Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems.

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With the development of renewable energy, improving the absorption capacity of power grid has become a difficult problem. It is very important to establish virtual power plants based on self-supplied coal-fired power plant to coordinate renewable energy consumption, especially in Xinjiang Province, China. This paper studied the optimal scheduling of a virtual power plant including wind turbines, photovoltaic units, and energy storage equipment based on the self-supplied power plant. First, a mathematical model of the power and power generation cost of each unit inside the virtual power plant was established, and the demand response mechanism was introduced. Secondly, a multi-objective optimization model is established by considering the maximization of net income of virtual power plants, the minimization of system coal consumption and the maximization of user interruption load benefits, and the use of analytic hierarchy process to determine the weights of three objective functions, of which user interruption load benefits are used to reflect the enthusiasm of users to interrupt the load. Finally, the particle swarm algorithm is used to solve the model. The optimization results show that when the coal price rises, the net income of the system decreases, and even a loss occurs, however, the change in coal price has little effect on the Interrupted load of user. In addition, multi-objective optimization can improve the enthusiasm of users while ensuring the net income of the system, it proves that the model has a good optimization effect.

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