Multi-objective Unit Commitment Research Considering Spatial Correlation Of Wind Power And Uncertainty Of Electric Vehicles

With the increasingly serious problems of environmental pollution and global energy crisis and the development of science and technology,the uncertainty modeling of renewable energy and electric vehicles represented by wind energy is the key to the green and sustainable development of the power grid.The accuracy of the modeling is It is the prerequisite to ensure the optimal dispatch of the power system under the new capacity structure.However,in practical applications,wind energy is a representative of clean energy,and the accuracy of its uncertainty model will cause huge economic and environmental impacts.At the same time,electric vehicles have excellent energy storage characteristics and good electric vehicle charging and discharging modeling.It will provide excellent resources for peak-shaving and valley-filling of the power system.If it can solve their own uncertain problems and optimize and utilize them on a larger scale,it will bring huge benefits to the power system and society.Therefore,this paper studies the stochastic multi-objective unit commitment model that includes wind power and electric vehicles to deal with the uncertainty problem.The more accurate uncertainty modeling in this paper has important theoretical significance and practical engineering value.The main content and methods of this article are as follows.(1)In view of the uncertainty of renewable energy represented by wind power,a high-dimensional uncertainty analysis of the forecast errors of multiple wind farms in the same forecasting system is carried out,and the distribution is regarded as a mixed Gaussian distribution.According to the relationship between wind power output power and prediction error,the data is refined and grouped.Based on the Dirichlet process Gaussian mixture model,the wind power prediction error data is clustered into Gaussian mixture distribution;Monte Carlo simulation and scene reduction technology are used to obtain representative typical scenarios of wind power output that conform to the historical data law,which is used for the calculation of unit commitment.(2)In order to give full play to the initiative of the active load represented by electric vehicles,this paper uses the virtual battery model of electric vehicles to sample electric vehicle groups with known statistical laws.Cluster analysis of electric vehicles obtains various indicators to obtain the virtual battery model of electric vehicles.The upper and lower bounds of the charging power and the upper and lower bounds of the state of charge are combined and used as the dispatch basis in the unit commitment.(3)This paper establishes a multi-objective stochastic unit commitment mathematical model considering the coordinated dispatch of wind power and electric vehicles.The objective function includes the unit’s power generation cost objective function,the emission reduction objective function and the demand side electric vehicle charging cost objective function.Including conventional thermal power unit constraints and wind power output constraints and electric vehicle virtual battery model constraints.Aiming at the nonlinear and non-convex mixed integer optimization model,the hierarchical division is integer programming and non-integer programming using the NSGA-III algorithm,which has high accuracy and has better performance in high-dimensional multi-objective optimization.In summary,through the comparison and analysis of the IEEE-39 node 10-unit model and simulation scenarios,it analyzes the multi-wind farm hybrid Gaussian with historical characteristics driven by the historical forecast data of the multi-wind farms in Belgium.The distribution density function and typical scenarios can well characterize the uncertainty of wind farms with spatial characteristics.The comparison of the final unit commitment solutions proves the effectiveness of the proposed model.