Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty
Abstract
This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first- and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming (SOCP) model with an adjustable coefficient. This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
- Tsinghua Univ., Beijing (China). State Key Lab. of Power Systems. Dept. of Electrical Engineering
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
- OSTI Identifier:
- 1513191
- Report Number(s):
- NREL/JA-5D00-71032
Journal ID: ISSN 2196-5625
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Modern Power Systems and Clean Energy
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 3; Journal ID: ISSN 2196-5625
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; economic dispatch; adjustable and distributionally robust chance-constrained optimization; wind power forecasting; uncertainty
Citation Formats
Fang, Xin, Hodge, Bri-Mathias, Li, Fangxing, Du, Ershun, and Kang, Chongqing. Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty. United States: N. p., 2019.
Web. doi:10.1007/s40565-019-0526-5.
Fang, Xin, Hodge, Bri-Mathias, Li, Fangxing, Du, Ershun, & Kang, Chongqing. Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty. United States. https://doi.org/10.1007/s40565-019-0526-5
Fang, Xin, Hodge, Bri-Mathias, Li, Fangxing, Du, Ershun, and Kang, Chongqing. Wed .
"Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty". United States. https://doi.org/10.1007/s40565-019-0526-5. https://www.osti.gov/servlets/purl/1513191.
@article{osti_1513191,
title = {Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty},
author = {Fang, Xin and Hodge, Bri-Mathias and Li, Fangxing and Du, Ershun and Kang, Chongqing},
abstractNote = {This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first- and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming (SOCP) model with an adjustable coefficient. This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.},
doi = {10.1007/s40565-019-0526-5},
journal = {Journal of Modern Power Systems and Clean Energy},
number = 3,
volume = 7,
place = {United States},
year = {Wed Apr 24 00:00:00 EDT 2019},
month = {Wed Apr 24 00:00:00 EDT 2019}
}
Web of Science
Figures / Tables:
Works referenced in this record:
Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties
journal, May 2019
- Fang, Xin; Hodge, Bri-Mathias; Du, Ershun
- IEEE Transactions on Power Systems, Vol. 34, Issue 3
On Distributionally Robust Chance-Constrained Linear Programs
journal, December 2006
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journal, March 2018
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Adjustable Robust OPF With Renewable Energy Sources
journal, November 2013
- Jabr, Rabih A.
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Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities
journal, March 2016
- Fang, Xin; Hu, Qinran; Li, Fangxing
- IEEE Transactions on Power Systems, Vol. 31, Issue 2
Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach
journal, November 2018
- Fang, Xin; Hodge, Bri-Mathias; Du, Ershun
- Applied Energy, Vol. 230
Strategic scheduling of energy storage for load serving entities in locational marginal pricing market
journal, April 2016
- Wei, Yanli; Cui, Hantao; Fang, Xin
- IET Generation, Transmission & Distribution, Vol. 10, Issue 5
Data-driven chance constrained stochastic program
journal, July 2015
- Jiang, Ruiwei; Guan, Yongpei
- Mathematical Programming, Vol. 158, Issue 1-2
Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties
conference, August 2019
- Fang, Xin; Hodge, Bri-Mathias; Du, Ershun
- 2019 IEEE Power & Energy Society General Meeting (PESGM)
Works referencing / citing this record:
Chance-Constrained OPF Based on Polynomials Approximation and Cornish–Fisher Expansion
journal, January 2020
- Cai, Yunfeng; Wang, Liang; Zhou, Jianhua
- Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 44, Issue 4
Figures / Tables found in this record: