Study of Long-term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

Distributed generation equipment improves renewable energy utilization and economic benefits through an storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on dynamic change load, which leads to a large deviation from expected return. Considering characteristics lack data less information, after introducing grey theory, we propose new long-term method for establish forecasting model (GFM) user improving basic improve accuracy model. Then, scheduling established with maximum social as optimization objective. Based forecast improved (IGFM), hierarchical solution used solve Finally, parameters are configured service life rate The simulation results show that higher realized in prediction model, algorithm gets convergence speed precision. Apart that, nonlinear correlation trend EES return between cycle revealed. Compared short period, this study provides more comprehensive accurate support ESS, reducing error actual significantly.