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

Abstract

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

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

“…In order to reduce the number of candidate scenarios that satisfy the probability threshold but have little difference in data distribution within the scenario matrix, so as to improve the representativeness of typical scenarios and reduce the complexity of association rules. So, in this paper, the Wasserstein distance metric is utilized to measure the degree of similarity between sets of candidate-typical scenes [26][27][28]. Finally, discard some redundant scenarios that are more similar even if they satisfy the probability threshold requirement.…”