A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study

The issue of renewable energy curtailment poses a crucial challenge to its effective utilization. To address this challenge, mitigating the impact intermittency and volatility wind solar is essential. In context, paper employs scenario analysis examine complementary features hybrid systems. Firstly, study defines two types indicators that distinguish between output smoothing source-load matching. Secondly, novel method for generating scenarios based on improved Generative Adversarial Networks presented compared against conventional Monte Carlo Copula function methods. Lastly, generated are employed furnish features. testing results across eight regions indicate proposed generation proficiently depicts historical relevance as well future uncertainties. This found method, root mean square error data was reduced by 4% 3.4% independent systems, respectively. Moreover, combining these resources in most showed total smoothness matching level cannot be enhanced simultaneously. research will serve valuable point reference planning optimizing systems China.