Abstract

In urban driving scenarios, Fuel cell hybrid vehicle (FCHV) needs to face mixed driving scenarios while taking into account multiple objectives such as traffic safety, driving comfort and energy efficiency. The trade-off problem of multi-objective co-optimization is ignored for cruise control and energy management strategy (EMS), which are easily affected by the predefined driving cycle constraints. In this paper, the integrated control framework of cooperative adaptive cruise control (CACC) and EMS is established, and the chaotic multi-objective particle swarm optimization algorithm (CMOPSO) is adopted to optimize the control parameters of the integrated framework. The results show that compared with the control strategy optimized based on WLTC conditions, it can reduce the integrated energy consumption (4.1%) and the following safety (58.1%) while meeting the driving comfort requirements. Compared with the weighted sum method, the proposed method can achieve the balance of multiple optimization objectives.

Abstract

Anaerobic oxidation of methane (AOM) coupled with sulfate reduction (SR) is an important process in cold- seep ecosystems to prevent methane emitted from the seafloor to the atmosphere. However, how the temperature and sulfate in seep habitats drive the SR- AOM process and further affect the methane cycles remains unknown. We simulated the habitat differences in sulfate and temperature using a high-pressure bioreactor system with a fed-batch mode for in vitro incubation of seep sediment. We found that SR-AOM was significantly affected by increased temperature (15 °C). The AOM activity was increased by sulfate supply (+15 mM), even at a low temperature (8 °C). Our findings provide a new insight into the methane budget in cold seeps.

Abstract

This study presents an efficient framework for locating and classifying faulty Photovoltaic (PV) panels from Unmanned Aerial Vehicle (UAV) thermal infrared images. First, aerial triangulation based on photogrammetry is used to obtain thermal infrared images of PV panels with coordinate information, then, individual PV panels are segmented based on High-Resolution Network (HRNetV2-W32), finally, the panels are fed into residual net (ResNet-50) to classify the fault types. Results showed that the panel segmentation accuracy reaches 98.54%, the classification accuracy reaches 88.74%, and the coordinate error is better than 0.033m.

Abstract

In this study, Yongren County, Yunnan Province was selected as the study case, and a photovoltaic (PV) power potential assessment system based on Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) method was used to calculate the PV power potential in mountainous areas and to estimate the levelized cost of electricity for PV power generation in mountainous areas. The results show that the ordinal priority approach (OPA)-MCDM is the best among the four different multi-criteria decision methods, and the selected optimal PV construction area fits well with the existing PV facilities, which demonstrates the effectiveness of the proposed method. The PV power generation potential is about 7861.953 million kwh, and the levelized cost of electricity is 0.3963 RMB/kWh. The estimated annual power generation capacity can meet the social power demand of Chuxiong Prefecture.

Abstract

Energy over-consumption, water shortage, and intensive carbon dioxide (CO2) emissions are the three vital environmental issues in the context of global climate change. China is promoting the coordinated development of the Beijing-Tianjin-Hebei region as a national strategy project; this makes it more urgent to integrate water conservation, energy structure transition, and CO2 reduction within process of high-quality development and green transformation.
This study uncovered energy-water nexus patterns within Beijing-Tianjin-Hebei, and carried out dynamic simulation research from the perspective of industrial synergy to facilitate optimization roadmaps. A Beijing-Tianjin-Hebei Energy-Water Nexus Simulator is innovatively developed, which is based on the theories of input and output, system dynamics, and dynamic multi-object planning; and is committed to revealing the regional energy and water consumption, carbon emission, and economic system developing trends. Through policy mix including industrial structure adjustment, collaborative development, and environmental efficiency improvement, multiple scenarios’ comparison can provide the optimization path of Beijing-Tianjin-Hebei during 2020-2035 under overall control of muti-regional energy-water and carbon targets.
The dynamic optimization decision-making model and policy simulator developed in this study is expected to be widely used in the sustainable development planning of Chinese inter-regional economy, and provide scientific support for regional coordinated development and ecological civilization construction.

