Renewable energy is boosting the deployment of microgrids (MGs) with stochastic and low inertia nature. To improve the operational efficiency of MGs while guaranteeing frequency security, a three-stage stochastic unit commitment problem is proposed, where renewable energy can be deloaded. In the first stage, the diesel generators (DGs) are scheduled, responding to uncertainties of loads and photovoltaic generator output. In the second stage, the outputs of DGs are optimized to reduce the operational cost under uncertain disturbances. In the third stage, a novel PV deloading strategy is proposed to test the frequency security of MGs. The three-stage optimization problem is formulated as a multi-stage stochastic optimization problem with recourse. This problem is then solved using a novel nested Benders decomposition algorithm with both dual cuts and partial primal cuts. Simulations are performed on an AC MG under different PV penetration levels and disturbances. The results verify the effectiveness of the proposed model in balancing operational efficiency, renewable energy utilization, and frequency security.

The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.

With large-scale integration of electric vehicles, this paper investigates the load frequency control problem in an islanded microgrid with plug-in electric vehicles (PEVs), which can be regarded as mobile battery energy storages to provide a valuable contribution to frequency regulation. A novel fully-distributed control strategy is proposed to achieve fast frequency regulation of islanded microgrids and effective coordination control of distributed energy sources. Firstly, distributed control based on an improved linear active disturbance rejection algorithm is realized through a multi-agent system, and it greatly enhances the anti-disturbance capability of the microgrid. Then, in order to guarantee the effectiveness of PEVs in frequency regulation, PEVs are controlled following the controllable power rate (CPR) calculated from the consensus-based multi-agent system. Furthermore, the system control construction in this paper is well designed to avoid the negative effects caused by system communication time delay. Finally, numerical simulations under different disturbances are carried out to demonstrate the effectiveness of the proposed control strategy in comparison with other previous control strategies.