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Title: Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty

Journal Article · · Journal of Modern Power Systems and Clean Energy
 [1];  [1]; ORCiD logo [2];  [3];  [3]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
  3. Tsinghua Univ., Beijing (China). State Key Lab. of Power Systems. Dept. of Electrical Engineering
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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.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1513191
Report Number(s):
NREL/JA-5D00-71032
Journal Information:
Journal of Modern Power Systems and Clean Energy, Vol. 7, Issue 3; ISSN 2196-5625
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 12 works
Citation information provided by
Web of Science

References (9)

Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties journal May 2019
On Distributionally Robust Chance-Constrained Linear Programs journal December 2006
Distributionally Robust Chance Constrained Optimal Power Flow with Renewables: A Conic Reformulation journal March 2018
Adjustable Robust OPF With Renewable Energy Sources journal November 2013
Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities journal March 2016
Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach journal November 2018
Strategic scheduling of energy storage for load serving entities in locational marginal pricing market journal April 2016
Data-driven chance constrained stochastic program journal July 2015
Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties conference August 2019

Cited By (1)

Chance-Constrained OPF Based on Polynomials Approximation and Cornish–Fisher Expansion journal January 2020

Figures / Tables (10)

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Table 1(p. 4)table Table 1
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Table 3(p. 5)table Table 3
Table 4(p. 6)table Table 4

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Related Subjects

17 WIND ENERGY
economic dispatch
adjustable and distributionally robust chance-constrained optimization
wind power forecasting
uncertainty