收藏切换
Spatiotemporal Correlation Scenario Generation of Wind-Solar Complementary System Based on SGMM-MCopula
收藏切换
PDF
Yi YU1, 2, Zi-xuan LIU3, Guo-han ZHAO4, 5, Wan LIU3, You-han DENG1, 2, Dong WEN2, Li MO3, *
Science Technology and Engineering | 2025, 25(10) : 4156 - 4167
Less
收藏切换
Science Technology and Engineering | 2025, 25(10): 4156-4167
Papers·Electrical Technology
Spatiotemporal Correlation Scenario Generation of Wind-Solar Complementary System Based on SGMM-MCopula
Full
Yi YU1, 2, Zi-xuan LIU3, Guo-han ZHAO4, 5, Wan LIU3, You-han DENG1, 2, Dong WEN2, Li MO3, *
Affiliations
  • 1 Laboratory of Hydro-Wind-Solar Multi-energy Control Coordination, Wuhan 430010, China
  • 2 China Three Gorges Corporation Science and Technology Research Institute, Beijing 101100, China
  • 3 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 4 China Yangtze Power Co.,Ltd., Wuhan 430010, China
  • 5 Three Gorges Jinsha Yunchuan Hydropower Development Co., Ltd., Kunming 650204, China
Published: 2025-04-08 doi: 10.12404/j.issn.1671-1815.2404067
Outline
收藏切换

In the context of large-scale integration of wind and solar power into the grid, power system dispatch strategies faced unprecedented challenges. The volatility and randomness of wind and photovoltaic power generation significantly impacted system stability and controllability. To accurately characterize the spatiotemporal correlation of wind-solar power output and construct a practically valuable scenario set, a method for generating spatiotemporal correlated scenarios for wind-solar complementary systems was proposed, based on a coupled SGMM (seasonal Gaussian mixture model) and MCopula (mixed Copula function). Initially, the SGMM was constructed to capture the temporal correlation among wind-solar output variables. Then, the mixed Copula function was employed to describe the spatial correlation among variables. Based on the comprehensive modeling of spatiotemporal correlations, a series of uncertainty scenario sets reflecting these characteristics was generated using the Copula conditional distribution function and inverse transform sampling technique. The simulation results confirmed the effectiveness and reliability of the proposed method. The generated scenario sets not only reflected the spatiotemporal correlation characteristics and annual variation trends of wind-solar output but also better matched the historical actual sequences in terms of distance, providing strong decision-making support for power system dispatch. New perspectives and tools were offered for quantifying uncertainties in wind-solar complementary systems, which had profound theoretical and practical significance for optimizing power system dispatch strategies, reducing uncertainty risks, promoting the efficient utilization of renewable energy, and advancing the sustainable development of power systems.

wind-solar output  /  spatiotemporal correlation  /  sceneario generation  /  Gaussian mixture model  /  Copula function
Yi YU, Zi-xuan LIU, Guo-han ZHAO, Wan LIU, You-han DENG, Dong WEN, Li MO. Spatiotemporal Correlation Scenario Generation of Wind-Solar Complementary System Based on SGMM-MCopula[J]. Science Technology and Engineering, 2025 , 25 (10) : 4156 -4167 . DOI: 10.12404/j.issn.1671-1815.2404067
Year 2025 volume 25 Issue 10
PDF
341
123
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2404067
  • Receive Date:2024-06-01
  • Online Date:2025-07-09
  • Published:2025-04-08
Article Data
Affiliations
History
  • Received:2024-06-01
  • Revised:2025-01-16
Funding
Affiliations
    1 Laboratory of Hydro-Wind-Solar Multi-energy Control Coordination, Wuhan 430010, China
    2 China Three Gorges Corporation Science and Technology Research Institute, Beijing 101100, China
    3 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    4 China Yangtze Power Co.,Ltd., Wuhan 430010, China
    5 Three Gorges Jinsha Yunchuan Hydropower Development Co., Ltd., Kunming 650204, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2404067
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT