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- Publisher Website: 10.1109/TPWRS.2021.3114083
- Scopus: eid_2-s2.0-85115732136
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Article: A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch
Title | A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch |
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Authors | |
Keywords | Data-driven economic dispatch polynomial chaos expansion (PCE) uncertainty quantification |
Issue Date | 1-Jan-2022 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Power Systems, 2022, v. 37, n. 1, p. 812-815 How to Cite? |
Abstract | This letter proposes a data-driven sparse polynomial chaos expansion-based surrogate model for the stochastic economic dispatch problem considering uncertainty from wind power. The proposed method can provide accurate estimations for the statistical information (e.g., mean, variance, probability density function, and cumulative distribution function) for the stochastic economic dispatch solution efficiently without requiring the probability distributions of random inputs. Simulation studies on an integrated electricity and gas system (IEEE 118-bus system integrated with a 20-node gas system) are presented, demonstrating the efficiency and accuracy of the proposed method compared to the Monte Carlo simulations. |
Persistent Identifier | http://hdl.handle.net/10722/338420 |
ISSN | 2021 Impact Factor: 7.326 2020 SCImago Journal Rankings: 3.312 |
DC Field | Value | Language |
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dc.contributor.author | Wang, X | - |
dc.contributor.author | Liu, RP | - |
dc.contributor.author | Wang, X | - |
dc.contributor.author | Hou, Y | - |
dc.contributor.author | Bouffard, F | - |
dc.date.accessioned | 2024-03-11T10:28:42Z | - |
dc.date.available | 2024-03-11T10:28:42Z | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.citation | IEEE Transactions on Power Systems, 2022, v. 37, n. 1, p. 812-815 | - |
dc.identifier.issn | 0885-8950 | - |
dc.identifier.uri | http://hdl.handle.net/10722/338420 | - |
dc.description.abstract | This letter proposes a data-driven sparse polynomial chaos expansion-based surrogate model for the stochastic economic dispatch problem considering uncertainty from wind power. The proposed method can provide accurate estimations for the statistical information (e.g., mean, variance, probability density function, and cumulative distribution function) for the stochastic economic dispatch solution efficiently without requiring the probability distributions of random inputs. Simulation studies on an integrated electricity and gas system (IEEE 118-bus system integrated with a 20-node gas system) are presented, demonstrating the efficiency and accuracy of the proposed method compared to the Monte Carlo simulations. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Power Systems | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Data-driven | - |
dc.subject | economic dispatch | - |
dc.subject | polynomial chaos expansion (PCE) | - |
dc.subject | uncertainty quantification | - |
dc.title | A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TPWRS.2021.3114083 | - |
dc.identifier.scopus | eid_2-s2.0-85115732136 | - |
dc.identifier.volume | 37 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 812 | - |
dc.identifier.epage | 815 | - |
dc.identifier.eissn | 1558-0679 | - |
dc.identifier.issnl | 0885-8950 | - |