Study On Optimizing Operation Based On Monte Carlo For Hybrid Wind Energy Storage System

Large-scale wind power integration motivates great change in grid operation mode.The characteristics of wind power uncertainty affect the controllable stability of wind power in the power grid,resulting in insufficient peak-load regulation of the power grid,and a large number of wind abandonment phenomena.This thesis studies from two aspects to achieve the purpose of wind curtailment.One is to improve the energy structure,and the other is to reduce the impact of wind power uncertainty in hybrid systems.The main research contents are as follows:(1)The structure and working principle of the hybrid wind and battery energy storage system were analyzed and the operation principle of hybrid system to absorb wind power was described.At the same time,the thesis analyzed and summarized the working principle and system power model of the battery energy storage system in the hybrid system.(2)The adverse effects caused by the uncertainty of wind turbine operation on the power system were analyzed in depth.Based on that,the thesis designed the wind speed prediction model based on kernel ridge regression and the wind power prediction model based on support vector regression and carried out simulation.The error index was used to evaluate the performance of the two models.The simulation results prove the effectiveness of the proposed methods and a given error in wind power prediction.(3)The robustness was introduced into the real-coded quantum evolution algorithm based on alleles for the uncertainty of wind turbine operation,and the algorithm was simplified.Firstly,the coding and update strategies of real-coded quantum evolutionary algorithm based on alleles were studied in depth,and the robust optimization theory and the application of uncertainty problems were summarized.Then the robust model was introduced into the quantum optimization framework and the concept of the objective function was proposed and verified by a numerical example.Finally,to simplify the calculation,the Monte Carlo Integral was used to calculate the effective objective function.(4)Taking hybrid wind and battery energy storage system as an example,this thesis established a coordinated optimization model under the state constraint,which take the minimum amount of wind curtailment as the optimization target.In order to solve the uncertainty of wind power,the hybrid wind energy storage system model was transformed into a robust optimization model based on Monte Carlo by effective objective function and Monte Carlo method,and the proposed robust quantum evolution algorithm was used to solve the model.The Quasi Monte Carlo method using SQRT sequence sampling was used to estimate the effective objective function,and the obtained value was used as the fitness value of the evolutionary algorithm.Finally,the example analysis shows that the proposed coordinated optimization strategy has strong robustness in the case of uncertain wind power output and can effectively reduce the amount of wind curtailment.