The mass roll-out of electric vehicles substantially contributes to reducing fossil fuel consumption and environmental emissions. Meanwhile, the large fluctuation, strong uncertainty and multiple coupled impact factors of plug-in electric vehicle charging significantly challenge the existing load forecasting approaches. Unlike the conventional load, the fluctuation of plug-in electric vehicle charging is particularly significant at the very-short-term minute level time scale given the large charging power and stochastic human behaviours. In this paper, a novel historical data based high resolution plug-in electric vehicle load forecasting model structure is established, accommodating the various factors that affect the charging load. Moreover, an enhanced attention-based long short-term memory deep learning approach is proposed for solving the intractable forecasting problem, where a feature upscaling and down scaling algorithm is proposed for the hierarchical high resolution data processing. Numerical results on real-world data of a charging station in Shenzhen show that the proposed model structure and algorithm demonstrates well performance in short-term and very short-term hierarchical high resolution plug-in electric vehicles charging load forecasting problems.

This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively.

The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO and DOLJaya were used to make comparisons with DOLGWO algorithm. Results indicate that the new model has strong robustness and adaptability, and has the significant advantage of converging to the global optimum, which demonstrates that the DOL strategy greatly improves the performance of original GWO algorithm.