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• Model-based method is combined with model-free method. • Actions are mapped to the devices to control the active and reactive power in parallel. • Domain-adaptation-based transfer learning is proposed for the same power grid with different structures. • Parameter-based transfer learning is constructed for different power grids. The power system rescheduling based on model-free methods has obvious defects in practical application, such as poor scenario transferability, long data training time, and waste of domain knowledge. To overcome the above defects, a transfer-reinforcement-learning-based rescheduling method of differential power grids considering security constraints is proposed. When constructing the Markov decision-making process of security-constrained rescheduling, both the off-limits of line power and node voltage are considered in the reward. The action space of deep reinforcement learning is narrowed by calculating the sensitivities of devices and mapped to control the active and reactive power regulating devices to reschedule active and reactive power simultaneously. According to the change degree of transfer object, the applications of transfer learning are divided into two scenarios. For the security-constrained rescheduling transfer scenario of different structures of the same power grid, a domain-adaption transfer learning method is formed, realizing good data adaptability after structure changes with the original model. Moreover, a policy-based transfer learning method is constructed for the security-constrained rescheduling transfer scenario of different power grids, enhancing the training speed and training effect of target power grid. Two standard systems and two actual power grids are utilized to verify the effectiveness of the method. For the actual power grids, the effects of the two scenarios are improved by 5.8% and 3.9% with transfer learning. Compared with other methods, this method not only has obvious advantages in transferability, but also has a shorter learning process and lower control cost. [ABSTRACT FROM AUTHOR]

A powerful thermal management scheme is the key to realizing the extremely fast charging of battery electric vehicles. In this scheme, a water-cooled plate is set at the bottom of the battery modules, which has a remarkable heat dissipation ability but increases the temperature difference between the top and bottom of the battery. This temperature difference problem is more obvious during the high-rate charge/discharge process; in particular, in the area of battery tabs, which are usually located far from the cooling plate, heat accumulates, risking thermal runaway. To address this problem, a thermal management method based on an ultrathin vapour chamber (UTVC) is proposed in this paper to improve temperature uniformity and reduce the highest temperature of batteries. According to the number of applied UTVCs, a unilateral UTVC (U-UTVC) and a bilateral UTVC (B-UTVC) cooling approach were presented, the effectiveness of which was verified by a set of thermal performance experiments. The test data show that the temperature differences in the batteries under the U-UTVC and B-UTVC schemes are 39.77% and 73.54% lower than those under the traditional method at a 25 °C ambient temperature and a 1.39C charge rate, respectively. The temperature difference on the battery surface with the B-UTVC scheme is approximately 3 °C at 25 °C and 40 °C ambient temperature. • An ultra-thin vapour chamber-based power battery thermal management is proposed to improve the temperature uniformity. • The methods have limited effect on battery volumetric specific energy, and the volumetric specific energy of battery is only reduced by 1.2% which is far less than reported investigations. • Temperature difference and maximum temperature of battery module of the presented scheme are reduced by 15.49% and 73.54%, respectively. [ABSTRACT FROM AUTHOR]

• A comprehensive literature review of meta-heuristic optimization algorithms for wind power forecasting is conducted. • A well-designed taxonomy of meta-heuristic algorithms for optimizing wind power forecasting model parameters is developed. • A deeply comprehensive and scientific multiple error evaluation metric is proposed for wind power error analysis. • Further research topics for future works in wind power forecasting are presented. The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers. [ABSTRACT FROM AUTHOR]