• Energy Research

Magnetic treatment is a method for improving the cold flowability of waxy oils. Previous studies have predominantly focused on the viscosity reduction resulted from the treatment, with the durability of the magnetic effect neglected, which is crucial for pipeline transportation of the treated crude oil. Therefore, this study focuses on the durability and its mechanism of the magnetic effect of a waxy crude oil under static, low shear, and high shear conditions. A viscosity reduction of 15.7% was achieved under the magnetic treatment condition of the magnetic treatment temperature at 52 °C, magnetic field strength at 0.1 T, and a duration of 1 min. However, the magnetic effect gradually diminished with time elapsing and disappeared in 9 h under static conditions. Shear was found to be beneficial to the preservation of the effect, and a correlation between the viscosity of the sheared treated-oil and the energy dissipation of the shear was found. Microscopic observations, impedance measurements, and x-ray diffraction analysis revealed that exposure to a magnetic field might disperse the charged particles, i.e., resins and asphaltenes, in the crude oil, facilitating their adsorption on the wax particle surfaces, thus enhancing electrostatic repulsion among wax particles and resulting in viscosity reduction. The desorption of the adsorbed resins and asphaltenes from the wax particles and reaggregation lead to the gradual diminishment of the viscosity reduction. Shear might inhibit this reaggregation and thus contribute to the durability of the viscosity reduction.

Abstract In an integrated energy system (IES), the operating state of each energy subsystem changes relatively frequently, which can seriously threaten the security of IES operation. A systematic data-driven approach is proposed for detecting anomalies and analyzing the dynamics of IES vulnerability. Firstly, an anomaly detection method is introduced to determine whether there are anomalies in the system operation. The method can be set up even if the data labels for discriminating the anomalies are unknown, often the cause in practice. Secondly, a method of complex network phase theory is proposed to model information propagation among IES nodes representative of the IES physical entities. Complex network models can then be constructed to describe the system behavior in different operating conditions and over different time horizons. The degree centrality, betweenness centrality, and closeness centrality are used as indications to analyze changes in IES vulnerability. Finally, a method is proposed to identify the critical points of the IES from the point of view of its vulnerability. The new approach is applied to analyze the vulnerability of an IES in Spain. The results show that the proposed methods allow revealing system anomalies, vulnerability and weaknesses. Outcomes from an analysis by these methods can be used by managers to take defensive measures in advance for preventing and mitigating the impact of potential factors and threats on the IES.

The current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a "memory" for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.

Abstract Advanced sensor and communication technologies can make natural gas supply systems smarter than ever before, in both system management and operation. This paper presents the development of a novel data-driven Demand Side Management, whose framework includes demand forecasting, customer response analysis, prediction of dynamic condition of the gas network, quick supply reliability evaluation, multi-objective optimization and decision-making. The aims of this DSM method are to smooth load profiles, improve company profit and enhance system reliability, by means of a dynamic pricing strategy. To verify the effectiveness of the developed framework, a case study is considered, concerning the management of a relatively complex gas supply system, wherein four different pricing periods are introduced for comprehensively testing. The results in the case study show that the DSM framework is able to effectively achieve the targets of peak shaving and valley filling. Besides, it can significantly and stably improve the system efficiency and reliability, for different pricing periods. Finally, pricing period determination is discussed in relation to the features of performance.

Abstract Different energy systems become highly connected to provide better flexibility. However, this change poses new challenges for system management considering the diversity of demands, complexities of the energy networks, uncertainties, etc. This work develops a smart Supply-Demand Side Management method to overcome these challenges. The main objectives of this Supply-Demand Side Management framework are improving system efficiency and smoothing energy load, through flexible supply planning and dynamic pricing. Firstly, the customer response analysis method is proposed by combining the Deep Learning model and the economic model. Then, the energy network simulation model is used to coordinate the Supply-Demand Side Management strategies and the overall energy system capacity. A method is proposed to introduce the compressibility of natural gas in the management framework to offset the uncertain disturbances. Finally, a multi-objective decision method is developed to find the optimal strategy. The results of the application on a typical integrated energy system show that the proposed method can reduce the energy load fluctuation by 4%–8% under different planning horizons, and improve the system efficiency by reducing energy loss and increasing the profitability. The results also present a possibility of the development toward resilient Integrated Energy Systems by managing the buffer capacity of natural gas pipeline networks.

Integrated Energy Systems (IES) is critical for the sustainability in energy system development. But, the disturbances of different uncertainties and their propagation in the system make it hard to maintain a reliable service to the customers. To overcome this problem, a systematic method for the supply reliability analysis in IES is proposed in this work. In this method, the models of different units in the IES are developed considering their working mechanism and target functions. Then their random behaviours are based on their specified stochastic properties. And, their responses are integrated by a developed two-stage optimization model, to simulate the propagation of these events and to analyze the consequences in the system level. Finally, in the risk evaluation part, probabilistic and statistic descriptions are presented, and also a risk measure is adapted from Value at Risk, an important risk measure in financial analysis, for giving solid knowledge of the potential loss of service to the customers in terms of time duration, loss and probability. The effectiveness of this developed method is verified on an assumed IES. The results present its ability to give valuable information for design, extension and management of IES.

As the total mileage of natural gas pipeline network continues to increase, the topological structure of natural gas pipeline network will become more and more complex. The complicated topological structure of natural gas pipeline network is likely to cause inherent structural defects, which have serious impacts on the safe operation of natural gas pipeline network. At present, related researches mainly focused on the safe and reliable operation of natural gas pipeline network, which has become a research hotspot, but few of them considered the complexity of natural gas pipeline network and its potential impacts. In order to understand the complexity of natural gas pipeline network and its behaviors when facing structural changes, this paper studied the robustness of natural gas pipeline network based on complex network theory. This paper drew on the methods and experience of robustness researches in other related fields, and proposed a robustness evaluation method for natural gas pipeline network which is combined with its operation characteristics. The robustness evaluation method of natural gas pipeline network is helpful to identify the key components of the pipeline network and understand the response of the pipeline network to structural changes. Furthermore, it can provide a theoretical reference for the safe and stable operation of natural gas pipeline network. The evaluation results show that natural gas pipeline network shows strong robustness when faced with random disturbances represented by pipeline accidents or component failures caused by natural disasters, and when faced with targeted disturbances represented by terrorist disturbances, the robustness of natural gas pipeline network is very weak. Natural gas pipeline network behaves differently in the face of different types of random disturbances. Natural gas pipeline network is more robust when faced with component failures than pipeline accidents caused by natural disasters.