考虑ENSO事件的高比例可再生能源系统季节性储能容量优化配置

Recently the extreme weather caused by El Niño-Southern Oscillation (ENSO) events has had a significant impact on the power system with high proportion of renewable energy, resulting in a seasonal electricity disequilibrium between source and load. Therefore, a novel model of optimal capacity allocation of seasonal energy storage (SES) for the High-Proportion Renewable Energy System (HP-RES) considering ENSO events is proposed. Firstly, the assessment model of source-load mismatch considering ENSO events is carried out. Specifically, the Spearman correlation coefficient is used to present the correlation between the ENSO events index and source/load data. And the modified Random Forest Regression Algorithm (RFRA) is applied to build a prediction model with the ENSO events index and related meteorological indicators (RMIs) as input under three Shared Socioeconomic Pathways (SSPs). The source-load mismatch of each SSP is calculated using predicted data by the modified RFRA. Secondly, the optimal capacity allocation model is proposed, the objective function of which is to minimize the comprehensive cost. Finally, the validity of the proposed model is verified by comparing the power gap penalty cost in different cases under three SSPs, based on the practical data of a certain region.