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Title: Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

Abstract

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

Authors:
; ; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Natural Science Foundation of China (NNSFC); China Scholarship Council; USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Wind and Hydropower Technologies
OSTI Identifier:
1422394
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Sustainable Energy
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1949-3029
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
MILP; Wind power uncertainty; electricity market; optimal uncertainty interval; power system scheduling

Citation Formats

Wang, Ying, Zhou, Zhi, Botterud, Audun, and Zhang, Kaifeng. Optimal Wind Power Uncertainty Intervals for Electricity Market Operation. United States: N. p., 2018. Web. doi:10.1109/TSTE.2017.2723907.
Wang, Ying, Zhou, Zhi, Botterud, Audun, & Zhang, Kaifeng. Optimal Wind Power Uncertainty Intervals for Electricity Market Operation. United States. doi:10.1109/TSTE.2017.2723907.
Wang, Ying, Zhou, Zhi, Botterud, Audun, and Zhang, Kaifeng. Mon . "Optimal Wind Power Uncertainty Intervals for Electricity Market Operation". United States. doi:10.1109/TSTE.2017.2723907.
@article{osti_1422394,
title = {Optimal Wind Power Uncertainty Intervals for Electricity Market Operation},
author = {Wang, Ying and Zhou, Zhi and Botterud, Audun and Zhang, Kaifeng},
abstractNote = {It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.},
doi = {10.1109/TSTE.2017.2723907},
journal = {IEEE Transactions on Sustainable Energy},
issn = {1949-3029},
number = 1,
volume = 9,
place = {United States},
year = {2018},
month = {1}
}