Based on the integration of dynamic traffic information, environmental temperature, real-time traffic flow, queuing theory, and other methods, a novel deep learning architecture for predicting Origin-Destination traffic flow in urban transportation systems has been developed to forecast the spatial and temporal distribution of electric vehicle charging loads.This method begins by analyzing the impact of various factors, such as urban traffic network data, daily patterns, and weather, on the driving patterns of electric vehicles. It employs a Graph Convolutional Recursive Neural Network algorithm to separately identify the starting and ending points of private cars and taxis.Next, it introduces impedance models for road segments and nodes, which take into account dynamic traffic information, intersection flow, and an air conditioning energy consumption model that considers environmental temperature and real-time vehicle speed. The method utilizes Graph Convolutional Network to extract spatial features of traffic nodes and their neighboring nodes. Time-related features are extracted using P-Prophet, creating a traffic intersection traffic flow prediction model.To optimize the minimum cost travel routes for electric vehicles, it improves the Floyd dynamic algorithm using a sparse graph optimization strategy, thereby simulating the driving behavior of electric vehicle users. Additionally, it uses K-means clustering to analyze potential charging preferences of electric vehicle users, providing insights into characteristic charging behaviors among typical urban electric vehicle users.
This study employs deep reinforcement learning algorithms, including Deep Q-Network, Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic, to control the air conditioning system of electric vehicles to improve thermal comfort and reduce energy consumption. Additionally, random adjustments to environmental temperature and solar radiation intensity during the training process are made to enhance the algorithms’ applicability. The results demonstrate that these algorithms significantly reduce energy consumption while maintaining thermal comfort. Notably, the Deep Deterministic Policy Gradient algorithm achieves an impressive 37.6% reduction in energy consumption. Comparative analysis among the algorithms reveals that Deep Q-Network, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient exhibit relatively stable control behaviors. In contrast, the Soft Actor-Critic algorithm’s compressor control curve exhibits more significant fluctuations, potentially leading to mechanical wear. Deep Q-Network, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient algorithms consistently demonstrate effective thermal comfort control and energy-saving performance in various operating conditions.
Injecting CO2 into geological formations can effectively slow down CO2 emissions. However, during the injection process, the physical properties of CO2 in the wellbore and reservoir change significantly, which will greatly affect the CO2 geological storage effect, and even cause injection difficulties or leakage, and the risk of hydrate formation. Therefore, a wellbore-reservoir-thermo-hydro-mechanical (WR-THM) fully coupled model is established. The model considers the heat transfer between the wellbore and the surrounding formation, the coupling between the wellbore and the target reservoir, and the THM coupling process of various fluids in the reservoir. The CO2 storage effect and possible risks under different engineering parameters were studied. The research results show that when the injection temperature is -10Â°C, there is a risk of hydrate formation at the bottom hole. Increasing the injected mass flow will greatly reduce the CO2 injection capacity. Low-permeability reservoirs are not easy to inject, and CO2 seeps uncontrollably into cap rock and base rock. The research results provide theoretical support for the safe and efficient geological storage of CO2.
In order to understand the ignition risk and fire hazard of LOX/kerosene leakage at space rocket launching sites, the experimental methods of kerosene ignition in oxygen-rich atmosphere will be established focusing on fire safety issues of kerosene. The mixture of oxygen and kerosene vapor was formed in a semi-closed experimental space by heating rocket kerosene, while the ignition test of rocket kerosene under different initial kerosene temperature and oxygen concentration were carried out by electric ignition. The results showed that the ignition risk of kerosene vapor increased with the increase of oxygen concentration, while the ignition energy required for kerosene ignition decreased with the increase of oxygen concentration. The ignition energy required for ignition of kerosene had a better linear relationship with the reciprocal of the kerosene initial kerosene temperature. Under the condition of same oxygen concentration, the higher initial kerosene temperature, the smaller the ignition energy required for ignition of kerosene. By theoretical analysis of the relationship between kerosene ignition energy(E) and ignition time(Ï„), oxygen concentration (x) and initial oil temperature(T), the ln(E/T2) and ln(t) can be correlated well with the form of (k/T+C(x)), where k is a constant and C(x) is also a constant related to the oxygen concentration. The research results can provide key scientific data and models for the fire risk assessment of LOX/kerosene leakage.
