A machine learning approach to evaluating renewable energy technology : an alternative LACE study on solar photo-voltaic (PV)

Currently, renewable technologies are often evaluated using the Levelized cost of electricity (LCOE), which is a measure of building and operating a generating plant over an assumed αnancial life and duty cycle. Naturally, instead of only measuring the cost, a more holistic approach would be to also assess the economical value of the renewable generating technology. One approach to this would be to measure the Levelized Avoided Cost of Electricity (LACE), which considers what it will cost the grid to generate electricity using renewable technology, amortized over its lifetime. However, estimating avoided cost can be challenging since it requires knowledge of how the renewable technology would perform in electricity generation, especially when taking into account a projected future period. Naturally this would have repercussions in policies adopting greater renewable technologies, further emphasising the importance of an adequate measure of evaluating renewable technology.

Name thesis:degree_name

Master

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

2020

Author dc:creator

• Ng, Benny Siu Hon.

Advisor dc:contributor.advisor

• Christopher R. Knittel.

• Institute for Data, Systems, and Society.
• Technology and Policy Program.
• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127172

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127172