The booming electric vehicle (EV) charging facilities play a vital role in connecting road transport networks to the urban power grid, as they have internal smart converters with four-quadrant power regulation capability. These converters can provide reactive power to regulate the voltages of urban distribution power grids. Considering the high scarcity of urban spatial resources, there are many restrictions on configuring additional capacitors or other voltage regulating devices. It is of significance to accurately assess the volt/var support capability from the charging facilities on urban grid voltage regulation. This paper constructs a road traffic network model and EV behavior characteristic models for private cars, cabs, and urban service vehicles, respectively. Then, a method for spatial and temporal charging load prediction as well as reactive power flexibility assessment is proposed considering dynamic traffic flow. The assessment results are adopted as boundaries for chargers participating the volt/var regulation of urban power distribution system. The voltage qualification rate indicators are investigated to verify the effectiveness of the proposed regulation method. The results of this paper are helpful for understanding the coupled urban electrified road transportation and power system facilities from a new perspective of volt/var regulation.
Due to their environmentally friendly and energy-saving characteristics, regional distributed energy systems (RDES) have become one of the important ways to enhance renewable energy consumption and promote low-carbon society in recent years. The bottleneck of using peak load of user nodes in the division of energy supply scope at energy stations without full considering the load timing characteristics is addressed in this paper. Furthermore, this paper proposes a collaborative optimization method that considers load timing characteristics. Firstly. the coupling characteristics between energy stations and pipeline network are analyzed. Then a collaborative optimization design model for energy stations and pipeline networks is constructed based on K-means and graph theory algorithms. Subsequently, considering the load timing characteristics to analyze their impact on REDS. The research results indicate that considering the load timing of user nodes can reduce the cooling load demand of energy stations by 19.05% and the total pipeline cost by 8% compared to using peak loads. This paper provides theoretical and technical guidance significance for decision-makers of regional distributed energy systems.
The CO2 methanation (that is, Sabatier reaction), has been considered as a promising way to recycle and utilize CO2. However, the industrial Sabatier process is energy intensive and traditionally powered by fossil fuels. Harvesting solar energy for the conversion of CO2 into solar fuels offers a sustainable and green solution to alleviating energy and environmental issues. In this work, solar-driven CO2 methanation over nickel-based catalysts was investigated under concentrated solar irradiation. A CO2 conversion rate of approximately 87%, with 100% selectivity towards CH4 and a reaction rate of 380 mmol/gNi/h were achieved under concentrated UV-visible-IR irradiation at 350 °C on 15 wt% Ni/Al2O3. The temperature required to achieve maximum CO2 conversion for the nickel-based catalysts were 25 °C lower in the solar-driven process compared to conventional thermocatalytic process. Meanwhile, the apparent activation energy of the solar-driven reaction is 25% lower than that of the thermocatalytic reaction. Moreover, in-situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiments were performed to gain an in-depth insight into the enhancement mechanisms of light in the solar-driven process. This study emphasizes the merits of utilizing concentrated solar energy for driving chemical reactions, revealing the promotion of the reaction by light and offering new insights into the reaction mechanism of solar-driven CO2 methanation over Ni-based catalysts.
The smart PV window, integrated of solar cells and electrochromic coating, is of great significance in the pursuit of building decarbonization, as it combines power generation and radiation modulation capabilities. This study aims to unraveling the untapped potential of smart PV windows in the realm of enhancing building energy conservation and flexibility in hot climates. To achieve this objective, a co-simulation approach utilizing EnergyPlus and Radiance software is employed, along with the implementation of the EMS module to control the coloring states of the smart PV window. The findings reveal that, compared to the Low-E window, the smart PV window with solar radiation control improves the proportion of the useful daylight illuminance by 61.8%, yields a significant reduction in peak load by 72.3%, a decrease in monthly net energy consumption by 53.9%, and an enhancement in energy flexibility by 51.8% in Hong Kong (22.32°N, 114.17°E).
An optimization model is used to design a hydrogen production system under different constraints for CO2 emissions, focusing on Japan as case of study. Two scenarios were considered: Base scenario, focusing on minimizing cost; and WEC scenario focusing on balancing energy use, water use and CO2 emissions. Domestic natural gas alone is not enough to produce 1 Mt-H2/year and electrolysis is used for all CO2 intensities. For low CO2 intensities, Base scenario uses hydroelectricity and geothermal electricity; while WEC scenario uses solar electricity and wind electricity. Zero-emission hydrogen production needs installed capacities for electrolyzers of 8.45 and 30.3 GW in Base and WEC scenarios, respectively.
This work proposes a combined cycle, a gas turbine power generation system and a CO2 cycle power generation system built on the basis of the Ericsson cycle. In this paper, the structure of the combined cycle system is firstly constructed, the temperature-entropy diagram of the gas-CO2 system is plotted, the simulation model is built on the simulation software platform and its accuracy and validity are verified, and the effects of CO2 flow rate, pressure ratio, ambient temperature and gas turbine loading rate on the power generation efficiency and primary energy utilization efficiency of the combined system are studied and analyzed. The simulation results have led to the optimal operating conditions of the gas-CO2 combined cycle, which provides a new technical idea for the future utilization of waste heat energy.
In this study, a microcosm experiment for 120 days exposure of microplastics (MPs) on cold seep sediment was conducted. The results showed that different types and doses of MPs addition negatively affected the anaerobic oxidation of CH4. The CH4 oxidation rates with 0.05 % PA and PET addition were only account for approximately 33% of that for the biotic control group. MPs addition did not significantly influence the archaeal community structures but caused a redistribution of bacterial community compositions. The addition of PA, PE, and PET could significantly increase the relative abundance of Desulfobacterota and made Caldatribacteriota almost undetectable. By the present study, we can have a preliminary understanding of the effects of microplastics on anaerobic oxidation of CH4 and prokaryotic communities in the cold seep sediment.
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.
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.
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.