Security-constrained unit commitment (SCUC) is the foundation to ensure the safe and economic operation of power systems. A large number of integer variables and complex network security constraints are the main barriers that limit the computation efficiency of the SCUC problem. In this paper, the congestion information is used to identify the units that play a key role in alleviating transmission congestion, in the process of iterative solving of the SCUC. Then the commitment statuses of non-critical units are fixed, and the number of integer variables is reduced. Additionally, a neighborhood search cutting plane is designed and added to the model so as to reduce the branch-and-bound search space of the mixed integer programming problem. The simulation results of the 3375-bus case demonstrate that the proposed method can significantly improve the solving efficiency compared with the traditional method.

As a promising technology dealing with electric behavior analysis and energy characteristic mining, nonintrusive load monitoring is popular with these people with energy conservation awareness. Coincidentally, these residents are with strong motivation to install roof-top photovoltaics. Therefore, it is a real challenge to implement non-intrusive load disaggregation considering PV integrations, which is rarely researched. In this paper, this problem is thoroughly investigated with an achievement of a corresponding robust load disaggregation approach. At the first stage, the problem formulation is established based on the evolved dictionary learning scheme, where the special features of PV, e.g., no switching pattern and continuous power variation, are addressed. Secondly, aiming at robustness of monitoring, the steady state disaggregation is enhanced by integrating with event-based detection, forming a hybrid validation strategy. Then, the whole problem is solved following the sparse coding principle. The proposed study is verified by both simulation tests and field measurements, and the results show that the proposed method is accurate and robust in non-intrusive load monitoring with PV consideration. Besides, it is compatible with various load signature utilization, leading to a practical disaggregation solution.

In order to solve the practical problem of 24-hours dynamic reactive power optimization in power system dispatching, according to the limit output range of generator’s operation, this paper divides generator’s operation into three regions in PQ mathematical model and takes the action times of discrete control equipment as global inequality constraints, finally establishes a complete 24-hours non-linear mixed integer dynamic reactive power optimization mathematical model. The simulation results show that the mathematical model in this paper can adjust the output of the generators according to the actual situation effectively when the generators are in phase-leading operation. In addition, dynamic reactive power optimization reduces the number of control equipment’s action at the cost of increasing active power loss in the whole network. So, In order to ensure the safe and economic operation of power grid, it is necessary to coordinate the relationship between active power loss and the constraints of control equipment’s action. This paper has positive reference significance for power system dispatching.