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.
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.
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 â€œcarbon peak and neutralityâ€, 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 provinceâ€™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.
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.
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.
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 employeeâ€™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.
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.
The objective of the paper is to develop a model and a conceptual framework for noise behaviour monitoring and mitigating climate change by imposing global environmental taxes and externalities on â€œNoise Behaviourâ€ through the monarchy of Concordia. The paper has presented the modelling of noise behaviour taxes and tariffs and climate change mitigation in the monarchy of Concordia. The Design of Experiment (DoE) for modelling â€œNoise Behaviour Taxesâ€ with the statistical method of â€œResponse Surface Methodology and Robust Parameter Design (Taguchi Technique)â€ has been utilized. The concepts of energy efficiency and societal well-being are presented by defining energy intensities and noise.
Integrating a variety of renewable energy and waste heat resources, the district energy bus system (EBS) has become a core infrastructure for realizing building heating and cooling in the context of the current low-carbon energy transition. This paper focused on office buildings in hot summer and cold winter areas of China, and developed parameter sensitivity research based on correlation analysis and regression analysis with selected parameters. The results show that the sensitivities are different among the input and output parameters. In general, based on correlation analysis and regression analysis, surface water temperature, outlet temperature of surface water coils are the variables with the highest correlation degree with the output parameters. In addition, the cooling load has a higher correlation with the output parameters than the heating load. The results of this paper can be used for parameter selection in system optimization to help confirm the priority of parameters and reduce the uncertainty of model input and output parameters.
There is a rising need for accurate battery state of health (SOH) diagnosis in electric vehicle maintenance and second-life evaluation. However, existing methods suffer from the transition from cell-level tests to real-world vehicle applications due to the ignorance of incorporating laboratory tests with large-scale, time-varying field data. This paper proposes a framework combining the system-level capacity calculation and cell-level decoupling experiment for battery system capacity diagnosis. A modified regional capacity calculation method for online applications is presented, and the regional capacity of the battery under various temperatures and SOHs is experimentally determined to decouple various working conditions. This work highlights the opportunity to integrate laboratory test data to leverage unlabelled field data for capacity diagnosis while revealing the characteristics of battery capacity under different working conditions.