Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSG.2013.2278398
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dc.titleA stochastic optimization formulation of unit commitment with reliability constraints
dc.contributor.authorXiong, P.
dc.contributor.authorJirutitijaroen, P.
dc.date.accessioned2014-10-07T04:23:12Z
dc.date.available2014-10-07T04:23:12Z
dc.date.issued2013-12
dc.identifier.citationXiong, P., Jirutitijaroen, P. (2013-12). A stochastic optimization formulation of unit commitment with reliability constraints. IEEE Transactions on Smart Grid 4 (4) : 2200-2208. ScholarBank@NUS Repository. https://doi.org/10.1109/TSG.2013.2278398
dc.identifier.issn19493053
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81916
dc.description.abstractThis paper proposes a new formulation for a stochastic unit commitment (UC) problem that incorporates two major and common sources of uncertainty in short-term generation scheduling, namely, unavailability of generators and load uncertainty. The objective is to minimize operating cost and expected loss of load cost subject to reliability constraints in terms of loss of load probability. The problem is first formulated as a two-stage recourse model in stochastic programming framework where generator unavailability is expressed by a discrete set of outage scenarios and system demand is set to be a nominal value in power balance equations. Then, load uncertainty is represented as a continuous random variable in loss of load probability constraint, which is approximated by a mixed-integer piecewise linear function and integrated to the second stage problem. As a result, the UC problem has a much smaller dimension as compared to the original two-stage recourse model. The proposed formulation can be solved in a timely manner even though the formulation requires some extra binary decision variables. Although loss of load probability constraint is approximated in the optimization problem, simulation results show that the optimal solutions yield desired reliability performance. Several case studies are conducted to examine the impact of reliability requirements and system uncertainties on UC decisions. © 2010-2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSG.2013.2278398
dc.sourceScopus
dc.subjectMixed-integer linear programming
dc.subjectReliability
dc.subjectStochastic programming
dc.subjectUncertainty
dc.subjectUnit commitment
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSG.2013.2278398
dc.description.sourcetitleIEEE Transactions on Smart Grid
dc.description.volume4
dc.description.issue4
dc.description.page2200-2208
dc.identifier.isiut000328064100049
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