Regulating The Standing Phase Angle Of Power System Transmission Loop Paralleling Considering Wind Power Uncertainty

The shortage of fossil fuels and the massive emissions of greenhouse gases have caused increasingly serious environmental pollution problems.With the proposal of the dual carbon goal,building a clean and low-carbon energy system has continuously promoted the new energy field.New energy technologies such as wind power and photovoltaic are considered the main direction of future energy development.The random variation of natural wind power leads to uncertainty in wind power output,which has adverse effects on the safe and stable operation of the power system.Due to the frequent occurrence of power outages caused by the massive integration of new energy into the power grid worldwide,a reasonable recovery plan can improve system operation safety and reduce economic losses caused by power outages.Therefore,it is particularly important to develop a recovery plan for new energy participation in the power system recovery process.Considering the operational risks brought by the uncertainty of wind power output to the system,this paper conducts research on the control model of the parallel closing angle of the loop network during the power system restoration process considering the uncertainty of wind power output.The main research content is as follows:(1)By studying the characteristics and models of wind power output,the influencing factors of wind power output uncertainty were analyzed,and the impact of wind power output fluctuations on system power flow distribution was demonstrated through examples.The necessity of considering wind power uncertainty during the parallel operation of the system ring network was pointed out.(2)A probability prediction model of wind power based on quantile regression,shortterm memory network,and kernel density estimation is established.After obtaining the probability function of wind power output through prediction,a joint output probability model of two wind farms is established using the Copula function.Then,Latin hypercube sampling and K-means clustering methods are used to generate and reduce the probability of wind power output scenarios and corresponding scenarios,achieving scenarioization of wind power uncertainty.This provides a data basis for the subsequent optimization model of parallel closing angles in the power system loop network considering wind power uncertainty.(3)A power system loop grid parallel closing angle optimization model considering wind power uncertainty has been established.Based on analyzing the principle and basic requirements of parallel operation in the power system,to minimize the total amount of active power output adjustment of the system generator and restore the most important loads,a multi-objective optimization solution is carried out based on ensuring the safe and stable operation of the system.Based on the wind power output scenarios established in the previous text,the wind power output values under different probabilities are integrated to ensure that the proposed scheme can meet the closing angle limits under different wind power output scenarios,ensuring the smooth operation of parallel closing of the ring network.(4)The IEEE-39 node system was used to validate the optimization results without considering wind power uncertainty,considering single wind farm uncertainty,and considering multiple wind farm uncertainties.Comparing the results of optimization schemes in different scenarios,it can be seen that when the impact of wind power output uncertainty on the system regulation process is ignored,the optimization scheme is difficult to ensure that the closing angle is within the limited range,indicating the necessity of establishing the model in this paper.Then,based on the analysis of the optimization scheme for the closing angle under the uncertainty of a single wind farm and multiple wind farms,it can be concluded that the optimization scheme obtained by the model established in this paper not only meets the closing angle limit when the wind power output is within the predicted range but also exhibits good robustness when the wind power output exceeds the predicted range.