately 311,853 GWh, with a corresponding estimated power generation of 49,897 GWh in 2019.

Place, publisher, year, edition, pages
Elsevier BV, 2021
Keywords
rooftop solar photovoltaic (PV) potential, geographic information systems (GIS), Deep learning, Sampling strategy, City-scale
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-304713 (URN)10.1016/j.apenergy.2021.117132 (DOI)000708642300003 ()2-s2.0-85108084262 (Scopus ID)
Note

QC 20220923

Available from: 2021-11-10 Created: 2021-11-10 Last updated: 2022-09-23Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0300-0762

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