Network partitioning algorithms for electricity consumer clustering
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/122917 |
| _version_ | 1826209209109184512 |
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| author | Oladeji, Olamide. |
| author2 | Ignacio Perez-Arriaga. |
| author_facet | Ignacio Perez-Arriaga. Oladeji, Olamide. |
| author_sort | Oladeji, Olamide. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T14:18:54Z |
| format | Thesis |
| id | mit-1721.1/122917 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:18:54Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1229172022-01-31T19:00:36Z Network partitioning algorithms for electricity consumer clustering Oladeji, Olamide. Ignacio Perez-Arriaga. Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Technology and Policy Program. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Institute for Data, Systems, and Society Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Technology and Policy Program Massachusetts Institute of Technology. Engineering Systems Division Technology and Policy Program Institute for Data, Systems, and Society. Technology and Policy Program. Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2018 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 97-103). In many developing countries, access to electricity remains a significant challenge. Electrification planners in these countries often have to make important decisions on the mode of electrification and the planning of electrical networks for those without access, while under resource constraints. To facilitate the achievement of universal energy access, the Reference Electrification Model (REM), a computational model capable of providing techno-economic analysis and data-driven decision support for these planning efforts, has been developed. Primary among REM's capabilities is the recommendation of the least-cost mode of electrification - i.e by electric grid extension or off-grid systems - for non-electrified consumers in a region under analysis, while considering technical, economic and environmental constraints. This is achieved by the identification of consumer clusters (either as clusters of off-grid microgrids, stand-alone systems or grid-extension projects) using underlying clustering methods in the model. This thesis focuses on the development and implementation of partitioning algorithms to achieve this purpose. Building on previously implemented efforts on the clustering and recommendation capabilities of REM, this work presents the development, analysis and performance evaluation of alternative approaches to the consumer clustering process, in comparison with REM's previously incorporated clustering methodology. Results show that the alternative methodology proposed can compare favorably with the hitherto implemented method in REM. Consequently, the integration of the pro- posed network partitioning procedures within REM, as well as some potential future research directions, is discussed. Finally, this thesis concludes with a discourse on the social and regulatory aspects of energy access and electricity planning in developing countries, providing some perspectives on the development policies and business models that complement the technological contributions of this work. by Olamide Oladeji. S.M. in Technology and Policy S.M. S.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-12T18:13:25Z 2019-11-12T18:13:25Z 2018 2018 2018 Thesis https://hdl.handle.net/1721.1/122917 1126790961 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 103 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Institute for Data, Systems, and Society. Technology and Policy Program. Electrical Engineering and Computer Science. Oladeji, Olamide. Network partitioning algorithms for electricity consumer clustering |
| title | Network partitioning algorithms for electricity consumer clustering |
| title_full | Network partitioning algorithms for electricity consumer clustering |
| title_fullStr | Network partitioning algorithms for electricity consumer clustering |
| title_full_unstemmed | Network partitioning algorithms for electricity consumer clustering |
| title_short | Network partitioning algorithms for electricity consumer clustering |
| title_sort | network partitioning algorithms for electricity consumer clustering |
| topic | Institute for Data, Systems, and Society. Technology and Policy Program. Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/122917 |
| work_keys_str_mv | AT oladejiolamide networkpartitioningalgorithmsforelectricityconsumerclustering |