| In the context of the "double carbon" goal,wind power and photovoltaic power generation have developed most rapidly in China.In addition,wind energy and solar energy have good complementarity in time and space,and have higher economic value and energy utilization rate than the single form of wind and solar complementary power generation system.Therefore,the study of wind and solar complementary power generation system has important practical value.This paper aims to optimize the efficiency and economy of wind and solar complementary power generation system,and mainly carries out research work on the maximum power tracking control strategy,system energy management strategy and capacity optimization and configuration of wind and photovoltaic power generation in independent wind and solar complementary power generation system.For the traditional wind power generation in the maximum power tracking process,the tracking step is fixed and the output power is prone to jitter.A segmented double fuzzy control algorithm is proposed to divide the maximum power tracking process into different regions by using the P-? curve of the generator,and apply fuzzy controllers with different accuracy in different regions for maximum power tracking,so that the tracking efficiency and accuracy of the wind power generation system can be significantly improved.Finally,simulation modeling analysis was performed,and the tracking time was reduced by 37.5% compared with the perturbation analysis method,and the drop phenomenon was reduced.The effectiveness and superiority of the method were verified.For the phenomenon that the PV(photovoltaic)power generation system is easy to misjudge when the environment changes abruptly during the maximum power tracking process,a three-point measurement method is proposed to determine the change of the working environment according to the detection of the current value and make work adjustment;to solve the problem of fixed tracking step,a zonal adaptive perturbation algorithm is proposed to use the P-U curve of the PV cell to divide the tracking process into different regions and select the appropriate In order to solve the problem of fixed tracking step,a partitioned adaptive perturbation algorithm is proposed to divide the tracking process into different regions by using the P-U curve of PV cells,and select the appropriate step size for maximum power tracking in different regions,thus improving the tracking efficiency and accuracy of the system.And through simulation modeling analysis,the tracking time was reduced by 64.7% compared to the traditional method.Finally,the feasibility of the MPPT control algorithm is further verified through experiments by using the STM32F103C8T6 as the core chip for the software and hardware design of the small-scale PV power generation system.Aiming at the problem of poor fluctuating power capability and easy overcharge and overdischarge in the single energy storage leveling system,a combined storage scheme of battery and supercapacitor is adopted,and a control strategy based on hybrid energy storage SOC variation is proposed,and finally the overall system is modeled and simulated to verify the correctness of the system construction and the feasibility and superiority of the proposed control strategy.Capacity allocation is established with the minimum iso-annual value cost of the sum of investment cost,equipment replacement cost,maintenance cost,and carbon emission benefit cost of the system as the optimization objective,and the power,load shortage rate,and load state of the system as the constraints of the capacity optimization allocation model.The system capacity configuration is solved by using the improved sticky bacterium algorithm to find the optimal solution,and the optimization results reduce the cost of the obtained equal annual value by 10.71%.The experimental results verify the effectiveness of the constructed capacity configuration model and the superiority of the algorithm.Figure [81] Table [13] Reference [83]... |
