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Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders. • A novel storage service mode is proposed combining P2G technology with microgrids. • Optimization models are tailored for distributed energy system management. • Nash bargaining based-model reformulation is used for fair benefit distribution. • The performance is investigated focusing on the full recovery of solar power. [ABSTRACT FROM AUTHOR]

• 3.7 million trajectories data are adopted to develop an integrated method. • A geometry-based probability model is proposed for uncertainties of the issue. • A multi-scenario programming model is proposed for rebalancing operations. • A multi-sided sensitivity analysis is made for potential emissions reduction. As a representation of smart and sustainable city development, bicycle-sharing system is one of the hottest topics in the domains of transportation, public health, urban planning, and so on. In this paper, a model is proposed for analyzing the potential reduction in emissions associated with the adoption of a bicycle-sharing system. Methods are proposed for extracting human travel modes from mobile phone GPS trajectories, together with a geometry-based probability model, to support particle swarm optimization. A comparison study is implemented to analyze the model's computational efficiency. Based on the resulting optimal layout for the network of bicycle docking stations, and considering demand uncertainty, a multi-scenario integer linear programming model is proposed to optimize rebalancing procedures (i.e., moving bicycles between docking stations according to demand), to determine the detailed design-scale information required. Mobile phone GPS trajectories from approximately 3.7 million local mobilities are used to construct a case study for Setagaya Ward, Tokyo. The results show that, compared with the previous methods, the optimal layout solved by the proposed method could reduce emissions by a further 6.4% and 4.4%. With an increase from 30 to 90 bicycle stations, the adoption of bicycle-sharing can reduce CO 2 emissions by approximately 3.1–3.8 thousand tonnes. However, emission reduction will maximally decrease by 21.26% after offset by bicycles production and rebalancing-generated emission. [ABSTRACT FROM AUTHOR]

With the pressing demand of climate change mitigation, the rapid technological development and market adoption of battery electric vehicles are imperative. However, the diverse consumption attitudes and their interactions, which are rarely considered, can significantly affect the adoption potential of battery electric vehicles. On the basis of three years of global positioning system data of 1.6 million people, we estimated the travel and adoption demands of battery electric vehicles in Japan considering diverse consumption attitudes. Under the current construction conditions for public charging systems and charging technologies, the adoption potential of battery electric vehicles may not be as promising as previously expected, and the government still faces great pressure to respond to the market share target. Given the current level of battery technology, technical and policy improvements such as fast charging, reducing the production cost, perfecting the public charging infrastructure, and increasing purchasing subsidies were found to be more effective than improving the battery capacity at increasing the adoption potential of battery electric vehicles. [ABSTRACT FROM AUTHOR]

Leak fault significantly affects the reliable and sustainable green hydrogen energy supply by renewable Power-to-Hydrogen (P2H2) system. Deep learning has been widely applied to automated leak fault detection and diagnosis of hydrogen systems. However, due to the intermittency of wind/solar-power-generation, existing approaches developed by monitored signals have the bad generalization to leak scenario under large volatility of power-to‑hydrogen production. This study introduces graph deep probability learning approach, in which an attention-based graph neural network (GNN) learns the dependency between installed sensors. Variational inference is integrated to model posterior distribution of sensor dependencies, by using which the leak fault under large volatility of power-to‑hydrogen production is detected and localized by using normal time-series signals under different timeslots of one day as training data. Experiment of hydrogen leak faults from P2H2 system is conducted to verify the more accuracy of proposed approach compared to 6 state-of-the-art approaches. Results demonstrated our proposed approach detects the leak fault more accurately with a higher AUC of 0.96 and successfully localizes all the leak faults. This study supports more reliable and efficient safety monitoring for upcoming renewable P2H2 system in future. • A graph deep probability learning approach is proposed to accurately detect and localize leak fault of P2H2 production. • Experiment of hydrogen leak is conducted to compare our proposed approach with 6 existing approaches. • Our approach achieves higher detection accuracy and localizes all faults when large volatility of P2H2 production exists. • Our proposed approach has better generalization to faults detection and diagnosis of upcoming renewable P2H2 system. [ABSTRACT FROM AUTHOR]

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatio-temporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID-19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties. [ABSTRACT FROM AUTHOR]

• Proposing a framework to analyze emission patterns of buses and changes in post-COVID-19. • 2056 buses with 1.5 million ridership and 7589 taxis with 0.2 million trips are used for analysis. • 224 social surveys are collected and show a 56.3% ridership reduction in post-COVID-19. • We find buses cannot be "greener" travel modal than cars if ridership reduces by more than 40%. Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers' trips are used for acquiring private cars' emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be "greener" travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future. [ABSTRACT FROM AUTHOR]

