Research On The Uncertainty Scheduling Optimization Method Of Power Systems Based On Interval Measurement

With the development of the global economy,energy and environmental issues have gradually become prominent,which has accelerated the transformation of the energy structure of the global power systems.To achieve the goal of "double carbon",the vigorous development of wind power,photovoltaic and other renewable energy,the improvement of load demand response mechanism and the efficient use of energy storage system is an important way to achieve the transformation of the power system and the construction of a new power system.And the uncertainty of renewable energy output and load demand bring great challenges to power systems transformation.How to deal with the uncertainty of source and load in the new power system and play the role of different resources of source-grid-load-storage in the power systems is the key problem that needs to be solved urgently to build the new power system and ensure its safe and stable operation.However,the characteristics of renewable energy output and load are ignored in the traditional interval optimization method for source-load uncertainty treatment,and there is a certain one-sidedness,which cannot accurately measure the uncertain variable intervals.Therefore,the traditional interval optimization can hardly meet the development demand of power system,and it is necessary to study the interval optimization method of power system based on interval measurement.In addition,the adjustable resources such as demand response and energy storage are developing rapidly,but the low utilization rate of energy storage is not conducive to the development of new power systems.In this paper,from the analysis of the impact of traditional interval optimization interval measurement on the system,the source and load interval measurement method is studied,and the energy storage utilization rate is enhanced on this basis.According to the progressive order of the research objects from small to large and from single-side to double-side uncertainty,a comprehensive study and analysis of the source-load uncertainty interval measurement of the power systems and the efficient utilization of the sourcegrid-load-storage resources are conducted to ensure the safe operation of the power systems and improve the systems economy.The main research work and innovations completed in this dissertation includes:(1)Considering the problems of seasonal mismatch between renewable energy output and load,and the unclear relationship between optimization influencing factors and results,a day-ahead interval optimization model of microgrid considering interactive power and different seasonal load demands is proposed.Based on the interval optimization theory,the uncertainty of wind power,PV output and load are expressed using interval numbers,and the percentage of forecasting values is measured as the interval range of uncertain variables,and then the day-ahead interval optimization model of microgrid considering source-load uncertainty is constructed.The simulation analyzes the load demand in different seasons under single-sided and double-sided uncertainties and the relationship with the interactive power of the upper grid.The results show the greater impact of considering the source-load dual-side uncertainty has a greater impact on the results of scheduling optimization,and further obtain the optimized interactive power,which effectively improves the system operation economy.The influence of the accuracy of source and load interval measurement on the results in traditional interval optimization is also analyzed.(2)To address the problem of low accuracy of uncertainty interval measurement of renewable energy output,an interval scheduling optimization method for power systems based on improved adaptive diffusion kernel density estimation is proposed.The accuracy of the uncertainty interval range of wind power and PV output is measured using improved adaptive diffusion kernel density estimation,and an interval optimization model with the minimization of operating cost,EV loss cost and environmental treatment cost as the objective function is constructed.It is verified through simulation that the proposed interval measurement method can effectively improve the fitting performance of the traditional kernel density estimation without involving numerical optimization,and overcome the deficiency of the traditional kernel density estimation method which requires the assumption that the sample data follows a definite distribution function.The accuracy and feasibility of the proposed method are verified by comparing it with the rest of interval measurement methods such as percentage of predicted value and extreme learning machine under the premise of satisfying the interval accuracy measure of renewable energy output,and realizing the safe and reliable operation of the power systems.(3)The interval scheduling optimization method of power system based on the improved extreme learning machine measurement is proposed for the difficult problem that the load interval measurement range is too wide due to insufficient load information collection.The load history interval is constructed by fitting the normal distribution to the load history forecasting error.Then considering the random initial population of the ant-lion algorithm will affect the speed and quality of the solution,the gray wolf algorithm is used to generate the high-quality initial population of the ant-lion algorithm to avoid falling into the local optimal solution.Next,an extreme learning machine input weight and hidden layer bias are optimized by the ant-lion algorithm,and an extreme learning machine short-term power load interval prediction method with strong generalization capability is proposed to achieve an accurate interval measure of load demand uncertainty.It is also applied to the day-ahead interval optimization model of power systems considering carbon emissions.By comparing the proposed interval measurement method with the ant-lion algorithmextreme learning machine,extreme learning machine,BP,random vector function link,and long short-term memory network,it is verified that the proposed interval measurement method and optimization model have higher accuracy and better quality of the optimized result intervals.(4)Considering the low utilization rate of existing energy storage systems and the coarse demand response mechanism under the source-load uncertainty interval measurement,an interval optimization model of power systems based on shared energy storage and refined demand response is proposed.The refined demand response mechanism and shared storage optimization model for different building load types are analyzed based on the accuracy measurement of the source-load dualside uncertainty interval.Then,the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed.To improve the accuracy of the optimization results,the sensitivity analysis is performed on the linearized segmentation number of the objective function under the bilateral uncertainty interval measurement.Finally,by comparing the simulation with the traditional centralized energy storage system,the results show that the accuracy of the dual-side uncertainty output range and the shared energy storage can effectively improve the energy storage utilization and system operation economy,and the cooperative interaction of sourcegrid-load-storage can be realized.