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The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

The reclosing time of a transmission line has a distinct influence on system stability. A new method of calculating the optimal reclosing time based on the transient energy function (TEF) is presented in this paper. It has been shown that an optimal reclosing time can be used to set the autoreclosing device so that the system stability or power-transfer capability can be significantly improved. Though power-system TEF is based on classical models, the results from simulation studies of a real system (46 generators and 230 buses) with complex models show that the optimal reclosing time calculated using TEF can be used effectively.

Multi-layer microgrids cluster (MLMC) has been formed in some certain areas with microgrids (MGs) merging into smart distributed system. It relies on an energy management system (EMS) to manage and schedule the optimal power flow among MGs and public power system in a stable and balanced way. Compared with EMS for single MG, energy management optimization for MLMC is much more complex that the interactions with other MGs require to be considered. In this paper a multi-time-scales EMS is proposed to minimize the total operating cost of MLMC. It consists of two parts: day-ahead static scheduling and real-time dynamic compensation. The day-ahead scheduling is an economic optimization based on the forecasting data within 24 hours. The real-time dynamic compensation is presented to compensate scheduling signal based on the real-time operating information, which is caused by the predictive error. Furthermore, charge-discharge protections of energy storage system (ESS) for each single MG are taken into account. A general-structure MLMG is studied as an example, and this problem is solved by mixed integer linear programming (MILP). The effectiveness of proposed EMS is verified in the MG Research Laboratory in Aalborg University, Denmark. The hardware-in-loop simulation results show the proposed EMS has a good convergence and performance in grid-connected mode.

Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.

AbstractGenerally, highly efficient organic solar cells require both a high open‐circuit voltage (VOC) and a high short‐circuit current density (JSC). Reducing the energy loss (Eloss) is an effective way to achieve a high VOC without compromising the photocurrent, which is ideal for enhancing the power conversion efficiencies (PCEs). Herein, a new chlorinated nonfullerene acceptor (ITC‐2Cl) with chlorinated thiophene‐fused end groups is developed. In comparison with the unchlorinated counterpart (ITCPTC), the introduction of Cl improves not only the electronic properties by redshifting the absorption spectra and deepening the lowest unoccupied molecular orbital energy levels, but also the molecular packing and thus thin‐film morphology. The PM6:ITC‐2Cl‐based device yields a significantly higher PCE (13.6%) with a lower Eloss (0.67 eV) than the ITCPTC‐based device (PCE of 12.3% with Eloss of 0.70 eV). More importantly, compared to the archetypal nonfullerene acceptors such as IT‐4F (PCE of 12.9% with Eloss of 0.73 eV) and IT‐4Cl (PCE of 12.7% with Eloss of 0.76 eV), the ITC‐2Cl‐based device shows a higher PCE and a lower Eloss. These results demonstrate that the chlorinated thiophene‐fused end group is a promising candidate for a high‐performance nonfullerene acceptors with low energy loss.

SummaryCoulomb Counting (CC) method plays an important role in the state of charge (SOC) estimation theory of lithium‐ion batteries, and a lot of improvement and optimization strategies are based on it. With the increasing demand for precise management of lithium‐ion battery systems, the performance of the traditional CC method is no longer suitable for more complex working conditions. First, the battery aging, extreme temperature, and high‐rate discharging were considered as the main influencing factors which limit the SOC estimation accuracy of the CC method, and the performance degradation mechanism of the traditional CC method under the influence of the above factors are experimentally analyzed, especially the change of battery total dischargeable capacity after aging, and the change rules of key parameters in the CC equation are analyzed. Then the initial SOC and the total dischargeable capacity of the CC method are modified and estimated respectively to realize the accurate estimation of SOC under complex working conditions, especially the accurate SOC estimation during the whole life cycle of lithium‐ion batteries. The experimental results show that the improved CC method can effectively deal with complex working conditions, and the comprehensive estimation accuracy of SOC is within 3.6%.

Since the renewable energy penetration in electricity system is getting more and more increased, new strategy is desired to help accommodate the fluctuated renewable energy. This paper proposes a transient-state gas flow model based unit commitment of integrated electricity and gas system (IEGS) with a decomposition optimization scheme adopted to satisfy the privacy protection requirements of system operators. The problem is solved decomposedly in an iteration procedure and the convergence of solution is guaranteed as well. Case study is conducted on a 6-bus 7-node system to verify the effectiveness of renewable energy accommodation capability. Besides, comparison between centralized optimization and the proposed decomposed optimization demonstrates that the adopted decomposition optimization scheme can find the global optimal integer variables, which further verify the validity of the proposed strategy.