• Proposing a framework to assess the emission reduction potential of ride-sharing from empirical mobility data. • Proposing a deep learning-based method that shows higher performance than traditional simulation method. • Over 1 million historical individual GPS records in Tokyo area are employed for analysis. • We found that averagely 26.97% of traveling distance can be saved and maximally 84.52% of reduction of CO2 emission. Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing of car journeys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO 2 is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO 2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers. [ABSTRACT FROM AUTHOR]

• A multi-objective model for the design optimization of energy system is developed. • The proposed model is expected reduce carbon footprint at a maximum of 4.53%. • The model could optimize the design and operation strategies of the energy system. • Economic and environmental objective are taken into account in the model. • The effectiveness of the methodology is verified through a Floating Production system. Deep-sea oil and gas fields are acting as a vital role by providing substantial oil and gas resource, and Floating Production Storage and Offloading is an indispensable tool for the development of offshore oil and gas fields effectively. Here, Life Cycle Assessment is applied to evaluate environmental loads in the whole life cycle of the deep-sea oil and gas production. This paper explores the carbon footprint of Floating Production Storage and Offloading as the time axis. It is found that Floating Production Storage and Offloading is a conceptual product at the design stage and does not generate carbon emission, while the operational stage releases considerable emission by the fuel combustion process, accounting for 88.2% of the entire life cycle. To decrease this part of carbon emission, distributed energy system is considered as a promising choice because it integrates different energy resources and provides an economic and environmental energy allocation scheme to meet the energy demand. For the operation stage, this paper establishes a Multi-objective Mathematical Programming model to determine the selection and capacity of facilities with minimum annual total cost and carbon emissions by considering the energy balance and technical constraints. The model is validated by an example and solved by the weight method. According to designer's demand, distributed energy system can optimize economic objectives in a maximum range of 14.6%, and a maximum emission reduction of 4.53% can be expected compared with the traditional scheme. Sensitivity analysis shows that cost is more sensitive to natural gas price. [ABSTRACT FROM AUTHOR]

• Long short-term memory is used to predict the multi-region solar power output. • Particle swarm optimization is used to optimize the long short-term memory model. • Sensitivity analysis is done to verify the stability of the method. • Different long short-term memory structures are compared. • A real case is studied to illustrate the effectiveness of the proposed method. Solar energy, as a renewable and clean energy source, has developed rapidly and has attracted considerable attention. The integration of solar energy into a power grid requires precise prediction of the power output of solar plants. Accurate solar power output prediction can promote power dispatch, maintaining the normal operation of power systems. However, research on multi-region solar power is still rare. In this study, long short-term memory and a particle swarm optimization algorithm contribute to solar power prediction considering time series. In order to improve the prediction accuracy, particle swarm optimization is used to optimize the parameters of the long short-term memory model. In addition, different long short-term memory structures are illustrated to determine the final prediction model with sensitivity analysis. Experiments are carried out to verify the effectiveness of the proposed method. The mean absolute error and root mean square error of the proposed method is the smallest among the prediction methods in four cases containing different seasons. In terms of prediction accuracy, results indicate that the proposed prediction model outperforms basic long short-term memory, artificial neural network, and extreme gradient boosting. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the existing natural gas pipeline network is regarded as the most potential transportation mode on the utility-scale. However, the impact of hydrogen on the operation performance of the pipeline remained unclear. This paper analyzes the economic and environmental performance of natural gas pipeline under different hydrogen blending ratios and different hydraulic boundary conditions. In order to reduce the operation costs and carbon emissions in each condition, this paper establish a non-linear optimization model to get the new operation plan of the pipeline system. Further, in order to characterize different boundary conditions of the system, four scenarios, namely, the baseline scenario (BS), the setpoint of flow rate scenario (SF), the setpoint of pressure (SP), and the setpoint of heat flow rate (SH) are proposed in this paper. Two studied cases demonstrated that: (1) the original compressor for natural gas can adapt to a hydrogen blending without change the type of compressor. (2) The optimization model has significant potential in reducing the economic cost and carbon emissions of the system with an average decrease of 11.48%. (3) For every 1% of hydrogen added, the annual operating cost of the system is reduced by $0.73 million (3.29%) in SF, 0.017 million (0.08%) in SP, or increased by $1.67 million (7.50%) in SH. Moreover, the annual carbon emission is reduced by 0.38 kt (3.53%) in SF, 0.047 kt (0.44%) in SP, or increased by 0.76 kt (7.14%) in SH. However, when the hydrogen mixing ratio is more than 8%, the contrary trend may occur. (4) The maximum blending ratio at different points of the multi-source pipeline network are physically interdependent. The paper proposed an optimization model for the 2-E analysis of the pipeline and can provide theoretical guidance for the further application of this transportation mode. • Hydraulic impact of hydrogen on natural gas pipeline is estimated. • Factors that constrain further hydrogen blending are analyzed. • A power optimization model is proposed for the given hydrogen blending ratio. • Pareto set of hydrogen blending are obtained for multi-source pipeline. [ABSTRACT FROM AUTHOR]

