Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling

Wenfeng Yao, Ziyu Huo (), Jin Zou, Chen Wu, Jiayang Wang, Xiang Wang, Siyu Lu, Yigong Xie, Yingjun Zhuo, Jinbing Liang, Run Huang, Ming Cheng and Zongxiang Lu
Additional contact information
Wenfeng Yao: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Ziyu Huo: Department of Electrical Engineering, Tsinghua University, Beijing 610213, China
Jin Zou: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Chen Wu: Yunnan Power Grid Co., Ltd., Kunming 100084, China
Jiayang Wang: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Xiang Wang: Department of Electrical Engineering, Tsinghua University, Beijing 610213, China
Siyu Lu: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Yigong Xie: Yunnan Power Grid Co., Ltd., Kunming 100084, China
Yingjun Zhuo: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Jinbing Liang: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Run Huang: Yunnan Power Grid Co., Ltd., Kunming 100084, China
Ming Cheng: Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China
Zongxiang Lu: Department of Electrical Engineering, Tsinghua University, Beijing 610213, China

Energies, 2024, vol. 17, issue 20, 1-17

Abstract: In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

Keywords: source-load uncertainty; joint probability distribution; scenario generation; scenario reduction; multi-scenario stochastic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5088/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5088/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:20:p:5088-:d:1497843

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().