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  • 1.
    Chen, Haoqian
    et al.
    Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China..
    Sui, Yi
    Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China.;Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan..
    Shang, Wen-long
    Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China.;Beijing Jiao Tong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China..
    Sun, Rencheng
    Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China..
    Chen, Zhiheng
    Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan..
    Wang, Changying
    Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China..
    Han, Chunjia
    Birkbeck Univ London, Dept Management, London WC1E 7HX, England..
    Zhang, Yuqian
    China Inst Marine Human Factors Engn, Yingshanhong Rd 117, Qingdao 266400, Peoples R China..
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan.
    Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 325, article id 119863Article in journal (Refereed)
    Abstract [en]

    Transforming the road public transport to run on renewable energy is vital solution to achieve carbon neutral and net zero goals. This paper evaluates the potential of using solar radiation-generated electricity as an auxiliary power supplementary for the battery of electric buses, based on a developed framework that using publicly street-view panoramas, GPS trajectory data and DEM data as input parameters of solar radiation model. A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas shows that the solar-radiation electricity generated at noon during the operation accounts for about one-fifth, one-eighth of the total elec-tricity consumption of a bus traveling one kilometer in a sunny day and a cloudy day, respectively. Spatial variability shows significant solar-radiation power generation advantages in newly-launched areas and expressway. The solar power generated in a sunny day can make a bus half of passengers and with air conditioner off at least one extra trip in 2:1 replacement schedule, and nearly close to one extra trip in 4:3 replacement schedule. A correlated relation between the solar-radiation power generation benefit and the operation schedule of electric buses is observed, implying that the high cost of 2:1 replacement schedule for long-distance routes during summer or winter can be reduced. The proposed framework can help us evaluate and understand the feasibility of solar radiation-generated electricity energy of electric bus fleets covering the large-scale urban areas at different times, locations, and weather conditions, so as to support effective decisions at better planning of PV-integrated electric buses.

  • 2.
    Chen, Y.
    et al.
    Peng Cheng Laboratory, Shenzhen, China.
    Huang, D.
    The University of Tokyo, Chiba, Japan.
    Zhang, D.
    Southern University of Science and Technology, Shenzhen, China.
    Zeng, J.
    Peng Cheng Laboratory, Shenzhen, China.
    Wang, N.
    Peking University, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. LocationMind Inc., Tokyo, Japan.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method2021In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 445, article id 110624Article in journal (Refereed)
    Abstract [en]

    Machine learning models have been successfully used in many scientific and engineering fields. However, it remains difficult for a model to simultaneously utilize domain knowledge and experimental observation data. The application of knowledge-based symbolic artificial intelligence (AI) represented by expert systems is limited by the expressive ability of the model, and data-driven connectionism AI represented by neural networks is prone to produce predictions that might violate physical principles. In order to fully integrate domain knowledge with observations and make full use of the strong fitting ability of neural networks, this study proposes theory-guided hard constraint projection (HCP). This deep learning model converts physical constraints, such as governing equations, into a form that is easy to handle through discretization, and then implements hard constraint optimization through projection in a patch. Based on rigorous mathematical proofs, theory-guided HCP can ensure that model predictions strictly conform to physical mechanisms in the constraint patch. The training process of theory-guided HCP only needs a small amount of labeled data (sparse observation), and it can supervise the model by combining the coordinates (label-free data) with domain knowledge. The performance of the theory-guided HCP is verified by experiments based on a published heterogeneous subsurface flow problem. The experiments show that theory-guided HCP requires fewer data, and achieves higher prediction accuracy and stronger robustness to noisy observations, than the fully connected neural networks and soft constraint models. Furthermore, due to the application of domain knowledge, theory-guided HCP possesses the ability to extrapolate and can accurately predict points outside of the range of the training dataset.

  • 3.
    Du, J.
    et al.
    National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China.
    Zheng, J.
    National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China.
    Liang, Y.
    National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China.
    Liao, Q.
    National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China.
    Wang, B.
    National-Local Joint Engineering Laboratory of Harbour Oil & Gas Storage and Transportation Technology, School of Petrochemical Engineering and Environment, Zhejiang Ocean University, No. 1 Haida South Road, Zhoushan, 316022, China.
    Sun, X.
    National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. National-Local Joint Engineering Laboratory of Harbour Oil & Gas Storage and Transportation Technology, School of Petrochemical Engineering and Environment, Zhejiang Ocean University, No. 1 Haida South Road, Zhoushan, 316022, China.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants2023In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 118, article id 105647Article in journal (Refereed)
    Abstract [en]

    Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM. 

  • 4.
    Jiang, X.
    et al.
    Jilin Jianzhu University, Changchun, 130118, China.
    Song, X.
    Harbin Institute of Technology, Harbin, 150006, China.
    Zhao, H.
    Jilin Jianzhu University, Changchun, 130118, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. LocationMind Inc, 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo, 101-0032, Japan; The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
    Rural tourism network evaluation based on resource control ability analysis: A case study of Ning’an, China2021In: Land, E-ISSN 2073-445X, Vol. 10, no 4, article id 427Article in journal (Refereed)
    Abstract [en]

    Organization of rural tourism resources is important for optimizing rural land use based on rational resource classification. Quantitative analysis was performed to evaluate the resource control ability of rural tourism networks. This was achieved by determining the resource control relationship and assessing the structure of the rural tourism network. The ability of resource control was analyzed via resource abstraction, which included the extraction of resource nodes and corridors, control scope analysis, and network structure level evaluation. The proposed approach was applied to the Ning’an in Heilongjiang Province, China, and proved to be effective for exploring the network degree and development trends in rural tourism resources. By examining the resource control ability, the spatial characteristics and development trend in rural tourism networks were quantitatively analyzed, especially the connection mode of key tourism resources, network structure analysis, and resource linking ability. The core resources showed a lack of outward ability in the network, and the secondary resource expansion ability was limited. Via resource control ability analysis, this study focused on areas with rich tourism but an unbalanced spatial structure, combining the directional characteristics of the network to provide suggestions for the optimization rural tourism resources network in other regions of the world. 

  • 5.
    Jiao, Yingqi
    et al.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China.
    Qiu, Rui
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China; .
    Liang, Yongtu
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China.
    Liao, Qi
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China.
    Tu, Renfu
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China.
    Wei, Xintong
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, Chiba, Japan.
    Integration optimization of production and transportation of refined oil: A case study from China2022In: Chemical engineering research & design, ISSN 0263-8762, E-ISSN 1744-3563, Vol. 188, p. 39-49Article in journal (Refereed)
    Abstract [en]

    The logistics management of refined oil under a separation of production and transportation leads to high logistics costs and a mismatch between the supply and demand sides. This paper intends to develop a general framework to assess the impact of the integration of the production and transportation in terms of economic, environmental, and energy benefits. Firstly, this paper proposes a tactical-level mathematical model for optimizing the integration of production and transportation of refined oil to minimize the total cost. In the model, several factors, such as level of market demand, production capacity limits, transportation modes, and transportation capacity, are taken into consideration. Then, the energy, economy, and environment analysis method are applied to assess the impact of the integration on the field of refined oil logistics. Four scenarios are set up and a comparative analysis is carried out in detail in China. The optimal resource allocation scheme and production adjustment scheme for each scenario are obtained. The results show that after the integration, the logistics cost is reduced by 6.8 %− 11 %, the greenhouse gas emission is reduced by 7.3 %− 17.7 %, and the energy consumption per unit turnover is reduced by 4.4 %− 7.4 %. This proves that the integration of production and transportation guided by the proposed method performs positive economic, environmental, and energy benefits. Finally, policy implications are provided.

  • 6.
    Li, Peiran
    et al.
    The University of Tokyo, Japan.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. The University of Tokyo, Japan.
    Guo, Zhiling
    The University of Tokyo, Japan.
    Lyu, Suxing
    The University of Tokyo, Japan.
    Chen, Jinyu
    The University of Tokyo, Japan.
    Li, Wenjing
    The University of Tokyo, Japan.
    Song, Xuan
    SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China.
    Shibasaki, Ryosuke
    The University of Tokyo, Japan.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning2021In: Advances in Applied Energy, ISSN 2666-7924, Vol. 4, p. 100057-100057, article id 100057Article in journal (Refereed)
    Abstract [en]

    The photovoltaic (PV) industry boom and increased PV applications call for better planning based on accurate and updated data on the installed capacity. Compared with the manual statistical approach, which is often time-consuming and labor-intensive, using satellite/aerial images to estimate the existing PV installed capacity offers a new method with cost-effective and data-consistent features. Previous studies investigated the feasibility of segmenting PV panels from images involving machine learning technologies. However, due to the particular characteristics of PV panel semantic-segmentation, the machine learning tools need to be designed and applied with careful considerations of the issue formulation, data quality, and model explainability. This paper investigated the characteristics of PV panel semantic-segmentation from the perspective of computer vision. The results reveal that the PV panel image data has several specific characteristics: highly class-imbalance and non-concentrated distribution; homogeneous texture and heterogenous color features; and the notable resolution threshold for effective semantic-segmentation. Moreover, this paper provided recommendations for data obtaining and model design, aiming at each observed character from the viewpoints of recent solutions in computer vision, which can be helpful for future improvement of the PV panel semantic-segmentation.

  • 7.
    Pan, Shiyuan
    et al.
    College of artificial intelligence, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China.
    Shi, Xiaodan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dong, Beibei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, Haoran
    School of Urban Planning and Design, Peking University, No.2199 Lishui Road, Nanshan District, Shenzhen, Guangdong, 518055, China.
    Liang, Yongtu
    Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants2024In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 359, article id 130344Article in journal (Refereed)
    Abstract [en]

    Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO2 concentration of the flue gas (FG), which further affect the operation of post-combustion CO2 capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO2 concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO2 concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO2 concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships.

  • 8.
    Qiu, R.
    et al.
    China University of Petroleum-Beijing, Beijing, China.
    Liang, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Liao, Q.
    China University of Petroleum-Beijing, Beijing, China.
    Wei, X.
    China University of Petroleum-Beijing, Beijing, China.
    Zhang, H.
    China University of Petroleum-Beijing, Beijing, China.
    Jiao, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, Japan.
    A model-experience-driven method for the planning of refined product primary logistics2022In: Chemical Engineering Science, ISSN 0009-2509, E-ISSN 1873-4405, Vol. 254, article id 117607Article in journal (Refereed)
    Abstract [en]

    Logistics planning is regarded as the most complex part of supply chain management for refined products. A vital knowledge gap still exists in understanding the trade-offs between the economy and the practicability of logistics schemes. Focus on this issue, this paper proposes a model-experience-driven method for the planning of refined product primary logistics. The method couples three sub-modules: (1) use coordinator's preference information and convex function interpolation to construct satisfaction indicator; (2) set up a multi-objective model for logistics coordination and optimization considering supply adjustment and secondary delivery; (3) adopt the augmented ɛ-constraint method to obtain the Pareto solutions and balance the economy and satisfaction indicators. The method is verified by a small-scale system, where the satisfaction degree increases by 77% while the logistics cost remains unchanged. The method is also successfully applied to a large-scale system with 29 refineries and 196 market depots, where Pareto logistics schemes are obtained and the supply–demand imbalance is greatly eased. The proposed method can help provide theoretical guidance for real-world logistics planning.

  • 9.
    Qiu, R.
    et al.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Liao, Q.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Klemeš, J. J.
    Sustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology – VUT Brno, Technická 2896/2, Brno, 616 69, Czech Republic.
    Liang, Y.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Guo, Z.
    Sinopec Engineering Incorporation, No.21, Anhui North Li'an Garden, Chaoyang District, Beijing, 100101, China.
    Chen, J.
    Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
    Roadmap to urban energy internet with wind electricity-natural gas nexus: Economic and environmental analysis2022In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 245Article in journal (Refereed)
    Abstract [en]

    Electrolysis hydrogen generation technology is one of the feasible ways to alleviate the problem of wind electricity curtailment. One promising hydrogen value-added application is to blend hydrogen into the natural gas grid and sell it as the heat energy carrier. This paper aims to discuss the feasibility of a roadmap to urban energy internet with wind electricity-natural gas nexus. Firstly, a framework is raised to integrate wind electricity generation, electrolysis hydrogen generation, and hydrogen-natural gas blending systems. Secondly, a series of reasonable hydrogen supply profiles are provided based on annual electricity curtailment and realistic natural gas scheduling. Then, an energy optimisation model and a techno-economic model are applied to simulate the generation of electricity and hydrogen, as well as determine the most economical hydrogen supply scheme. Finally, a case study in the Beijing-Tianjin-Hebei region of China is taken to validate the benefits of the proposed roadmap. The preferred scheme is worked out with the net present value of 88.8 M$, including the economy configurations of the electricity-hydrogen hybrid generation system, as well as the hydrogen-natural gas blending plan. The results also indicate that annual electricity curtailment and annual carbon emission are decreased by 204 GWh (48.8%) and 40.2 kt (49.9%).

