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A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

Quan,H, Srinivasan,D, Khambadkone,AM and Khosravi,A 2015, A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources, Applied Energy, vol. 152, pp. 71-82, doi: 10.1016/j.apenergy.2015.04.103.

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Title A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources
Author(s) Quan,H
Srinivasan,D
Khambadkone,AM
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Journal name Applied Energy
Volume number 152
Start page 71
End page 82
Publisher Elsevier Ltd
Publication date 2015-08
ISSN 0306-2619
Keyword(s) Genetic algorithm
Prediction interval
Renewable energy
Scenario generation
Uncertainty integration
Unit commitment
Summary The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.
Language eng
DOI 10.1016/j.apenergy.2015.04.103
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074077

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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