Research On Network Configuration And Optimization Scheduling Of Wind Power Systems Considering The Operation Of Energy Storage Power Stations

Due to the characteristics of wind resources,wind power generation has strong fluctuations and uncertainties,which makes it difficult for the power grid to effectively accommodate large-scale wind power generation.Resulting in serious wind curtailment,which is an important issue that constrains the development of wind power in China.With the maturation of energy storage technology,which has good ability to improve the volatility of wind power when connected to the grid,it is considered a key technology for responding to the development of new energy and achieving the "dual carbon" goal.Therefore,this thesis takes wind energy storage system as the research object,and through simulation analysis,studies the stability and economy of the optimization and scheduling of wind energy storage system.The specific research contents are as follows:(1)Considering the impact of the fluctuation and uncertainty of wind power generation,a short-term wind power output prediction model based on improved Markov chain is proposed.In order to solve the problem of the high cost of calculating the transition matrix in the high-order Markov model,an improved real-time algorithm is proposed,and the effectiveness of the algorithm is verified through experiments.Finally,through comparison with traditional algorithms,it is demonstrated that the short-term wind power output prediction model proposed in this thesis has good prediction effect.(2)Wind power generation integration requires smoothing of the output power of wind farms.In this paper,a sliding average filter is used to determine the expected output power and the wind power fluctuation that needs to be smoothed and stored.A wind power generation smoothing optimization system model is established,and the objective function of the model comprehensively considers the power cost and capacity cost of the energy storage system,as well as the punishment for insufficient smoothing of wind power output and wind curtailment.The particle swarm optimization algorithm is used to solve the model,and the optimal energy storage power and capacity configuration for smoothing wind power generation are obtained.Finally,the sensitivity of the energy storage system is experimentally analyzed.(3)Under the consideration of the trading between wind energy storage system and the electricity market,an optimization scheduling model combining power prediction and energy storage control strategies is established,and it is divided into two-stage control strategies to optimize the energy storage charge and discharge and the power purchase and sale behavior.The model comprehensively considers wind power output prediction information,load power prediction information,electricity market price information,energy storage construction cost and operation cost.In the scheduling stage,the predicted information and real-time system status information are combined to determine the operating mode of the next adjustment stage,thereby optimizing the charge and discharge of the energy storage system and the purchase and sale of electricity,balancing the economy and stability of the wind energy storage system.The effectiveness of the proposed control and scheduling strategy is verified through experimental comparison of different control strategies.In summary,the improved Markov algorithm proposed in this thesis reduces the complexity of solving high-order models,and the combination prediction model ensures the accuracy of the prediction.The two-stage scheduling model proposed in this thesis integrates power prediction information with energy storage state information,and in the process of electricity market trading,it achieves both the stability of wind power generation integration and the economy of energy storage system.