查看更多>>摘要：The energy industry faces a significant challenge in extracting natural gas from offshore natural gas hydrate (NGH) reservoirs, primarily due to the low productivity of wells and the high operational costs involved. The present study offers an assessment of the feasibility of utilizing geothermal energy to augment the production of natural gas from offshore gas hydrate reservoirs through the implementation of the methane-CO_2 swapping technique. The present study expands the research scope of the authors beyond their previous publication, which exclusively examined the generation of methane from marine gas hydrates. Specifically, the current investigation explores the feasibility of utilizing the void spaces created by the extracted methane in the hydrate reservoir for carbon dioxide storage. Analytical models were employed to forecast the heat transfer from a geothermal zone to an NGH reservoir. A study was conducted utilizing data obtained from a reservoir situated in the Shenhu region of the Northern South China Sea. The findings of the model indicate that the implementation of geothermal heating can lead to a substantial enhancement in the productivity of wells located in heated reservoirs during CO_2 swapping procedures. The nonlinear relationship between the temperature of the heated reservoir and the rate of fold increase has been observed. It is anticipated that the fold of increase will surpass 5 when the gas hydrate reservoir undergoes a temperature rise from 6℃ to 16℃. The mathematical models utilized in this study did not incorporate the impact of heat convection resulting from CO_2 flow into the gas reservoir. This factor has the potential to enhance well productivity. The mathematical models' deviation assumptions may cause over-prediction of well productivity in geothermal-stimulated reservoirs. Additional research is required to examine the impacts of temperature drawdown, heat convection resulting from depressurization, heat-induced gas pressure increment, and the presence of free gas in the formation containing hydrates. The process of CH_4-CO_2 swapping, which has been investigated, involves the utilization of geothermal stimulation. This method is highly encouraging as it enables the efficient injection of CO_2 into gas hydrate reservoirs, resulting in the permanent sequestration of CO_2 in a solid state. Additional research is warranted to examine the rate of mass transfer of CO_2 within reservoirs of gas hydrates.
查看更多>>摘要：Abuse of Lithium-ion batteries, both physical and electrochemical, can lead to significantly reduced operational capabilities. In some instances, abuse can cause catastrophic failure, including thermal runaway, combustion, and explosion. Many different test standards that include abuse conditions have been developed, but these generally consider only one condition at a time and only provide go/no-go criteria. In this work, different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure. Vibrational loading is chosen to be the consistent type of physical abuse, and the first group of cells is cycled at different vibrational frequencies. The next group of cells is cycled at the same frequencies, with multiple charge pulses occurring during each discharge. The final group of cells is cycled at the same frequencies, with a partial nail puncture occurring near the beginning of cycling. The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities. The cells with vibration only experience an increase in internal resistance by a factor of 1.09-1.26, the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16-1.23, and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity. However, the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83-22.1, and each cell reaches its end-of-life capacity within 50 cycles. Overall, the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.
查看更多>>摘要：Taking an industrial park as an example, this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources (DERs). The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data. To predict and analyze the load growth of the industrial park, an improved back-propagation algorithm is employed. Furthermore, the study classifies users within the industrial park according to their specific power consumption and supply requirements. This user segmentation allows for the introduction of three constraints: node voltage, wire current, and capacity of DERs. By incorporating these constraints, the study constructs an optimization model for the distribution network in the industrial park, with the objective of minimizing the total operation and maintenance cost. The primary goal of these optimizations is to address the needs of DERs connected to the distribution network, while simultaneously mitigating their potential adverse impact on the network. Additionally, the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.
查看更多>>摘要：Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons. On this basis, sample entropy is used to calculate the complexity of each component and reconstruct them into trend components, oscillation components, and random components. Then, these three components are input into the LSTM neural network, respectively. Combined with the predicted values of the three components, the overall power prediction results are obtained. The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57% to 9.46% than other traditional models, which indicates the reliability and effectiveness of the proposed method for power prediction.
查看更多>>摘要：To enhance system stability, solar collectors have been integrated with air-source heat pumps. This integration facilitates the concurrent utilization of solar and air as energy sources for the system, leading to an improvement in the systems heat generation coefficient, overall efficiency, and stability. In this study, we focus on a residential building located in Lhasa as the target for heating purposes. Initially, we simulate and analyze a solar-air source heat pump combined heating system. Subsequently, while ensuring the system meets user requirements, we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system. Through this analysis, we determine the optimal installation angle and collector area to optimize system performance.
