Evolutionary Multi-Objective Optimization Algorithms in Microgrid Power Dispatching

The long-term operation of a railroad usually leads to defects in its rails, axles, fasteners, etc. These problems directly affect the safety of the rail system. Therefore, it is important to ensure the health of key railroad structures. In this paper, a deep learning-based rail damage identification method is established by analyzing the rail vibration signals collected with piezoelectric ceramic pads. The multiple features of vibration signals are combined and then reconstructed into grayscale maps as the inputs of the model. The key information of the grayscale maps is extracted using neural networks. The idea of pre-convolution is used to solve the problem that the model pays more attention to certain features due to the different input sizes and the implied weights of the features. Finally, the performance of the three convolutional neural networks (CNN) in rail damage detection is evaluated and compared. The results show that the CNN with pre-convolution and Residual structure has better recognition for the presence of rail damage than other methods.

The development of both microgrids and electric vehicles has become an important part of the current energy scenario. Useful complementary advantages can be formed between electric vehicles and microgrids, the consumers of which can utilize renewable energy and narrow the peak–valley differences of the net load curve while ensuring their own pecuniary interests. Based on the idea of the Stackelberg game, an optimal dispatch model of a microgrid with electric vehicles is proposed herein, where the benefits of the state of charge are taken into account. In the upper layer of the model, the charging and discharging behaviors of electric vehicles are guided by the goal of minimizing the operating cost of the microgrid. In the lower layer of the model, electric vehicle users adjust the charging and discharging strategies with the goal of maximizing their individual interests. The study results demonstrate that the proposed model not only reflects the benefits of both the master and slave but also reduces the peak–valley differences of the microgrid load. Further, the charging and discharging times of electric vehicles are reduced, and their state of charge is maintained at a high level.