A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis

• Published in: Sustainability Vol. 16; no. 7; p. 2762
• Main Authors: Gao, Yanna, Dong, Hong, Hu, Liujun, Lin, Zihan, Zeng, Fanhong, Ye, Cantao, Zhang, Jixiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Alternative energy sources; China; Climate change; Cluster analysis; Electric power systems; Electricity; General circulation models; Global temperature changes; Methods; Probability distribution; Renewable resources; Solar energy industry; Systems analysis; Temperature; Variables; Weather; Wind farms; Wind power; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su16072762

As multiple wind and solar photovoltaic farms are integrated into power systems, precise scenario generation becomes challenging due to the interdependence of power generation and future climate change. Future climate data derived from obsolete climate models, featuring diminished accuracy, less-refined spatial resolution, and a limited range of climate scenarios compared to more recent models, are still in use. In this paper, a morphing-based approach is proposed for generating future scenarios, incorporating the interdependence of power generation among multiple wind and photovoltaic farms using copula theory. The K-means method was employed for scenario generation. The results of our study indicate that the average annual variations in dry-bulb temperature (DBT), global horizontal irradiance (GHI), and wind speed (WS) are projected to increase by approximately 0.4 to 1.9 °C, 7.5 to 20.4 W/m2, and 0.3 to 1.7 m/s, respectively, in the forthcoming scenarios of the four considered Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). It seems that accumulated maximum wind electricity output (WEO) and solar electricity output (SEO) will increase from 0.9% to 7.3% and 1.1% to 6.8%, respectively, in 2050.