Knowledge graph, originated from Semantic Web technology, is an emerging technology in the field of artificial intelligence (AI). As miscellaneous data and knowledge accumulate in power and energy system sections, an expert system with cognitive competence based on knowledge graph technologies can assist complex decision-making and quick response in relevant knowledge-intensive tasks. This study introduces the basic concept, key research progress and prospects of knowledge graphs, and their applications in low carbon power and energy systems.
Rooftop photovoltaic (PV) system is a common utilization of renewable energy in Guangzhou with considerable energy saving potential, which not only produces electricity but also has an impact on building energy consumption system. There are still deficiencies in models for quickly assessing the energy saving potential for rooftop PV system, which makes it more difficult for architects to determine the installed area of rooftop PV in the design stage. This article, therefore, aims to establish the energy saving potential model of a typical office building Rooftop PV system in Guangzhou through experiment and simulation. The results revealed that the most energy-saving potential of the rooftop PV system comes from its electricity generation capacity. Especially for high-rise buildings with high-performance air conditioning systems, the impact of the rooftop PV system on the building HVAC energy consumption is almost negligible.
The influence of temperature on the lifetime of lithium batteries (LIBs) is significant, so it is important to fully understand the role of temperature in the aging of LIBs to extend the battery life. Although there are many reviews on the factors influencing the aging of LIBs, there is no systematic analysis of the effect of temperature on the aging mechanism of LIBs. This paper summarized the impact of temperature on aging mechanism. For anode, high temperature would accelerate the growth of SEI; while low temperature mainly results in lithium plating. For cathode, high temperature leaded to electrolyte oxidation and metal oxide decomposition; and low temperature leads to passive layer growth and phase change on the cathode surface, resulting in an increase in impedance. It should be note that, little research was conducted on low temperature. In addition, for electrolyte, the temperature mainly affects its impedance and its stability, and therefore, leading to the capacity degradation.
Thermal runaway of battery leads to a serious consequence, such as explosion, in which the variation of temperature is the key parameter needed to be controlled. Therefore, by using the validated 3D model, this paper discussed the impact of discharge rate and convection heat transfer coefficient on the behavior of thermal runaway, which is caused by the local overheating. Results showed that, a high discharge rate could increase the rate of temperature rise and decrease the triggering time of thermal runaway. It changed from 895 s to 771 s when the discharge rate increased from 0.5 C to 4 C. Increasing the convection heat transfer coefficient was an effective way to mitigate the thermal runaway. Compared to 15 W/(m2·K), the highest temperature of battery could decrease by 40 °C and the triggering time could be delayed by 280 s when the convection heat transfer coefficient was 75 W/(m2·K). The result obtained in this paper could provide guidance to understand the characteristic of thermal runaway.
Blending hydrogen into the existing natural gas pipeline network is regarded as a potential mode in the future. However, the influence of hydrogen on the economic and environmental performance of natural gas pipeline networks remained unclear. This paper established a mathematical programming model to get the optimal operation plan of the pipeline system under different hydrogen blending ratios, and the operation costs and carbon emissions of the hydrogen mixed system are analyzed accordingly. The studied case demonstrated that: (1) The optimization model has significant potential in reducing the economic cost and carbon emission of the system with an average decrease of 11.48%. (2) For every 1% of hydrogen added, the annual operating cost varies from minus $0.73 million to positive $1.67 million, and carbon emission varies from minus 0.38 kiloton to $0.76 kiloton. The proposed optimization model can provide theoretical guidance for the further application of this transportation mode.
Hydrogen has tremendous potential to bridge the energy transformation to a green and sustainable future. Steam methane reforming (SMR) is currently the primary means of hydrogen production, while it suffers from major barriers of high temperatures, high system complexity, and high CO2 emission. To address such challenges, we propose separation-enhanced SMR driven by simultaneous separation of H2 and CO2 to reduce reaction temperature on the premise of ensuring high methane conversion for temperature ranges compatible with commercial solar parabolic trough collectors. Experimental and numerical studies both demonstrate methane conversion of >99% and high-purity hydrogen and CO2 obtained at 400°C. Such low-energy penalty and low-carbon footprint approach shall enable promising solar hydrogen production by further integration with photovoltaic-powered separation and CO2 sequestration.
The utilization of energy storage technology is beneficial to improve renewable energy penetration. A novel compressed carbon dioxide energy storage system is proposed in this paper. A flexible gas holder is applied to store low pressure carbon dioxide in gaseous state. Detailed mathematic model of the novel compressed carbon dioxide energy storage system is established. To investigate the influence of key parameters on system
performance, the parametric analysis is conducted. Results indicate that higher energy storage pressure has a positive effect on increasing system round trip efficiency within certain ranges. Higher isentropic compressor efficiency and turbine efficiency are beneficial to improve the system round trip efficiency. Lower energy storage pressure and higher isentropic efficiency of compressors are effective to reduce input power.
Compressed air energy storage technology is considered as an effective way to solve the intermittency and instability of renewable energy. In this paper, anunderwater compressed air energy storage system is investigated. The thermodynamic model of the system is established to explore the system performance. The parameter analysis is carried out to study the effect of heat exchange efficiency, compressor efficiency, gas velocity in header pipe and offshore distance on system performance. The analysis results show that the increase of heat exchanger efficiency and compressor efficiency is beneficial for increasing system efficiency. Lower gas velocity in header pipe and lower offshore distance could make the system reach higher energy density.
Energy Internet (EI), which deeply integrates digital and new energy technologies, is known to be an effective way to reform the energy system and lower carbon emissions. Current studies on the carbon emission reduction advantages of the Energy Internet mainly focus on the generation of renewable energy, rarely discussing the environmental effects of digital technology. This paper proposed a system dynamics model, which included a thorough evaluation index for EI’s digitalization, to simulate the carbon emissions of China’s energy system and forecast its peak time. The simulation results demonstrate that the construction of the EI could enable China’s energy system to meet the carbon reduction commitment in 2028, with a peak of 11,633 million tons of carbon emissions. These findings might provide a new perspective for quantitative research on the environmental outcomes of the EI and the relationship between environmental improvement and economic growth.
Accurate energy consumption prediction is a prerequisite for effectively dispatching distributed power sources. For a building, due to the frequent fluctuations derived from many dynamic factors,the precise energy consumption prediction is still facing challenges. Existing methods usually only use common recurrent neural networks to predict building energy consumption, consider common recurrent neural networks model does not have the ability to extract spatial features and they have a long-term memory problem,so they have limitations to deal with long term task. To overcome these challenges, in this paper, we propose a hybrid model to predict the cooling consumption of a building.
Our hybrid model has the merits of convolutional neural network and gated recurrent unit in capturing spatial-temporal features. Experiment results show that our hybrid model has the best performance, compared with other methods. The result will benefits managers to make reasonable scheduling of power and equipments.