You are not logged in.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study

Quan, Hao, Srinivasan, Dipti and Khosravi, Abbas 2016, Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study, Energy, vol. 103, pp. 735-745, doi: 10.1016/j.energy.2016.03.007.

Attached Files
Name Description MIMEType Size Downloads

Title Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study
Author(s) Quan, Hao
Srinivasan, Dipti
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Journal name Energy
Volume number 103
Start page 735
End page 745
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-05-15
ISSN 0360-5442
Keyword(s) Scenario generation
Uncertainty
Renewable generation
Unit commitment
Genetic algorithm
Risk assessment
Summary The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.
Language eng
DOI 10.1016/j.energy.2016.03.007
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083357

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 4 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 84 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 09 May 2016, 15:41:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.