Abstract

As the world’s largest energy consumer and carbon emitter, China accounted for 26.5% of global energy consumption and 31% of carbon dioxide (CO2) emissions in 2021; Shandong province as a heavy industry intensive base in China, confronted with the most imminent task of energy conservation and emission reduction. With pursue on “carbon peak and neutrality”, Shandong province need to transfer the high-carbon energy system fundamentally to accomplish the high-quality development. To explore the timetable and roadmap of carbon peak in Shandong province, this research integrates the input-output modelling, system dynamics and multi-objective programming, and innovatively develops the carbon peak model. The model focuses on energy structure transformation and energy efficiency improvement, including incentives for electrification level increase, renewable power (such as wind and solar power) penetration enhancement, and low-carbon technology investment, combined with industrial restructure. A dynamic simulation measure is adopted to predict Shandong province’s economy-energy-carbon development from 2020 to 2035. The research can provide a useful reference for formulating operatable energy management and carbon peak schemes in industrially developed regions such as Shandong province.

Abstract

To analysis China’s provincial low carbon transition under the carbon neutrality goals, this study developed a provincial energy-environment-economy model (China TIMES-30PE). Under carbon neutrality target, each province has a different pathway for achieving carbon neutrality and requires deep decarbonization of their energy systems, with a dominant share of non-fossil energy sources, increasing electrification rates, growing hydrogen consumption and significantly reducing energy intensity. The mismatch between wind and solar resources and electricity demand has resulted in an increasing transmission volume across provinces. Power transmission from the northwest and southwest regions continues to grow, while some central provinces have transitioned from net electricity suppliers to net users. The eastern provinces continue to receive increasing long-distance power transmission and rely on ultra-high voltage transmission lines as backbone channels.

Abstract

In this study, a hydrogen trade module was developed based on the GCAMv6.0 model to investigate the role of hydrogen trade in the low-carbon transition of energy systems, we conducted a study using Japan as an example. The results indicate that hydrogen imports in Japan will replace the local production of gray and blue hydrogen, leading to an increase in hydrogen demand and penetration rate in end-use sectors. Among the import pathways, ammonia shipping is the primary mode. Therefore, Japan should prioritize the development and popularization of hydrogen production, trade, and end-use technologies, and contribute to the expansion of hydrogen imports in the local region.

Abstract

This paper proposes an optimal energy management method for a group of air source heat pumps (ASHPs) in a low-carbon industrial park while considering the cold island effect of the ASHPs. The low-carbon industrial park is a real case in Binhai New District, Tianjin, China where 14 factories are based in. The heating loads of the 14 factories are satisfied by a group of ASHPs that are powered by distributed photovoltaic (PV) generations to reduce the carbon emissions of the whole industrial park. First, an optimal scheduling model for the operation of the group of the ASHPs considering the cold island effect is developed. The unit commitment of the ASHPs within the group is optimized while considering the operational constraints of each ASHP unit. Furthermore, the cold island effect of all the ASHPs is considered as a constraint to avoid the influence of the cold island effect as far as possible and thus keep the ASHPs in their efficient states. Then, the resistance-capacitance (RC) network is used to model the thermal dynamics of the factory buildings in the park. The thermal inertia and the schedules of the employee’s working hours are also considered to explore the flexibility of the factory buildings. Finally, the operator of the industrial park can actively control the group of ASHP units to reduce the heating costs and meet the comfort requirements of the factory buildings. Numerical studies show that the proposed strategy can help to reduce the heating costs, improve the efficiency of the ASHP and reduce the carbon emissions as well for the low-carbon industrial park.

Abstract

Methane and carbon dioxide are major greenhouse gases contributor. CO2 dry reforming of methane (DRM) for syngas production is a promising approach to reducing global CO2 emission and extensive utilization of natural gas. However, the reported catalysts endured rapid deactivation due to severe carbon deposition at high temperature. Here, CO2 reduction by CH4 on hexagonal nano-nickel flakes wrapped by porous SiO2 (Ni@SiO2) catalysts driven by thermal and solar light are tested. High resistance to carbon deposition and reactive activity are demonstrated under focused solar light. Furthermore, the mechanism of light-enhanced reaction reactivity is investigated by Infrared spectroscopy and the activation effect of light is depicted. The light-driven DRM provides a promising method for renewable solar energy conversion and CO2 emission reduction due to the excellent activity and durability.