Incorrect predictions or underestimation of a city's solar potential can result from neglecting common features of photovoltaic (PV) panels from remote sensing images. This paper proposes an improved approach to address the challenge of accurately segmenting PV panels from remote sensing images using deep learning methods. The proposed method incorporates common features of PV panels and a constraint refinement module (CRM) to perform the localization of PV panel regions and shape regularization more accurately. Specifically, the method uses a color loss function based on prior knowledge of color to refine the predicted region with correct color information among confusing objectives, and a shape loss function based on multi-layer shape targets calculation to refine the initial segments and constrain the edge information of predicted regions. Different CRMs are embedded into the four refined initial segment modules, respectively, to improve the detection IoU of PV panels by up to 7.44%. The best CRM-integrated model performs the best IoU of 74.66% when segmenting PV panels. The proposed method has important implications for urban PV panel segmentation at the city level and provides a promising solution for remote sensing image-based PV plate segmentation tasks in challenging scenarios. • We proposed an improved feature-oriented segmentation method to enhance the accuracy and contextual information of PV panels. • We incorporated common features of PV panels and CRM to perform PV panel region localization and shape regularization. • We designed a color constraint refinement module via prior knowledge to refine the predicted region among confusing targets. • We designed a shape constraint refinement module based on multi-layer shape targets to constrain the edge of predicted areas. [ABSTRACT FROM AUTHOR]

• LNG point supply has the advantages of safety, economy, and environmental protection. • Incomplete construction regulations are the challenges of LNG point supply method. • LNG supply chain optimization model aims to minimize economic & environmental costs. • This paper employs the actual data of towns in the optimization model for research. • The sensitivity of construction cost is relatively high. Liquefied natural gas (LNG) point supply is one of the most flexible and convenient methods advocated by China for distributing energy to end-users such as towns and villages where it is difficult or uneconomical to construct gas pipelines. This study sorts out the advantages and challenges and designs the optimization model for LNG point supply in China based on a mixed-integer linear programming (MILP) model. In this model, the goal of optimization is to minimize economic and environmental costs in the construction and operation stages. By optimizing location-allocation, gas storage capacity, vaporization capacity, equipment selection, and pipeline distribution, the model meets the fluctuating seasonal demand for natural gas. This model design uses the actual construction and operation-related parameters of the vaporization station and pipeline, and applies the model to four different types of towns. Through sensitivity analysis, this study finds that the sensitivity of construction cost is relatively high. [ABSTRACT FROM AUTHOR]

• An evaluation approach for quantifying the impact of policy on bioenergy is proposed. • Policy scenarios have been coupled into the mathematical model. • The bioenergy development potential of 31 provinces in China has been evaluated. • The impact of policy and correspondingly development direct of bioenergy have been revealed. As the most competitive energy for replacing fossil fuel, bioenergy accords with the requirement of clean supply and brings great economic and social benefits. This study develops a comprehensive framework to quantitatively evaluate the impact of policy on the bioenergy industry. The proposed framework comprises of two major parts, containing simulation models for the biomass resource allocation under different policies and the comprehensive evaluation system for bioenergy development potential under the specific policy. Five policies are considered, including encouraging biomass cogeneration (EBC), supporting biomass heating (SBH), developing technology for biofuel (DPT), establishing raw material collection system (RMC), and priority of using bio-natural gas (PUB). The results demonstrate that the order of policy impact is DPT > PUB > SBH > EBC > RMC. DPT has the largest promotion of biofuel, which is the preferred policy of the development of bioenergy. PUB can better promote the development of biomass power generation, while EBC and SBH have a greater impact on biomass heating. The impacts of policies on regional bioenergy development have been revealed and regional policy implications are given. This approach provides a tool for policymakers to quantify the impacts of policy. [ABSTRACT FROM AUTHOR]

