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.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.publisherVirginia Polytechnic Institute and State Universityen
dc.relation.isformatofOCLC# 12833137en
dc.rightsIn Copyrighten
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.type.dcmitypeTexten Engineering and Operations Researchen Polytechnic Institute and State Universityen D.en


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