Study On Optimization Strategy Of Wind Turbine Unit Combination

With the energy consumption of coal and oil and the increasing environmental deterioration,it is very important to develop renewable energy.Wind power generation has many outstanding advantages such as low cost,no pollution and remarkable economy,and is widely used in many countries.However,because of the uncertainty and randomness of wind,it brings great difficulties to the power generation system scheduling.In order to overcome the influence of wind power on the safe operation of power grid,the optimization of unit combination is a hot topic.Considering the randomness and uncertainty of wind power,this thesis constructs the combination optimization model of wind turbine based on the analysis of the output characteristics of thermal power units,and discusses the objective function and constraint conditions,which lays the foundation for the subsequent research.In order to deal with a series of problems of unit combination economically and quickly,this thesis presents an improved(DPSO-APSO).The DPSO and APSO are used to optimize the start and stop of thermal power units and power distribution.The results show that the proposed algorithm can improve the convergence speed of the solution process,reduce the energy consumption cost of the unit,and provide a new way of thinking for the optimization of unit combination.For wind power large-scale integration into the grid,traditional optimization algorithm is difficult to solve the problem of wind turbine combination optimization.In this thesis,a combination optimization model of wind turbine based on benders algorithm is established.The generalized Benders Decomposition algorithm is used to divide the combined optimization model of wind turbine units into two main and sub problems.The upper sub problem optimizes the start and stop of the unit,and then the lower layer sub problem allocates the economic cost of the unit.Different sub problems are organized by benders.Finally,the calculation and analysis of the system with 9-machine and 30 nodes shows that the proposed algorithm reduces the solution scale,improves the calculation accuracy and improves the economic performance of the unit.