• Proposing a framework for assessing fuel consumption of taxi-sharing. • Proposing a novel dispatch system to improve taxi operation efficiency. • Routing probability model integrated to plan flexible and efficient shared trips. • Case study in Shanghai with over 43 million GPS records conducted for analysis. • Result found that taxi-sharing may increase fuel consumption in high demand areas. With the emergence of big geospatial data and the breakthrough of massive data processing, taxi-sharing offers the public a novel transportation mode with high comfort but low cost. However, designing a taxi-sharing system to effectively allocate taxi resources and provides high public acceptance services is an urgent problem to be solved. Furthermore, to what extent the taxi-sharing can be an eco-friendly service without bringing extra pressure to urban emission, fuel consumption, and transportation system is still an unanswered question. This paper proposes a methodology framework to design a taxi-sharing system with driver routing probability based matching and dispatching algorithms. The methodology is capable of matching multiple taxi trips into a sharing trip, with consideration of temporal and spatial feasibilities. The matching of sharing trips includes the determination of which trips to match and the sequence of the destinations. To examine the potential of operation efficiency improved and fuel consumption reduced in taxi-sharing, three scenarios are proposed with different constraints, representing different operation strategies. The sharing trips are then dispatched to the taxis. The potential of operating performance improvement and the potential of fuel consumption reduction are analyzed in the three scenarios. It is found that the delivery part of taxi-sharing may produce more travel distance because of the detour. The key factor for taxi-sharing service to reduce Vehicle Kilometres Travelled is the idle trips and the taxi resources saved. Considering both delivery trips and idle trips together, although taxi-sharing can reduce total fuel consumption in the city, it may increase traffic pressure in certain range area, especially in the key road sections or intersections in the urban road network and the area with high traffic demand. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy for hydrogen production and blending it into natural gas networks is recognized as a promising approach to promote a low-carbon transformation of power systems and enhance the integration of renewable energy sources. However, the temporal and geographical availability of renewable energy and natural gas operation conditions vary greatly, emphasizing the importance of precise planning and operational strategies for the renewable energy‑hydrogen-natural gas coupling system. This article introduces a smart design and operational strategy for a wind electricity‑hydrogen-natural gas coupling system aimed at enhancing renewable energy accommodation. The primary objectives are to evaluate the technical and economic feasibility of such coupling systems and to determine the optimal planning and operation strategies for hydrogen production, transportation, and blending into natural gas networks while minimizing total investment and operational costs. By utilizing real curtailed electricity data and natural gas pipeline network operating flow rate data, the potential of this coupling system to enhance renewable energy accommodation and reduce carbon emissions is explored. The results indicate that this optimized coupling system can accommodate 20.11% of curtailed wind electricity and reduce carbon emissions by 1.05 billion tons per year. Additionally, the impact of hydrogen blending ratios and hydrogen prices on the economic and environmental performance of the coupling system is also investigated. Improving the hydrogen blending ratio is the key to enlarging the economic and environmental performance of the coupling system, but the hydrogen sale price has a negligible impact on the renewable energy accommodation and environmental performance of the coupling system. • A coupling system with wind farm, hydrogen, and natural gas subsystems is proposed. • A planning and operation optimization model of the coupling system is established. • Hydrogen blending strategies for high renewable energy accommodation are developed. • Impact of hydrogen blending ration and sale price on optimization results is investigated. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic (PV) segmentation based on remote sensing images is highly applied in solar potential assessment and prediction. Still, such methods often feature dataset limitations of PV data, poor robustness, and are non-generalizable. General Generative AI eliminates the need for pre-training emerging to improve the sample diversity and algorithm robustness and generalizability of the segmentation. This paper designs a PV sample generation method based on the generative model, which leverages the text-guided stable diffusion inpainting model to augment the PV dataset and generate massive multi-background rooftop PV panel samples. The real and generated samples are mixed in different proportions to form a new training set for ablation experiments. Results show that a small number of real datasets mixed with generated data could reach a high relative IoU and Precision value. In small sample learning, the generated data achieves similar effects as real data during the segmenting process even better than without generated data. It demonstrates that the generated datasets outperform traditionally augmented data and that the manual text prompts are tested more accurately than ChatGPT-generated ones. This study highlights the efficiency and robustness of generated datasets in PV segmentation tasks and moves beyond the constraints of remote sensing data acquisition and limited data diversity. Further, it would facilitate large-scale assessments of the urban PV potential for urban planners and policymakers using an efficient and low-cost method. • This study leverages a text-guided stable diffusion inpainting model to augment the PV dataset and improve data diversity. • It proves that a small number of real datasets mixed with generated data could reach high relative precision and robustness. • The General Generative AI-based data augmentation reaches a higher accuracy than conventional data augmentation. • The manual text prompts perform better than the ChatGPT-generated versions. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources. • Real trajectory data with 19,900 ETs is used for V2G potential and resilience analysis. • A method infers charging patterns, energy use, and reconstructs State of Charge is proposed. • A general method framework for modeling ET-V2G system on supply side is proposed. • An agent-based model is introduced for ET-V2G system modeling and simulation. • ET-V2G system can supply up to 60 MW and recover within 2–3 h during peak periods. [ABSTRACT FROM AUTHOR]

