Optimization Scheduling Model And Method For Wind-PV-Pumped Joint Operation In High Proportion Renewable Energy Base

In recent years,China's renewable energy continues to develop rapidly,and a high proportion of renewable energy grid will be an inevitable trend and an important feature of the development of China's power system.A large number of distributed power supply connected to the power system,affect the normal operation of power grid,power quality,and also puts forward new requirements and challenges for power given,storage,new energy industry in Gansu area under the background of developing,taking advantage of the new energy,the pumped storage power plant and wind power,photovoltaic power station joint operation,reduced the wind light,reduce the impact and influence to power network,is the main research purpose of this article.In order to grasp the trend of wind power and photovoltaic power and facilitate the formulation of dispatching tasks,an improved short-term wind-power prediction method of GSA-BP is introduced in this paper.Firstly,in view of the shortcomings of the neural network,such as slow convergence,overfitting,parameter redundancy and so on,the good global optimization ability of GSA algorithm to determine the optimal weight and threshold value of BP and enhance the performance of BP neural network.Then,a new error correction scheme is introduced to verify the correlation between the prediction error and the specific comprehensive meteorological index.By establishing the mapping relation between the comprehensive meteorological index and the error value,the corresponding error correction models are built respectively.The simulation results show that the improved wind power prediction method proposed in this paper can obtain better prediction results and the error correction method can further improve the prediction accuracy.In order to realize the economic operation of the wind farm,improve the stability of the output power of the wind power and enhance the efficiency of the wind energy,the improvement and limitation of the wind-light-storage power generation model are proposed.In this thesis,the Wind-Light-Storage multi-objective optimization model and constraints are established.Firstly,a multi-objective optimization model was established with the aim of optimal comprehensive benefit and minimum abandon rate of Wind-Light-Storage combined power generation system.Then the DCMOPSO algorithm is introduced to obtain the Pareto optimal solution set of the co-generation system.The algorithm sets up multiple populations to search independently at the same time,which effectively improves the search ability of the algorithm.Through dynamic learning of samples and difference variation,the algorithm is further avoided to fall into local optimality.Finally,in order to reduce the difficulty of decision makers in Pareto solution set,TOPSIS decision method is introduced to obtain the unbiased optimal solution.The effectiveness of the proposed model is verified by an example.The fluctuation of scenery will lead to the threat of wind power and photovoltaic gridconnection to the reliability,safety and economic operation of the power system.Therefore,pumped storage energy is introduced to reduce the fluctuation of scenic power and achieve the effect of peak and valley cutting.Aiming at the uncertainty of wind energy output and the fluctuation of wind energy output during grid connection,an optimal scheduling model and method for wind-light-storage joint operation in a high proportion renewable energy base are proposed.Firstly,the optimal scheduling model for 96 time periods of the whole day was established with the objectives of optimal comprehensive benefit,minimum power fluctuation and best matching power demand of the wind-light-storage co-generation system.Then,NSGA-III algorithm based on NSGA-III algorithm which is more suitable for high dimensional multi-objective optimization is introduced to obtain the Pareto optimal solution set of joint generation system.Finally,the simulation of Jiuquan new energy base is carried out,and the effectiveness of the proposed model is verified by example analysis.