Research On Rotation Reserve Partition And Optimization Of Power System Including Wind Power

With the gradual increase of the proportion of wind power in the power system,the randomness of wind resources itself will affect the economic dispatch and safe operation of traditional power grids in many ways.Therefore,thermal power units in the system need to provide a large number of rotating spares to respond the power shortage caused by the fluctuation of wind power output.In order to ensure that the system can accept more clean energy and maintain good economy under the conditions of safe and stable operation,it is urgent to make a reasonable and effective decision on the optimization of the rotating reserve capacity of the system after large-scale wind power grid connection.The specific work of this article is as follows:Firstly,the vulnerability of each bus in the system is evaluated,and the main indicators affecting the vulnerability of the buses are found from the perspective of electrical structural characteristics and state transition characteristics.The entropy weight method is used to reasonably weight each indicator to determine the inclusiveness.The entropy weight method is used to weight the indicators reasonably,and an assessment method for the comprehensive vulnerability of buses in a wind power system is determined.Eventually,an IEEE30 bus system is used as an example to verify the effectiveness of the proposed evaluation method.Secondly,in order to ensure the availability of rotating spares and alleviate the problem of inefficient supply of spares caused by network congestion or outages,an adaptive semi-supervised spectral clustering algorithm is proposed to partition the system.Aiming at the characteristics of the spectral clustering algorithm that has a strong dependence on scale parameters and the number of clusters to be determined manually,a sensitivity parameter adaptive selection strategy based on sensitivity and a method for determining the number of clusters based on feature gaps are proposed respectively;in order to make the partitioning results more reasonable,replace the Euclidean distance between the sample points with the electrical distance between the buses.The partition clustering process is guided by the semi-supervised information of the comprehensive vulnerability of the buses,and the spare partition is constructed and the spare resources are configured by using the adaptive semi-supervised spectral clustering algorithm.Simulation results show that the proposed method can effectively improve the clustering performance and can automatically determine the number of partitions,which is suitable for the problem of rotating reserve partitions of wind power systems.Then,to solve the problem of optimizing the rotating reserve capacity of the wind power system,a mathematical model with the objective of optimal system economy was established,and an improved plant growth simulated algorithm was proposed to dynamically solve the rotating reserve capacity in each zone.In order to solve the problem that the algorithm is slow and easy to fall into local extremum when solving large-scale systems,the diversity of the population is enriched by introducing back learning,and the speed and accuracy of the algorithm are improved by using an adaptive variable step size strategy.Simulation results show that the improved algorithm is fast to solve and is not easy to fall into local extremes,and the effectiveness of the actual backup solution obtained by the solution is verified.Finally,the proposed method is applied to the IEEE118 bus system.The results show that the method in this paper can effectively deal with the problem of rotating backup partitions and capacity optimization of large-scale wind power systems.It can still ensure the availability of rotating backup when the bus fails,which effectively reduces the reserve demand and the total system operation cost are conducive to the improvement of the system’s ability to accept wind power,and provide a new idea for the rotation reserve division and optimization of the power system.