The increasing need to develop renewable energy sources to combat climate change has led to a significant rise in demand for photovoltaic (PV) installations. Consequently, accurately detecting and estimating the capacity and potential for electricity generation of these installed PV systems has become crucial for effective energy management. However, due to the limitation of localized mechanism in convolution operations, traditional FCN-based methods face challenges in capturing global dependencies among remote sensing image patches. As a result, they struggle to model long-range interactions between different PV panels with diverse structures, including varying size, shape, and texture, particularly in distributed residential areas. To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation. Specifically, Mix Transformer block is incorporated in the encoder to enhance the modeling of global context, while the U-Shaped structure enables the combination of multi-level features, resulting in enriched feature representation, which helps the model better understand objects in the images, regardless of their shape, size, and texture. In addition, we implement the PointRend module in the decoder to obtain finer segmentation boundary details, and utilize a novel refiner loss function during the training process to alleviate the problem of extremely unbalanced samples. The experimental results from the Heilbronn datasets demonstrate the remarkable performance of our proposed TransPV model in addressing intra-class diversity of PV structures, surpassing previous state-of-the-art methods with an impressive IoU and accuracy of 0.802 and 0.876, respectively. Furthermore, our model exhibits high generalization capability on the BDPV dataset with IoU of 0.745. The obtained results highlight the superiority of TransPV in improving accuracy and addressing diverse structure issues in PV segmentation, which provides valuable insights for optimizing the efficiency and sustainability of renewable energy. • The main challenges in real-world PV segmentation scenarios are studied. • The Vision Transformer mechanism is pioneeringly applied to enhance PV segmentation. • A novel U-shaped Vision Transformer model is designed for PV segmentation refinement. • Our method showcases remarkable segmentation accuracy and generalization capability. [ABSTRACT FROM AUTHOR]

• This study investigated city-level energy-related household consumption. • City-scale input-output table and consumption inventories are applied. • Ten age-based household groups and their corresponding emissions were calculated. • Temperature decrease is responsible for the increased emission in older households. City is the main place to consume goods and services throughout the world. Among the various consumption terminals, household-level consumption is highly behavior driven, which can be affected by various factors such as household income level, age, living environment etc. However, city-level household emissions characteristics are still not fully understood due to the complexity of consumption behaviors and the lack of the supply chain's data. To include the environmental responsibility embodied in residential consumption and reveal how it varies among household type and season, this study investigates city-level household consumption as it relates to energy demand using a city-scale input-output model and urban residential consumption inventories. Importantly, age- and month-based emission are analyzed from different aspects such as emission type, source, fuel types and consumption items. Findings indicate that (1) household emissions differ substantially among the various household age groups; older households generally produce higher emissions levels on a per capita basis; (2) decreases in temperature are the main reason for the increased emissions in older households, while this is not a significant factor in younger households; (3) the high per capita household emissions in older households indicate inefficient energy usage among elder citizens, which strongly suggests that aging societies will face long-term emissions increases if appropriate measures are not taken. [ABSTRACT FROM AUTHOR]

