Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.
| 第一著者: | |
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| その他の著者: | |
| フォーマット: | 学位論文 |
| 言語: | eng |
| 出版事項: |
Massachusetts Institute of Technology
2014
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| オンライン･アクセス: | http://hdl.handle.net/1721.1/90724 |
| _version_ | 1826218089032712192 |
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| author | Seelhof, Michael |
| author2 | Mort Webster. |
| author_facet | Mort Webster. Seelhof, Michael |
| author_sort | Seelhof, Michael |
| collection | MIT |
| description | Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014. |
| first_indexed | 2024-09-23T17:13:59Z |
| format | Thesis |
| id | mit-1721.1/90724 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:13:59Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/907242019-04-10T17:36:11Z Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques Seelhof, Michael Mort Webster. System Design and Management Program. Massachusetts Institute of Technology. Engineering Systems Division. System Design and Management Program. Engineering Systems Division. System Design and Management Program. Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 179-183). 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. by Michael Seelhof. S.M. in Engineering and Management 2014-10-08T15:25:13Z 2014-10-08T15:25:13Z 2014 2014 Thesis http://hdl.handle.net/1721.1/90724 891139083 eng 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. http://dspace.mit.edu/handle/1721.1/7582 183 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Engineering Systems Division. System Design and Management Program. Seelhof, Michael Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title_full | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title_fullStr | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title_full_unstemmed | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title_short | Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| title_sort | long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques |
| topic | Engineering Systems Division. System Design and Management Program. |
| url | http://hdl.handle.net/1721.1/90724 |
| work_keys_str_mv | AT seelhofmichael longterminfrastructureinvestmentsunderuncertaintyintheelectricpowersectorusingapproximatedynamicprogrammingtechniques |