  • 10.
    Wei, X.
    et al.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    Liang, Y.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    Qiu, R.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    Liao, Q.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    zhang, B.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    Jiao, Y.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
    Assessing benefits in the flexibility of refined oil logistics from pipeline network integration reform: A case from South China2022In: Chemical Engineering Science, ISSN 0009-2509, E-ISSN 1873-4405, Vol. 253, article id 117605Article in journal (Refereed)
    Abstract [en]

    The pipeline network integration reform enables unified management of pipelines from different entities. For refined oil logistics, this paper proposes a framework based on the MILP optimization model to quantify its flexibility. Considering the uncertainty, three disturbances occur in the logistics concurrently, and 10,000 simulations are performed to obtain the turnover cost. The ratio of pipeline transportation cost to the calculated average turnover cost is defined as the flexibility indicator. Taking China's largest refined oil pipeline network as an example, the results show that the flexibility rises 8.9% after the reform. The paper also quantifies the impact of the reform on logistics flexibility in South China, which is embodied in achieving lower freights and GHG emissions, lower impact by fluctuations, higher pipeline utilization, more efficient oil product turnover, and the avoiding of depot shortages when facing logistical disturbances. The underlying reasons for the results and 3E analysis are analyzed.

  • 11.
    Wu, Y.
    et al.
    The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Japan.
    Xia, T.
    The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Japan.
    Wang, Y.
    China University of Petroleum (Beijing), Changping, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. The University of Tokyo, Japan.
    Feng, X.
    Xi'an Jiaotong University, Shaanxi, China.
    Song, X.
    Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China.
    Shibasaki, R.
    The University of Tokyo, Kashiwa-shi, Chiba, Japan.
    A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network2022In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 185, p. 302-320Article in journal (Refereed)
    Abstract [en]

    Offshore wind farms are increasingly becoming the focus of clean sources market because of the huge energy potential and fast-maturing technology. The existing researches normally optimize the wind turbine layout and two-dimensional cable routing independently. This work focuses on the synchronization optimization of site selection of the offshore wind farm, three-dimensional wind turbine layout and three-dimensional cable network routing based on meta-heuristic algorithms and geographic information systems. Several practical issues, i.e., restricted areas, power generation, cable network and energy loss, are taken into consideration. A two-layer model is proposed. The outer layer model is for the site selection and the wind turbine layout optimization. The inner layer model is for the obstacle-avoiding cable routing optimization. In this stage, the seabed terrain is considered for the first time. The proposed integrated model is complex and non-convex. Thus, a hybrid method including an improved ant colony optimization combined with genetic algorithm, dual-simplex method and Kruskal algorithm is proposed to search the solution more efficiently. The initialization stage of the hybrid method is improved from random assignment to directional assignment. The directional solution is obtained by the widely used genetic algorithm. A case study based on a real offshore wind farm is established to prove the effectiveness of the proposed methodology. The results show an over one million dollars increase in annual benefit compared with conventional methods.

  • 12.
    Yan, Y.
    et al.
    China University of Petroleum-Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, Chiba, Japan.
    Liao, Q.
    China University of Petroleum-Beijing, China.
    Liang, Y.
    China University of Petroleum-Beijing, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Roadmap to hybrid offshore system with hydrogen and power co-generation2021In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 247, article id 114690Article in journal (Refereed)
    Abstract [en]

    Constrained by the expansion of the power grid, the development of offshore wind farms may be hindered and begin to experience severe curtailment or restriction. The combination of hydrogen production through electrolysis and hydrogen-to-power is considered to be a potential option to achieve the goal of low-carbon and energy security. This work investigates the competitiveness of different system configurations to export hydrogen and/or electricity from offshore plants, with particular emphasis on unloading the mixture of hydrogen and electricity to end-users on land. Including the levelized energy cost and net present value, a comprehensive techno-economic assessment method is proposed to analyze the offshore system for five scenarios. Assuming that the baseline distance is 10 km, the results show that exporting hydrogen to land through pipelines shows the best economic performance with the levelized energy cost of 3.40 $/kg. For every 10 km increase in offshore distance, the net present value of the project will be reduced by 5.69 MU$, and the project benefit will be positive only when the offshore distance is less than 53.5 km. An important finding is that the hybrid system under ship transportation mode is not greatly affected by the offshore distance. Every 10% increase in the proportion of hydrogen in the range of 70%–100% can increase the net present value by 1.43–1.70 MU$, which will increase by 7.36–7.37 MU$ under pipeline transportation mode. Finally, a sensitivity analysis was carried out to analyze the wind speed, electricity and hydrogen prices on the economic performance of these systems.