• Low-cost and flexible photovoltaic power generation for urban slums was realized. • Three business models, namely slums, high-rise buildings, and energy service providers, analysed. • Different loads of various buildings and overall power generation potential in city-level analysed. • 11 typical building groups with slums and high-rise buildings selected for evaluating business model. • Most suitable solutions obtained considering energy policy impact and flexible electricity price. Photovoltaics (PVs) have become an essential part of the energy transition in recent years, and decision-makers have gradually entered the era of PV markets without subsidies. Consequently, the importance of PV policy and business models has been further demonstrated. However, in the case of rooftop PVs in urban areas, most PV promotion policies only focus on formal areas, whereas there is a lack of suitable policies directly applicable to slums with informal built environments. This study focused on a method for realizing low-cost and flexible PV power generation for urban slums with competitive business models. First, an analysis of rooftop PV in slums revealed three business models as dominant including slums, high-rise buildings, governments, power systems, and energy service providers. Afterward, considering Shenzhen as a case study, different loads of various buildings were provided, and the overall power generation potential at the city level was analyzed. Subsequently, 11 typical building groups were selected to evaluate the business model profits. Consequently, considering the construction rate caused by the energy policy of PVs, the most suitable solutions for different types of areas and various construction-rate combinations were discussed. Thus, this study argued that our novel analysis could serve as a powerful tool for policymakers to plan the future development of sustainable cities and alleviate poverty. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We incorporate a variety of principles and indicators into an improved ZSG-DEA model. • We conduct a multi-criteria allocation of China's CEA by 2030 to provincial shares. • We measure the total and per capita space for carbon emissions by province. • All provinces reach the DEA frontier with different CEA ranging from 4.21 to 16.77 Gt. • Differentiated provincial reduction policies are the key to achieving China's INDCs. Abstract Accelerating global warming has suggested the importance of controlling greenhouse gas emissions associated with human activities. In striving to fulfill the Paris Agreement, China has announced its Intended Nationally Determined Contributions (INDCs) aimed at reducing its carbon dioxide (CO 2) emission intensity by 60–65% in 2030 against the level of 2005. However, China's INDCs cannot be fulfilled without formulating appropriate schemes for the allocation of carbon emission allowance (CEA) at sub-national scales. To help close the gap in our knowledge, this paper starts with measuring the overall CEA of China by 2030, and then proposes a science-based scheme for CEA allocation by developing an improved zero sum gains-data envelopment analysis (ZSG-DEA) model. It demonstrates that the final CEA of some northern provinces can be cut down as compared to their initial shares and, conversely, most southern provinces experience an increase in their CEA. Comparing the final share of CEA by province with current carbon emissions, we observe that provinces with abundant energy reserves, such as Shanxi, Inner Mongolia and Shaanxi, tend to be operating in a state of overshoot in terms of space for carbon emissions (SCE). In contrast, there remains SCE when it comes to Guangdong, Hunan, Fujian, etc. The remaining provinces, such as Heilongjiang, Hebei and Ningxia, are close to the break-even point. In view of the differing SCE of individual provinces, common but differentiated policies for CO 2 emission control would be the key to achieving China's INDCs. The research findings lay a scientific basis for the Chinese government to make its INDCs come true through inter-provincial collaboration on emission reduction, but also serve as a reference for fueling further scientific discussions and development of schemes for the allocation of responsibility for carbon emissions at multiple scales within and beyond China. [ABSTRACT FROM AUTHOR]

Highlights • A modification has been made on producer-price based input-output model. • Household carbon footprint is evaluated from purchaser point of view. • Margin-based emission of Tokyo household is evaluated as 0.56 MtC. • Variations in Tokyo household monthly emission is unraveled. Abstract Residential consumption produces carbon emissions through the combustion of primary energy (directly) as well as through the consumption of goods and services (indirectly). To capture the latter indirect emissions, it is common to use producer-price based input-output analysis associated with household monetary expenditures. However, producer-price input-output tables usually do not account for economic transactions and energy flows at the margin. To better understand household indirect emissions from the purchaser perspective, there is a need for a modified input–output modeling approach. To that end, this study attempts to measure the carbon footprint of households in the Tokyo Metropolis, Japan by integrating monthly household consumption inventories into an input–output table of Tokyo. By doing so, the carbon footprint was estimated to be 0.76 tC per household and the total estimated marginal carbon emissions for Tokyo is up to 0.56 MtC. A sectoral breakdown of the total emission coefficients is provided based on purchaser prices and temporal variations in emissions. Findings indicate that within household consumption, Clothing and Services categories contributed most to marginal emissions. The conclusions drawn in this paper are pertinent and policy relevant because they provide relatively accurate information compared to the status quo to inform appropriate policies for guiding residential consumption at the city scale of resolution. [ABSTRACT FROM AUTHOR]

• Suitability of YieldCo for solar PV project is confirmed. • Option value and investment timing are explored for solar PV projects with YiledCo. • Investors should set a low dividend ratio to cope with potential technical progress. • Investors should set a low dividend ratio to cope with potential descending FIT. Many corporations invest in solar photovoltaic (PV) projects to mitigate climate change. However, lack of capital poses a great challenge to the development of the solar PV industry. YieldCos that are a financial tool to securitize assests for renewable energy corporations were introduced to alleviate the financial pressure, but their achievements are unable to meet the expected performance level. Therefore, a question araises: Can YieldCos promote the development of solar PV industry? To solve this issue, this paper utilized a real option method to investigate whether YieldCos can facilitate the deployment of solar PV projects and their impact on decision-making processes. Based on the solution obtained from the real option model, we explore the impacts of decisive factors on the defer option value, investment threshold and expected execution time. The joint impacts of the dividend ratio and other factors are also investigated. We conclude that YieldCos are suitable for solar PV projects, while a high dividend ratio will make investors postpone their investments. In addition, by using the technological level to represent the drift term of investment, we further discuss how decision-makers should react to the potential technological innovation. Moreover, we also discuss the impacts of potential declines in feed-in tariffs on investors' behaviour. We conclude that YieldCos can promote the development of solar PV in China as it can urge investors to invest at an early stage. Related policy recommendations are also proposed. [ABSTRACT FROM AUTHOR]

• This paper systematically reviews the research progress of RS technology applied to various stages of PV system development. • We conclude that RS plays a significant role in PV potential assessment, large-scale data analysis and PV health monitoring. • We discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. Developing solar photovoltaic (PV) systems is an effective way to address the problems of limited fossil fuel reserves, soaring world energy demand and global climate change. The earth observation information provides a promising perspective for estimating the PV energy potential and understanding the status of the PV system development, which is critical for making scientifically sound and cost-optimal sustainable planning strategies. Remote sensing (RS), a versatile technology that captures surface information at various temporal and spatial scales, is now widely applied in different fields of the PV development. However, despite the rapid growth of related research, there is still a lack of comprehensive review on the application of RS to different stages (i.e., planning, site selection, installation, maintenance, etc.) of the PV system development. This paper systematically reviews the research progress of RS technology applied throughout various stages of the PV system development. The reviewed literatures are organized as four major parts: i) PV potential estimation, ii) PV array detection, iii) PV fault monitoring and diagnosis, and iv) other cross-cutting areas where RS can facilitate PV development. We conclude that RS technology can bridge the gap caused by the traditional methods in effective assessment of resource potential, large-scale data analysis and PV health monitoring, which can provide strong support in assisting the planning, management, and decision-making of PV systems. Finally, we discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. [ABSTRACT FROM AUTHOR]

• The PV potential in coal mining subsidence areas of Yangquan City was evaluated. • Effects of different deformation thresholds on the PV potential has been discussed. • The PV power potential in subsidence area can meet 30% of the electricity demand. • The LCOE considering the land price of the subsidence area is RMB 0.3934/kWh. Accurately assessing the photovoltaic (PV) power generation potential in coal mining subsiding regions is of great significance for the transformation of a resource-based city and the goal of carbon neutrality. In this paper, we proposed an assessment method for the PV power potential in coal mining subsidence areas. Both the environmental factors and ground deformation by interferometric synthetic aperture radar were fully considered. Furthermore, the levelized cost of PV electricity in subsidence areas and the benefits were predicted. Yangquan City, Shanxi province was selected as a study case. Results show that the optimal areas for PV power generation under the three-deformation rate ranges of (−40, −10), (−50, −10), and (−60, −10) mm/year in the Yangquan subsidence area are 32.13, 37.89 and 41.22 km2, respectively. The selected optimal PV construction regions agree well with the exiting PV facilities, indicating the effectiveness of the proposed method. The levelized cost of energy is estimated at 0.3934 RMB/kWh. The annual power generation can meet about 33 % of the electricity demand of Yangquan City. This work would guide the construction of PV power generation facilities in the coal mining subsidence areas for the transformation and upgrading of the mineral resource-based cities. [ABSTRACT FROM AUTHOR]

• Developing a framework to estimate solar PV potential of urban fleet-wide electric buses. • Publicly street-view panoramas, GPS trajectory data and DEM data are used as input. • A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas is studied. • Benefits under different driving conditions and operation schedules are evaluated. 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 electricity 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. [ABSTRACT FROM AUTHOR]

• A data set of 4.3 million bicycle records are used as dynamic input for the method. • Propose a dynamic electric fence planning algorithm base on real demand. • Propose an agent-based model to simulate massive bicycle-sharing trips. • Evaluation of potential energy consumption for electric fence implementation. 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. [ABSTRACT FROM AUTHOR]

• Novel fins and nanoparticles to enhance melting process of phase change material. • Efficient melting performance is achieved in tubular thermal energy storage systems. • Charging performance is improved by 91% via novel fins-nanoparticles combination. The present work proposes an integration of a novel fin structure and Al 2 O 3 nanoparticles as an enhancement technology to improve the melting performance of phase change materials (PCMs) for latent heat thermal energy storage systems. A mathematical model of the melting process of PCMs with nanoparticles in a triple-tube heat exchanger is formulated and validated against the experimental data. The effect of different fin layouts and different volume fractions of nanoparticles on the melting process is discussed and reported, including the evolution and deformation of solid–liquid interfaces, the distribution of isotherms, and the time-varying profile of liquid fraction and average temperature over the entire melting process. The results indicate that the melting characteristic is improved by applying the enhanced strategies of novel fins and nanoparticles. Compared to the original structure, the melting time of four different novel fins is reduced by 80.35%, 77.62%, 77.33%, and 80.65%, respectively, which are attributed to the heat transfer enhancement by adding fin configurations to the system. Al 2 O 3 nanoparticles (at 3%, 6%, and 9%) are integrated into the PCMs, and the results show that the melting time is decreased by 13.1%, 15.6%, and 18.8%, respectively. It can be concluded that the combination of fins and nanoparticles is an efficient way to enhance the meting process of phase change materials for thermal energy storage systems. [ABSTRACT FROM AUTHOR]

