Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models

Yanhong Wang, Limin Yu, Ziheng Zhao, Han Wang (), Jinghua Xie and Lin Zhang
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Yanhong Wang: Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, China
Limin Yu: Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, China
Ziheng Zhao: Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, China
Han Wang: Key Laboratory of Power Transmission and Conversion of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
Jinghua Xie: Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, China
Lin Zhang: Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, China

Energies, 2025, vol. 18, issue 7, 1-23

Abstract: Static voltage stability margin is an important index for measuring the stability of the operating point of the power system, and its stochastic characterization is important for instructing the operation of power systems with a high percentage of renewable energy. On the basis of computational efficiency and accuracy, the existing uncertainty representation methods of SVSM are divided into two categories in this paper, namely high-fidelity and low-fidelity models, and the disadvantages of both methods are discussed. On this basis, an uncertainty characterization method of SVSM in power systems with a high percentage of renewable energy is proposed, based on the multi-fidelity model to achieve high-precision estimation of the moments and probabilistic distribution of SVSM. For moment estimation, an optimal input sample allocation method combining the characteristics of high- and low-fidelity models is proposed to achieve unbiased estimation of the moments of the SVSM with a pre-given computational budget. For probabilistic distribution estimation, a method based on the starting distribution is proposed to improve the estimation accuracy by using prior information provided by the multi-fidelity model. Finally, the effectiveness of the proposed method is verified by simulation calculations of a 118-bus power system.

Keywords: static voltage stability margin; stochastic characterization; multi-fidelity model; moments estimation; probabilistic distribution estimation (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: 2025
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