  • 13.
    Yu, Q.
    et al.
    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
    Xie, Y.
    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
    Li, W.
    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
    Liu, X.
    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
    Shang, W. -L
    Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China.
    Chen, J.
    Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
    Yang, D.
    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    GPS data in urban bicycle-sharing: Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 322, article id 119533Article in journal (Refereed)
    Abstract [en]

    As a newly-emerging option of shared transportation, Internet-enabled dockless bicycle sharing is well accepted by the public. The implementation of electric fences has great potential to tackle the problem of random parking in bicycle sharing services. However, the deployment of electric fences would have a negative impact on the convenience of bicycle sharing services, which might lead to an increase in energy consumption among customers who switch their methods of transportation. This paper proposes a dynamic electric fence planning method with an assessment of resource-saving and potential energy consumption increasement. An agent-based model is proposed to simulate the trips and evaluated the performance of static and dynamic electric fences. The results show that dynamic electric fences require significantly shorter walking distances than static electric fences. The implementation of electric fences in the city center can significantly avoid random parking and improve the parking tidiness of bicycles. The implementation of dynamic and static electric fences can averagely save 25.31% and 27.76% bicycle resources. By estimating travel mode shifting, dynamic electric fence can reduce energy consumption by 5.79% per day compared to the static electric fence situation. 

  • 14.
    Zhang, Haoran
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa-shi, Japan.
    Chen, Jinyu
    The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa-shi, Japan.
    Yan, Jie
    North China Electric Power University, Beijing, China.
    Song, Xuan
    Southern University of Science and Technology(SUSTech), Shenzhen, China.
    Shibasaki, Ryosuke
    The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa-shi, Japan.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Urban power load profiles under ageing transition integrated with future EVs charging2021In: Advances in Applied Energy, ISSN 2666-7924, Vol. 1, p. 100007-100007, article id 100007Article in journal (Refereed)
    Abstract [en]

    Understanding ageing transition caused fine-grained changes of electricity profile is the significant insight for coping with future threatens in grid flexibility management. The research gaps for the hourly-basis knowledge exist due to challenges in microanalysis on user-side behavior. Based on billions of users’ behavior data, we investigated the changes on the load profiles due to population aging. We found that owing to ageing transition, the participation population in high electricity-density activities decreases by about 8%. The corresponding shift in driving behavior rises the 14% difference between peak charging load and valley. We concluded that population aging will dramatically change both the magnitude and shape of future dynamic-load profiles. Therefore, we further suggested a new solution with comprehensive and quantitative management for PVs development and the smart charging market with smooth operation of the grid in coupling the potential challenges caused by the ageing issue.

  • 15.
    Zhang, Haoran
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Li, Peiran
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Zhang, Zhiwen
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Li, Wenjing
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Chen, Jinyu
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Song, Xuan
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Shibasaki, Ryosuke
    Center for Spatial Information Science, The University of Tokyo 5-1-5 Kashiwanoha, 277-8568 Kashiwa-shi, Chiba, Japan.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis2022In: Cities, ISSN 0264-2751, E-ISSN 1873-6084, Vol. 120, article id 103502Article in journal (Refereed)
    Abstract [en]

    Lockdown measures have been a “panacea” for pandemic control but also a violent “poison” for economies.Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governmentsmeet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive andquantified guides. Based on millions of financial transaction records, and billions of mobility data, we trackedspatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sidedframework to assess the epidemiological performance and economic damage of different lockdown policies. Wefound that the pandemic duration under the strictest lockdown is less about two months than that under thelightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economicallyefficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue thatcutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helpedgovernments figure out the Pareto optimal solution set of lockdown strategy.

  • 16.
    Zhang, Haoran
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Co-benefits of renewable energy development: A brighter sky brings greater renewable power2022In: Joule, E-ISSN 2542-4351, Vol. 6, no 6, p. 1142-1144Article in journal (Refereed)
    Abstract [en]

    Air pollution reduction is one of the most straightforward co-benefits of PV development, but its mechanism is complex. In a recent One Earth paper, Chen et al. analyzed the respective effects of different factors on solar power performance and show that the co-benefits of air pollution control policies for PV over the past decade would be grossly underestimated. 