CO2 owing to its excellent heat transfer properties and environmental sustainability, has been extensively explored for integration into future energy systems. CO2-based combined cooling and power cycles exhibit great potential for diverse multi-energy complementarity scenarios, offering high source-sink matching and adjustability. However, they face challenges related to stringent condensation requirements and suboptimal energy conversion. In response, CO2/R32 mixture systems have emerged as a promising solution to enhance efficiency and condensation performance. This paper presents preliminary experimental research results on the CO2/R32 mixture combined cooling and power cycle. It showcases the performance of CO2/R32 system under varying heat source conditions and its impact on power and cooling sub-cycles. CO2/R32 demonstrates excellent thermal matching performance and outstanding potential for utilizing medium and low-temperature thermal energy. The CCPC system can simultaneously produce 536W of predicted net power and 1550W of cooling capacity with an energy efficiency of 13.91% and a COP of 4.71. Additionally, the reduction in critical pressure and the increase in critical temperature have effectively improved the operating pressure and condensing conditions of the system. Under the same condensing conditions, the condensing pressure has decreased from 6.7MPa to 3.0MPa, and the operable condensing temperature range has significantly expanded.
In order to improve the thermal management performance of photovoltaic modules, extend the thermal management time and realize the full utilization of photovoltaic waste heat. In this study, an integrated PV thermal management system with coupled phase change and water cooling and heat dissipation was established. Using electric heating to simulate photovoltaic heat flow, the enhancement effects of phase change cooling and phase change air cooling in thermal management were studied and the existing problems were analyzed. The results show that phase change cooling can reduce the hot surface temperature, but the introduction of copper foam would shorten the thermal management time. Phase change and air cooling can improve thermal management time but is seriously affected by ambient temperature changes, and photovoltaic waste heat will be wasted. On this basis, the effect of phase change and water cooling system on PV thermal management is studied and the waste heat is fully utilized. The results show that the effective thermal management time reaches 3550 s when the water level of the water tank is 7 mm and the ambient temperature is 30 oC, which can satisfy the effective thermal management under 1 h high intensity light and the greater the water quantity, the longer the thermal management time.
Under the “Dual-Carbon” targets, hydrogen production powered by renewable energy and hydrogen direct reduction offer approaches to integrating a high proportion of renewable energy and catalyzing the steel industry’s transition towards a low-carbon footprint. The hydrogen-based steelmaking system (HBSS) presents a multi-energy interaction, encompassing processes from hydrogen production and ironmaking to the final steelmaking processes. Additionally, the high investment and operational expenses necessitate that decision-makers prioritize enhancing the system’s economic efficiency to ensure its long-term viability and effectiveness. In this study, we first introduced a multi-period optimization model for HBSS, aiming to reduce the levelized cost of steel (LCOS) from both the investment and operational aspects. Then, the rolling-horizon approach has been used to overcome computational infeasibility for large mixed-integer linear programming problems by solving the problem periodically, including additional information from proximately following periods. We further compared it with the single-period and forward-looking approaches, indicating that the optimal result of LCOS varied from $406 to $520 for the different approaches. It proves that the rolling-horizon approach can lead to an economics-better solution than the single-period approach and is only a few percent away from the forward-looking approach.
Water electrolysis systems accelerate the green transition in Danish power, notably via mature alkaline electrolyzers integrated with renewables. The demand for clean and sustainable energy sources has spurred significant interest in electrolysis for hydrogen production. Electrolyzers play a pivotal role in this context, and efforts to scale up their operation are central to meeting the growing hydrogen demand. This study develops MATLAB/Simulink models of various-sized alkaline electrolyzers to design large hydrogen plants, assessing their scalability and economic viability.
This work presents a methodology to evaluate technology-specific hurdle rates for energy system optimization models. Hurdle rates are usually assumed through educated guesses by energy system modellers, while they are estimated here by adopting the weighted average cost of capital methodology where possible and collecting data from the available literature in the other cases. The methodology is applied to the TEMOA-Italy open-source model: first, the updated hurdle rates are compared to the original model values; then, the effects of such an update are deepened in a base scenario. The results suggest that hurdle rates do not significantly affect the optimal system configuration (and the competition between the alternative technologies), while they vary the computed discounted costs of the technologies selected by the model.
Hydrogen, especially when stored within Metal Hydride (MH) containers, exhibits significant potential for energy storage. One of the primary challenges associated with using metal hydrides is their efficient thermal management. This can be addressed by incorporating an appropriate Phase Change Material (PCM) into the MH container, eliminating the need for additional active systems.In this study, we perform numerical assessments on the performance of hybrid MH-PCM storage systems with varying aspect ratios. Numerical simulations indeed play a crucial role in designing, managing, and improving this technology. This evaluation aids in identifying instances where multi-dimensional phenomena can be disregarded, allowing simplified 1D models to be used. We evaluate different cylindrical layouts of the hybrid MH-PCM hydrogen storage system concerning process duration, temperature distributions, and power. We identify a critical aspect ratio for the canister, beyond which relatively straightforward one-dimensional simulations can be employed without compromising outcomes. Moreover, we highlight that the overall time required for absorption and desorption reactions is notdramatically influenced by the discretization method, suggesting the practicality of relying on one-dimensional models especially when process evolution is not the focal point.