查看更多>>摘要：To solve the problem of residual wind power in offshore wind farms, a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power. By studying the mathematical model of wind power output and calculating surplus wind power, as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank, an innovative capacity optimization allocation model was established. The objective of the model was to achieve the lowest total net present value over the entire life cycle. The model took into account the cost-benefit breakdown of equipment end-of-life cost, replacement cost, residual value gain, wind abandonment penalty, hydrogen transportation, and environmental value. The MATLAB-based platform invoked the CPLEX commercial solver to solve the model. Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province, the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained. Under the calculation scenario, this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year. It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power. Additionally, it could produce 66,500 kg of green hydrogen from wind power, resulting in hydrogen sales revenue of 3.63 million RMB. It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB. The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB. These results verified the feasibility and rationality of the established capacity optimization allocation model. The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.
查看更多>>摘要：Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions. Unlike conventional turbines, Savonius turbines employ S-shaped blades and have simple internal structures. Therefore, there is a large space for optimizing the blade geometry. In this study, computational fluid dynamics (CFD) numerical simulation and genetic algorithm (GA) were used for the optimal design. The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results. The weighted objective function was constructed with the maximum power coefficient C_p and the high-power coefficient range R under multiple working conditions. GA helps to find the optimal individual of the objective function. Compared the optimal scheme with the initial scheme, the overlap ratio β increased from 0.2 to 0.202, and the clearance ratio e increased from 0 to 0.179, the blade circumferential angle y increased from 0° to 27°, the blade shape extended more towards the spindle. The overall power of Savonius turbines was maintained at a high level over 22%, R also increased from 0.73 to 1.02. In comparison with the initial scheme, the energy loss of the optimal scheme at high blade tip speed is greatly reduced, and this reduction is closely related to the optimization of blade geometry. As R becomes larger, Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions. The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners.
查看更多>>摘要：The N-l criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks. However, the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners. To address this issue, we propose a fast N-l verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling. Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis. We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP. We also take into account the characteristics of medium voltage distribution networks after a line failure and select the feeder section at the exit of each substation with a high load rate to improve the efficiency of N-1 analysis. We validate our approach through a series of case studies and demonstrate its scalability and superiority over traditional N-l analysis and heuristic optimization algorithms. By enabling online N-1 analysis, our approach significantly improves the work efficiency of distribution network planners. In summary, our proposed method provides a valuable tool for distribution network planners to enhance the accuracy and efficiency of their N-1 analyses. By leveraging the advantages of CIM file data analysis and MILP-based mathematical modeling, our approach contributes to the development of more resilient and reliable electric power distribution networks.
查看更多>>摘要：With the continuous development of new energy generation technology and the increasingly complex power grid environment, the traditional black start scheme cannot meet the requirements of today's power grid in order to ensure the stable operation of the power system can be restored quickly in the face of large power outages, so a more complete black start scheme needs to be developed to cope with the new power system. With the development of energy storage technology, the limitations of the traditional black-start scheme can be solved by new energy farms with energy storage configuration. Therefore, this paper investigates the problems faced by black-start, the key technologies of energy storage assisted new energy black-start, and introduces the research related to new energy black-start technology to provide reference for future research and application of new energy black-start.
查看更多>>摘要：With the goal of "carbon peaking and carbon neutralization", it is an inevitable trend for investing smart grid to promote the large-scale grid connection of renewable energy. Smart grid investment has a significant driving effect (derivative value), and evaluating this value can help to more accurately grasp the external effects of smart grid investment and support the realization of industrial linkage value with power grid investment as the core. Therefore, by analyzing the characterization of the derivative value of smart grid driven by investment, this paper constructs the evaluation index system of the derivative value of smart grid investment including 11 indicators. Then, the hybrid evaluation model of the derivative value of smart grid investment is developed based on anti-entropy weight (AEW), level based weight assessment (LBWA), and measurement alternatives and ranking according to the compromise solution (MARCOS) techniques. The results of case analysis show that for SG investment, the value of sustainable development can better reflect its derivative value, and when smart grid performs poorly in promoting renewable energy consumption, improving primary energy efficiency, and improving its own fault resistance, the driving force of its investment for future sustainable development will decline significantly, making the grid investment lack derivative value. In addition, smart grid investment needs to pay attention to the economy of investment, which is an important guarantee to ensure that the power grid has sufficient and stable sources of investment funds. Finally, compared with three comparison models, the proposed hybrid multi-criteria decision-making (MCDM) model can better improve the decision-making efficiency on the premise of ensuring robustness.
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