Renewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utility

dc.contributor.authorStaschus, Konstantinen
dc.contributor.committeechairSherali, Hanifen
dc.contributor.committeememberMalmborg, Charles J.en
dc.contributor.committeememberRahman, Saifuren
dc.contributor.committeememberRandolph, Johnen
dc.contributor.committeememberSarin, Subhash C.en
dc.contributor.departmentIndustrial Engineering and Operations Researchen
dc.date.accessioned2015-06-29T22:07:04Zen
dc.date.available2015-06-29T22:07:04Zen
dc.date.issued1985en
dc.description.abstractMany electric utilities have been tapping such energy sources as wind energy or conservation for years. However, the literature shows few attempts to incorporate such non-dispatchable energy sources as decision variables into the long-range planning methodology. In this dissertation, efficient algorithms for electric utility capacity expansion planning with renewable energy are developed. The algorithms include a deterministic phase which quickly finds a near-optimal expansion plan using derating and a linearized approximation to the time-dependent availability of non-dispatchable energy sources. A probabilistic second phase needs comparatively few computer-time consuming probabilistic simulation iterations to modify this solution towards the optimal expansion plan. For the deterministic first phase, two algorithms, based on a Lagrangian Dual decomposition and a Generalized Benders Decomposition, are developed. The Lagrangian Dual formulation results in a subproblem which can be separated into single-year plantmix problems that are easily solved using a breakeven analysis. The probabilistic second phase uses a Generalized Benders Decomposition approach. A depth-first Branch and Bound algorithm is superimposed on the two-phase algorithm if conventional equipment types are only available in discrete sizes. In this context, computer time savings accrued through the application of the two-phase method are crucial. Extensive computational tests of the algorithms are reported. Among the deterministic algorithms, the one based on Lagrangian Duality proves fastest. The two-phase approach is shown to save up to 80 percent in computing time as compared to a purely probabilistic algorithm. The algorithms are applied to determine the optimal expansion plan for the Tijuana-Mexicali subsystem of the Mexican electric utility system. A strong recommendation to push conservation programs in the desert city of Mexicali I results from this implementation.en
dc.description.degreePh. D.en
dc.format.extentx, 294 leavesen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/53898en
dc.language.isoen_USen
dc.publisherVirginia Polytechnic Institute and State Universityen
dc.relation.isformatofOCLC# 12833137en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1985.S827en
dc.subject.lcshElectric utilities -- Planning -- Data processingen
dc.subject.lcshElectric utilities -- Mathematical modelsen
dc.subject.lcshRenewable energy sourcesen
dc.subject.lcshProgramming (Mathematics)en
dc.titleRenewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utilityen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineIndustrial Engineering and Operations Researchen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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