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

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 effi...

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Main Authors: Yanhong Wang, Limin Yu, Ziheng Zhao, Han Wang, Jinghua Xie, Lin Zhang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/18/7/1614
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author Yanhong Wang
Limin Yu
Ziheng Zhao
Han Wang
Jinghua Xie
Lin Zhang
author_facet Yanhong Wang
Limin Yu
Ziheng Zhao
Han Wang
Jinghua Xie
Lin Zhang
author_sort Yanhong Wang
collection DOAJ
description 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.
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spelling doaj-art-cdcbce1f789546908b99471e4f5a7ed42025-08-20T02:15:55ZengMDPI AGEnergies1996-10732025-03-01187161410.3390/en18071614Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity ModelsYanhong Wang0Limin Yu1Ziheng Zhao2Han Wang3Jinghua Xie4Lin Zhang5Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaKey Laboratory of Power Transmission and Conversion of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaStatic 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.https://www.mdpi.com/1996-1073/18/7/1614static voltage stability marginstochastic characterizationmulti-fidelity modelmoments estimationprobabilistic distribution estimation
spellingShingle Yanhong Wang
Limin Yu
Ziheng Zhao
Han Wang
Jinghua Xie
Lin Zhang
Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
Energies
static voltage stability margin
stochastic characterization
multi-fidelity model
moments estimation
probabilistic distribution estimation
title Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
title_full Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
title_fullStr Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
title_full_unstemmed Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
title_short Uncertainty Characterization Method of Static Voltage Stability Margin in Power Systems with High Percentage of Renewable Energy Based on the Multi-Fidelity Models
title_sort uncertainty characterization method of static voltage stability margin in power systems with high percentage of renewable energy based on the multi fidelity models
topic static voltage stability margin
stochastic characterization
multi-fidelity model
moments estimation
probabilistic distribution estimation
url https://www.mdpi.com/1996-1073/18/7/1614
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