Enhanced techniques to plan rural electrical networks using the Reference Electrification Model

Although there have recently been many significant digital and technical advances in the electric power industry, rural electrification remains a pressing issue in the developing countries around the world. The International Energy Agency estimates that there are 1.2 billion people globally who lack access to electricity. Planning electrical networks to provide energy access to these primarily rural consumers is complicated by the lack of accurate data by electric utilities in these areas and the increased prevalence of microgrids and low-cost individual energy systems which challenges the traditional definition of energy services. Advanced computational planning tools can allow planners and policymakers to take resource constraints, environmental considerations, interactions between off-grid and traditional grid extension projects, and many other factors into account when designing rural electrification policies and plans. The goal of this thesis is to contribute to the development and application of the Reference Electrification Model (REM), a decision support tool which can help planners design optimal electrical networks for rural electrification purposes. In this thesis, I develop the functionalities of REM through several case studies. I also address the topics of estimating the electrification status of buildings and calculating the cost of upstream network reinforcements due to new load additions in the system. This research emphasizes the need for computational tools like REM to develop both feasible network designs as well as viable energy policies and regulations in order to advance efforts related to rural electrification and energy access around the world.

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Cotterman, Turner

Advisor dc:contributor.advisor

• Jose Ignacio Pérez-Arriaga and Claudio Vergara.

• Institute for Data, Systems, and Society.
• Engineering Systems Division.
• Technology and Policy Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111229

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111229