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Title: Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

Abstract

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conducted in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), NREL Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1376664
Report Number(s):
NREL/JA-5D00-67653
Journal ID: ISSN 0885-8950
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Power Systems; Journal Volume: 32; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; stochastic processes; biological system modeling; load modeling; optimization; economics; reliability; generators

Citation Formats

Wu, Hongyu, Krad, Ibrahim, Florita, Anthony, Hodge, Bri-Mathias, Ibanez, Eduardo, Zhang, Jie, and Ela, Erik. Stochastic Multi-Timescale Power System Operations With Variable Wind Generation. United States: N. p., 2017. Web. doi:10.1109/TPWRS.2016.2635684.
Wu, Hongyu, Krad, Ibrahim, Florita, Anthony, Hodge, Bri-Mathias, Ibanez, Eduardo, Zhang, Jie, & Ela, Erik. Stochastic Multi-Timescale Power System Operations With Variable Wind Generation. United States. doi:10.1109/TPWRS.2016.2635684.
Wu, Hongyu, Krad, Ibrahim, Florita, Anthony, Hodge, Bri-Mathias, Ibanez, Eduardo, Zhang, Jie, and Ela, Erik. 2017. "Stochastic Multi-Timescale Power System Operations With Variable Wind Generation". United States. doi:10.1109/TPWRS.2016.2635684.
@article{osti_1376664,
title = {Stochastic Multi-Timescale Power System Operations With Variable Wind Generation},
author = {Wu, Hongyu and Krad, Ibrahim and Florita, Anthony and Hodge, Bri-Mathias and Ibanez, Eduardo and Zhang, Jie and Ela, Erik},
abstractNote = {This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conducted in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.},
doi = {10.1109/TPWRS.2016.2635684},
journal = {IEEE Transactions on Power Systems},
number = 5,
volume = 32,
place = {United States},
year = 2017,
month = 9
}
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