• Energy Research
• other engineering and technologies close
• US close
• Southeast University close

Interest in the highly efficient energy hub (EH) model has been growing despite the high computational requirements of planning for a multi-energy, multi-device operation. To address both the device size limitation and the multi-scenario issue, we propose a new solution methodology for solving the EH planning problem. In the method, the decision variables are device sizes. First, a dimension reduction technique is proposed to address the curse of dimensionality based on the correlation of unknown variables such as the capacities of different devices in an EH. Second, to avoid local convergence, a solution method called the variable-sized unimodal searching (VUS) approach is proposed to assure a global optimal planning scheme for the one-dimensional non-convex optimization model obtained from the preceding dimension reduction process. The case study indicates that the proposed approach has a higher computing efficiency than the Benders decomposition (BD) algorithm to deal with a scenario-based stochastic planning problem with a large number of scenarios. Thus, the effectiveness of the EH planning approach is verified.

Abstract Effective thermal conductivity (ETC) is an important parameter in packed-bed systems and is affected significantly by packing structure. Heat flow through a packed bed can be divided into three parallel paths: the solid-fluid-solid path, the solid-solid path and the fluid path. The models to evaluate ETC in packed beds for the first two paths were previously developed. In the present work, a comprehensive model to consider the heat transfer through the voids of a randomly-packed bed is further developed using the Delaunay tessellation. The results show that heat conduction through the fluid-filled voids becomes significant when the solid-to-fluid conductivity ratio decreases to a certain level (

Abstract Cetyl palmitate (CP) was thermally synthesized using binary 1-hexadecanol (HD) and palmitic acid (PA) mixture as the starting materials in nitrogen with the absence of any catalyst. The molecule structure, thermal property, thermal stability and reliability of as-prepared cetyl palmitate was studied by 1H nuclear magnetic resonance (1H NMR), Fourier transformation infrared (FT-IR) spectrometer, differential scanning calorimetry (DSC) and thermogravimetric analyzer (TGA), respectively. 1HNMR and FT-IR confirmed the as-prepared product under 350 °C for 30 min was CP with high purity. According to the DSC measurements, the onset melting temperature and latent heat of fusion for the as-prepared CP was 52.3 °C and 180.9 J/g, respectively. Results showed that the synthesized cetyl palmitate was favorable used as phase change material (PCM) owing to its desirable thermal properties, excellent thermal stability and reliability. Due to the low thermal conductivity of CP, it was impregnating into nickel (Ni) foams with different pore sizes to make CP/Ni foam composite PCMs (CPCMs). Compared with pure CP, thermal conductivity of CP/Ni foam (70, 90, and 110PPI) CPCMs were increased by 1.88, 2.02 and 4.86 times, respectively. The CP/Ni foam CPCMs displayed appropriate thermal properties for latent heat energy storage applications.

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.

Stabilization of road aggregate using emulsion bitumen is a common alternative when constructing and/or rehabilitating low volume pavements. The engineering behavior of the Emulsion Aggregate Mixture (EAM) is primarily influenced by aggregate gradation, properties of bitumen emulsion, environment temperature, and moisture content. In this study, a well-graded crushed limestone was treated with SS-1hp type anionic emulsion bitumen. Several laboratory tests including gradation, Atterberg limits and compaction, i.e. moisture-density, were conducted on the virgin as well as emulsion stabilized base aggregate. According to a special curing procedure that was followed, the level of moisture susceptibility could be adequately specified for the EAM samples. Performance tests, such as indirect tensile strength and permanent deformation, and a new advanced directional resilient modulus test were conducted to evaluate the engineering properties of dry and wet cured EAM and virgin aggregate specimens. The findings from this study indicated that emulsion stabilization could adequately improve the bearing capacity of the pavement base/subbase by increasing the strength and modulus properties and reducing the tendency to deform under applied traffic loading.

Abstract As the most important part of machining system, machine tools are widely used in industry, and the resource consumption and environmental problems caused by their use are becoming more and more serious. For promoting energy efficiency of machining system, various methods have been developed based on statistical approaches with design of experiments. However, the methods cannot be easily utilized when the optimization target or machine tool design is modified because the optimal solution is determined based on the experimentally measured data. An effective and practical monitoring method is not only the precondition of energy efficiency promoting, but also the basis of the integration with industry 4.0 applications. This paper presents a modelling approach for energy efficiency of machining system which can be used to monitor energy efficiency on-line and without any extra cutting force dynamometer. The approach is proposed based on the energy consumption mechanism of machining system and the dynamic factors (temperature, lubrication, etc.) of machining processes are considered, so that it has a quite high accuracy under various machining conditions. The case study and validation have been carried out and the experimental results indicate that the accuracy of the approach can reach over 90% under various processing conditions. Finally, the static errors and dynamic errors which may affect monitoring accuracy are discussed.

One important characteristic of current and voltage signals is their dynamic behavior which is usually ignored by the traditional phasor measurement algorithm, and inaccurate phasor estimates will be attain by the traditional algorithm because of dynamic behavior such as power system oscillations. The derivatives of Taylor expansion are employed to express dynamic signals and their coefficients are regarded as the constraints for an optimization function, which will generate a sequence to modify the coefficient of the filter. Not only will the physical meaning of the derivative coefficients be described in detail within this paper, but also an efficient phasor estimate algorithm, that has good performance under dynamic conditions, will be proposed. Theoretical analysis, simulation and analysis of real data demonstrate that the performance of the algorithm proposed by this paper is superior to traditional phasor estimate algorithm based on a FIR filter in the presence of oscillations and frequency deviations.

Abstract Plug-in hybrid electric buses (PHEBs) have the potential to satisfy both the fuel efficiency and the driving-mileage under complex urban traffic conditions. However, the optimal charge and discharge management is still a pivotal challenge of energy management for the inherent uncertainty in driving conditions. The common reference state-of-charge (SOC) profile based methods are limited by the adaptiveness which restricts the economic performance of on-line energy management systems. Promisingly, reinforcement learning based energy management strategies exhibited the significant self-learning ability. However, for PHEBs, the sparse rewards by the long-term SOC shortage make the strategies easily trick into the local optimal solution. The work presented in this paper concentrates on combining battery power reduction in the form of conditional entropy into reinforcement learning based energy management strategy. The proposed method named adaptive policy optimization (APO) introduces a novel advantage function to evaluate energy-saving performance considering long-term SOC dynamic, and a Bayesian neural network based SOC shortage probability estimator is utilized to optimize the energy management strategy parameterized by a deep neural network. Several experiments in a standard driving cycle demonstrate the optimality, self-learning ability and convergence of the APO. Moreover, the adaptability and robust performance get validated over the real bus trajectories data. With the comprehensive experiments in this paper, the proposed model exhibits enhanced fuel economy and more suitable SOC planning in comparison with the existing energy management strategies. The results indicate that APO respectively outperforms the compared online strategies by 9.8 % and 2.6 % and reaches 98 % energy-saving rate of the offline global optimum.