| dc.contributor.advisor | Mort Webster. | en_US |
| dc.contributor.author | Seelhof, Michael | en_US |
| dc.contributor.other | System Design and Management Program. | en_US |
| dc.date.accessioned | 2014-10-08T15:25:13Z | |
| dc.date.available | 2014-10-08T15:25:13Z | |
| dc.date.copyright | 2014 | en_US |
| dc.date.issued | 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/90724 | |
| dc.description | Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014. | en_US |
| dc.description | Cataloged from PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 179-183). | en_US |
| dc.description.abstract | A computer model was developed to find optimal long-term investment strategies for the electric power sector under uncertainty with respect to future regulatory regimes and market conditions. The model is based on a multi-stage problem formulation and uses approximate dynamic programming techniques to find an optimal solution. The model was tested under various scenarios. The model results were analyzed with regards to the optimal first-stage investment decision, the final technology mix, total costs, the cost of ignoring uncertainty and the cost of regulatory uncertainty. | en_US |
| dc.description.statementofresponsibility | by Michael Seelhof. | en_US |
| dc.format.extent | 183 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Engineering Systems Division. | en_US |
| dc.subject | System Design and Management Program. | en_US |
| dc.title | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | S.M. in Engineering and Management | en_US |
| dc.contributor.department | System Design and Management Program. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Engineering Systems Division | |
| dc.identifier.oclc | 891139083 | en_US |
