Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 102006 |
| Fachzeitschrift | Sustainable Energy, Grids and Networks |
| Jahrgang | 44 |
| Frühes Online-Datum | 23 Okt. 2025 |
| Publikationsstatus | Veröffentlicht - Dez. 2025 |
Abstract
This article presents an advanced two-stage statistical framework for operational infeasibility analysis (OIA) in active distribution systems operating under high uncertainties. In Stage-I, enhanced Mean and Multiple Quantile Lite-Polynomial Chaos Expansions (IMQ-Lite-PCEs) are proposed as robust meta-modeling tools for uncertainty quantification. In Stage-II, the IMQ-Lite-PCEs are leveraged to extract comprehensive statistical insights, enabling accurate estimations of key metrics such as means, variances, confidence intervals, and conditional distributions of system states, facilitating informed decision-making. The efficacy of the proposed method (PM) is rigorously validated through comparisons with state-of-the-art PCE variants for uncertainty quantification in renewable energy resource (RES)- and electric vehicle (EV)-dominated power systems. The results underline the superior accuracy of the PM, with L1 -relative errors as low as 0.22 %, 0.19 %, 0.16 %, 0.12 %, and 0.43 % for state estimations on the IEEE 33-, −69, −85, 141-, and unbalanced three-phase 37-bus systems, respectively. Moreover, the PM demonstrates exceptional capabilities in probabilistic and classification analyses, achieving 98.27 %, 98.72 %, 98.63 %, and 98.95 % classification accuracy for identifying nodal voltage violations and 91.06 %, 99.58 %, 92.94 %, and 93.11 % accuracy for detecting overloaded line power flows in the IEEE −33, −69, −85, and 141-bus networks, respectively. Additionally, comparative analysis against low-rank approximation methods, Gaussian Process Regression (GPR), and Deep Sparse GPR underscores the PM’s robust performance in handling complex probabilistic computations and classification tasks.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Energie (insg.)
- Erneuerbare Energien, Nachhaltigkeit und Umwelt
- Energie (insg.)
- Energieanlagenbau und Kraftwerkstechnik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
Ziele für nachhaltige Entwicklung
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in: Sustainable Energy, Grids and Networks, Jahrgang 44, 102006, 12.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - A novel framework for operational infeasibility assessment of active distribution systems using improved quantile polynomial chaos expansion
AU - Ly, Sel
AU - Chauhan, Kapil
AU - Nguyen, Tan Minh
AU - Wolter, Franz Erich
AU - Nguyen, Hung Dinh
N1 - Publisher Copyright: © 2025 Elsevier Ltd.
PY - 2025/12
Y1 - 2025/12
N2 - This article presents an advanced two-stage statistical framework for operational infeasibility analysis (OIA) in active distribution systems operating under high uncertainties. In Stage-I, enhanced Mean and Multiple Quantile Lite-Polynomial Chaos Expansions (IMQ-Lite-PCEs) are proposed as robust meta-modeling tools for uncertainty quantification. In Stage-II, the IMQ-Lite-PCEs are leveraged to extract comprehensive statistical insights, enabling accurate estimations of key metrics such as means, variances, confidence intervals, and conditional distributions of system states, facilitating informed decision-making. The efficacy of the proposed method (PM) is rigorously validated through comparisons with state-of-the-art PCE variants for uncertainty quantification in renewable energy resource (RES)- and electric vehicle (EV)-dominated power systems. The results underline the superior accuracy of the PM, with L1 -relative errors as low as 0.22 %, 0.19 %, 0.16 %, 0.12 %, and 0.43 % for state estimations on the IEEE 33-, −69, −85, 141-, and unbalanced three-phase 37-bus systems, respectively. Moreover, the PM demonstrates exceptional capabilities in probabilistic and classification analyses, achieving 98.27 %, 98.72 %, 98.63 %, and 98.95 % classification accuracy for identifying nodal voltage violations and 91.06 %, 99.58 %, 92.94 %, and 93.11 % accuracy for detecting overloaded line power flows in the IEEE −33, −69, −85, and 141-bus networks, respectively. Additionally, comparative analysis against low-rank approximation methods, Gaussian Process Regression (GPR), and Deep Sparse GPR underscores the PM’s robust performance in handling complex probabilistic computations and classification tasks.
AB - This article presents an advanced two-stage statistical framework for operational infeasibility analysis (OIA) in active distribution systems operating under high uncertainties. In Stage-I, enhanced Mean and Multiple Quantile Lite-Polynomial Chaos Expansions (IMQ-Lite-PCEs) are proposed as robust meta-modeling tools for uncertainty quantification. In Stage-II, the IMQ-Lite-PCEs are leveraged to extract comprehensive statistical insights, enabling accurate estimations of key metrics such as means, variances, confidence intervals, and conditional distributions of system states, facilitating informed decision-making. The efficacy of the proposed method (PM) is rigorously validated through comparisons with state-of-the-art PCE variants for uncertainty quantification in renewable energy resource (RES)- and electric vehicle (EV)-dominated power systems. The results underline the superior accuracy of the PM, with L1 -relative errors as low as 0.22 %, 0.19 %, 0.16 %, 0.12 %, and 0.43 % for state estimations on the IEEE 33-, −69, −85, 141-, and unbalanced three-phase 37-bus systems, respectively. Moreover, the PM demonstrates exceptional capabilities in probabilistic and classification analyses, achieving 98.27 %, 98.72 %, 98.63 %, and 98.95 % classification accuracy for identifying nodal voltage violations and 91.06 %, 99.58 %, 92.94 %, and 93.11 % accuracy for detecting overloaded line power flows in the IEEE −33, −69, −85, and 141-bus networks, respectively. Additionally, comparative analysis against low-rank approximation methods, Gaussian Process Regression (GPR), and Deep Sparse GPR underscores the PM’s robust performance in handling complex probabilistic computations and classification tasks.
KW - Electric vehicle
KW - Meta-modeling
KW - Operational infeasibility
KW - Renewable energy management
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=105020944158&partnerID=8YFLogxK
U2 - 10.1016/j.segan.2025.102006
DO - 10.1016/j.segan.2025.102006
M3 - Article
AN - SCOPUS:105020944158
VL - 44
JO - Sustainable Energy, Grids and Networks
JF - Sustainable Energy, Grids and Networks
M1 - 102006
ER -