Renewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utility
dc.contributor.author | Staschus, Konstantin | en |
dc.contributor.committeechair | Sherali, Hanif | en |
dc.contributor.committeemember | Malmborg, Charles J. | en |
dc.contributor.committeemember | Rahman, Saifur | en |
dc.contributor.committeemember | Randolph, John | en |
dc.contributor.committeemember | Sarin, Subhash C. | en |
dc.contributor.department | Industrial Engineering and Operations Research | en |
dc.date.accessioned | 2015-06-29T22:07:04Z | en |
dc.date.available | 2015-06-29T22:07:04Z | en |
dc.date.issued | 1985 | en |
dc.description.abstract | Many 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.degree | Ph. D. | en |
dc.format.extent | x, 294 leaves | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.uri | http://hdl.handle.net/10919/53898 | en |
dc.language.iso | en_US | en |
dc.publisher | Virginia Polytechnic Institute and State University | en |
dc.relation.isformatof | OCLC# 12833137 | en |
dc.rights | In Copyright | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
dc.subject.lcc | LD5655.V856 1985.S827 | en |
dc.subject.lcsh | Electric utilities -- Planning -- Data processing | en |
dc.subject.lcsh | Electric utilities -- Mathematical models | en |
dc.subject.lcsh | Renewable energy sources | en |
dc.subject.lcsh | Programming (Mathematics) | en |
dc.title | Renewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utility | en |
dc.type | Dissertation | en |
dc.type.dcmitype | Text | en |
thesis.degree.discipline | Industrial Engineering and Operations Research | en |
thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
thesis.degree.level | doctoral | en |
thesis.degree.name | Ph. D. | en |
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