Probabilistic Evaluation Method for Renewable Energy Integration Capability for Wind-Photovoltaic-Storage Coupling System with Small Sample
Targeting the problem of small sample uncertainty faced by wind and photovoltaic power integration,the paper proposes a probabilistic evaluation method for renewable energy integration to solve the small sample uncertainty based on the data-driven polynomial chaos expansion method.Firstly,an evaluation model of power rejection rate with the goal of minimizing the power rejection rate is proposed considering the operation constraints for the wind-photovoltaic-storage system.Then focusing on the lack of historical wind and photovoltaic power data,it is difficult to use the traditional uncertainty optimization method to deal with the problem,so a data-driven chaotic polynomial expansion method is proposed for the probability evaluation of the power rejection rate.An arbitrary chaotic polynomial is constructed by using the multi-order moment of wind-photovoltaic output,and the expansion coefficients are solved based on multidimensional Gaussian integral.Finally,the higher-order moment information of the power rejection rate is calculated according to the expansion coefficient analysis,and the probability distribution is solved according to the maximum entropy method.The simulation results show that the proposed method is more efficient than the Monte Carlo simulation method.
wind-photovoltaic-storage coupling systemoptimal schedulingoptimal operationwind-photovoltaic power integrationpolynomial chaos expansions