• This paper introduces a case study of locating the priority to constructing hydrogen facilities in Tokyo based on a multiple dataset fusion method. • The adopted method considers the effect of land use planning and policy regulation on the practice of facility construction. • The complex network theory and massive GPS trajectory records in the capital region of Japan is adopted for computation. • The rationality and feasibility of result is proved and it also provides detailed analysis and guideline for the future scenarios of hydrogen facility locations. Hydrogen socialization has become a promising target in future energy development. Hydrogen fuel cell vehicle is gradually becoming a considerable substitute for fossil fuel vehicles in many countries. As for the promotion of hydrogen fuel cell vehicles, The construction of facilities like hydrogen refueling stations and depots should be strengthened. The research provides a case study of locating the priority of the construction of hydrogenation facilities, based on policy regulation, massive GPS trajectory records, geographical land use data, road network and, POI data. The PageRank value in the complex network theory is employed to estimate the priority of the Land use Unit and policies regulation is referred to to ensure the feasibility of construction. Among 24,595 Land Use Units in sum, 59.78% of them are allowed to build hydrogen facilities. The results show that the available location of hydrogen refueling stations with high priority (we pick the first 100 for analysis) and feasibility in policy are geographically clustered around the city centers and sub-centers. The place with the highest PageRank value of 0.0033 is Haneda airport, which is an important transportation hub. The PageRank values of most of the other first 100 ranked Land Use Units are under 0.001. This indicates that the vehicle transportation network is highly centralized. There is an obvious sequence of priority of hydrogen refueling stations. We furtherly provide sensitivity analysis and it shows that higher-ranked places are spatially clustered. The best hydrogen depot is located near Tokyo Bay with the advantage of low transportation cost of liquid hydrogen, convenient transportation conditions, strong safety assurance. After verification, the results showed consistency with the current hydrogenation facility construction process and demonstrated the potential for future hydrogenation facility construction. [ABSTRACT FROM AUTHOR]

• Effect of thermodynamics model on flow structure considering CO 2 phase transitions. • Ideal gas assumption under-predicts the generation of the liquid phase by 46%. • Redlich-Kwong model predicts a liquid fraction of 28% of the total mass. The separation technology of carbon dioxide (CO 2) is a key step to achieve high efficient carbon capture and storage targets. In the present study, we propose a new concept to remove CO 2 from the offshore natural gas industry, which utilises the combined effect from nonequilibrium condensation phenomena in the supersonic flow and cyclonic separation process from swirling flows. The feasibility study of this concept is evaluated by using computational fluid dynamics modelling. The effect of thermodynamics properties on the phase change process in supersonic flows is analysed in detail. The results show that the supersonic flow can condense 28% CO 2 in a liquid state from the main gas flow based on the real gas model. Nine orders of magnitude differences are observed between the mass generations due to the nucleation process and droplet growth process, which indicates that the droplet growth process contributes more significantly to the mass transfer during CO 2 condensations. The ideal gas model both under-predicts the mass flow rate and the liquid fraction by 25% and 46% compared to the real gas model. This study demonstrates the potential application of the CO 2 separation using the phase change behaviour in supersonic flows. [ABSTRACT FROM AUTHOR]

• Concept of quantile power curve of the wind turbine is proposed. • Quantile loss based neural network algorithm to establish the quantile power curve. • Index to evaluate the performance and uncertainty of a wind turbine is proposed. Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads. [ABSTRACT FROM AUTHOR]

• Typical electric vehicle charging load is simulated considering user demographics. • New probabilistic travel models are established for each user type. • Charging preference, power consumption rate, day type and locations are included. • Typical charging load profiles vary with different types of users. Travel pattern of an electric vehicle (EV) user and the accuracy of their probability distribution models are the key factors affecting the simulation and prediction of EV charging load. Most of the existing works utilized the travel data for all kinds of populations and ignored the influence of people social attributes on their travel pattern, which deteriorates the accuracy of the charging load model. This paper demonstrates that the daily EV charging load profiles vary with different demographic and social attributes by presenting a refined EV charging load simulation method considering people's demographics and social characteristics, e.g. gender, age, education level. First, to improve the fitting accuracy of people travel pattern, new probabilistic models of many defined spatial–temporal variables are established under refined conditions (i.e. location, day type, etc.). Second, additional factors (i.e. charging preference, power consumption rate, etc.) are included to simulate the daily profile of EV charging load based on the refined probabilistic models and Monte Carlo algorithm. Data from the US National Household Travel Survey are used to validate the proposed method. The results show that the user's demographic and social attributes have a considerable effect on the magnitude and peak time of the EV charging load profile, particularly for workdays and workplace. The proposed probabilistic models can improve the accuracy of the data fitting and the charging load simulation. [ABSTRACT FROM AUTHOR]