  • 17.
    Zhang, Z.
    et al.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Qian, Z.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Zhong, T.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Chen, M.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Zhang, K.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Yang, Y.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Zhu, R.
    Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
    Zhang, F.
    Senseable City Lab, Massachusetts Institute of Technology, Cambridge, 02139, MA, United States.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan; LocationMind Inc, 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo, 101-0032, Japan.
    Zhou, F.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Yu, J.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Zhang, B.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Lü, G.
    Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Department of Chemical Engineering, KTH Royal Institute of Technology, Stockholm, 10044, Sweden.
    Vectorized rooftop area data for 90 cities in China2022In: Scientific Data, E-ISSN 2052-4463, Vol. 9, no 1, article id 66Article in journal (Refereed)
    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 km2 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. 

  • 18.
    Zou, X.
    et al.
    China University of Petroleum-Beijing, Beijing, China.
    Qiu, R.
    China University of Petroleum-Beijing, Beijing, China.
    Yuan, M.
    China University of Petroleum-Beijing, Beijing, China.
    Liao, Q.
    China University of Petroleum-Beijing, Beijing, China.
    Yan, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Liang, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. The University of Tokyo, Japan.
    Sustainable offshore oil and gas fields development: Techno-economic feasibility analysis of wind–hydrogen–natural gas nexus2021In: Energy Reports, E-ISSN 2352-4847, Vol. 7, p. 4470-4482Article in journal (Refereed)
    Abstract [en]

    Offshore oil and gas field development consumes quantities of electricity, which is usually provided by gas turbines. In order to alleviate the emission reduction pressure and the increasing pressure of energy saving, governments of the world have been promoting the reform of oil and gas fields for years. Nowadays, environmentally friendly alternatives to provide electricity are hotspots, such as the integration of traditional energy and renewable energy. However, the determination of system with great environmental and economic benefits is still controversial. This paper proposed a wind–hydrogen–natural gas nexus (WHNGN) system for sustainable offshore oil and gas fields development. Combining the optimization model with the techno-economic evaluation model, a comprehensive evaluation framework is established for techno-economic feasibility analysis. In addition to WHNGN system, another two systems are designed for comparison, including the traditional energy supply (TES) system and wind–natural gas nexus (WNGN) system. An offshore production platforms in Bohai Bay in China is taken as a case, and the results indicate that: (i) WNGN and WHNGN systems have significant economic benefits, total investment is decreased by 5,190 and 5,020 million $ respectively, and the WHNGN system increases 4,174 million $ profit; (ii) WNGN and WHNGN systems have significant environmental benefits, annual carbon emission is decreased by 15 and 40.2 million kg respectively; (iii) the system can be ranked by economic benefits as follows: WHNGN>WNGN>TES; and (iV) the WHNGN system is more advantageous in areas with high hydrogen and natural gas sales prices, such as China, Kazakhstan, Turkey, India, Malaysia and Indonesia.

  • 19.
    Zou, X.
    et al.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Qiu, R.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Zhang, B.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Klemeš, J. J.
    Sustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology – VUT Brno, Technická 2896/2, Brno, 616 69, Czech Republic.
    Wang, B.
    National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage, Transportation Technology/Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, No. 1 Haida South Road, Zhoushan, 316022, China.
    Liao, Q.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Liang, Y.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
    Roadmap to urban energy internet: Techno-enviro-economic analysis of renewable electricity and natural gas integrated energy system2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 373, article id 133888Article in journal (Refereed)
    Abstract [en]

    The integrated energy system which coordinates natural gas, renewable energy, and other energy subsystems is an effective way to promote a low-carbon economy. An effective framework for system assessment and optimisation is a critical issue. This paper takes a natural gas-wind-photovoltaic integrated energy system as the research object and uses the simulation software to analyse its techno-enviro-economic feasibility. Firstly, a mathematical model is customised to optimise the system installation and operation plans. Renewable electricity replaces some natural gas, resulting in pipeline pressure fluctuation. Here, the Stoner Pipeline Simulator software is used to simulate pipeline network operation to quantify the aforementioned pressure fluctuations. The proportion of renewable energy is gradually reduced until the network pressure fluctuation is less than 20% to ensure the stability of pipeline operation. Then, the optimal operation scheme can be determined. Taking three cities in Shandong, China, as cases, the results show that the proposed system is beneficial for urban energy internet development: (i) the total net present cost is reduced by 19.7%, 19.8%, and 20.8%, (ii) annual CO2 emission is reduced by 23.7%, 18.4%, and 12.2%; (iii) the levelised cost of energy is 0.142 $/kWh, 0.143$/kWh, and 0.153$/kWh. 

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