3 Carbon footprint and cost evolution of green technologies
Internship or Master thesis
Summer 2023
IPESE, EPFL, Sion, Switzerland.
3.1 Context
Is the carbon footprint and cost of the renewable technologies today able to represent those ones in the future? The answer is obviously “NO”! From the experience of PV panels, it is summarized that the price of a technology will decrease with its global shipment. This phenomenon is named as “learning effect”. There are a number of reasons behind the learning effect, such as scaling effect, learning by doing and research & development efforts. However, there is lacking a button-up model which can reveal the mechanisms behind the price reduction. In addition, the carbon footprint evolution is less addressed by researchers but with great importance.
3.2 Problem statement
This project will address the cost and carbon footprint evolution based on a detailed scaling model developed in 2022 by Lu. PEMEC and SOEC technologies will be considered as a typical technology. The candidate will continue to complete and improve the existing cost model developed on R. The detailed tasks will include the following:
Collect the cost and carbon footprint data of the electrolysis technologies.
Connect the existing model with the LCA database – ecoinvent and conducted the LCA of the electrolysis technologies.
Introduce learning by doing and R&D efforts into the model and forecast the price evolution of the electrolysis technologies.
Simplify the model by machine learning or some other artificial methods.
3.3 For candidates
The successful candidate is expected to have the following profiles:
High-level motivation and ability to work in an autonomous environment.
Good knowledge of life cycle assessment and a concrete background in mathematics.
Coding skills (R) are a plus.
System thinker and problem-solver oriented.
The candidate will develop different competencies, such as project management experience, technical knowledge with renewables and mathematical optimizations, and critical thinking skills, etc.
3.4 Supervising
This project will be supervised by Prof. François Maréchal from EPFL. Yi Zhao will serve as the assistant of the project.
If interested, please take contact with Yi Zhao (yi.zhao@epfl.ch) attaching your CV, Cover Letter and transcript of records (Bachelor’s and Master’s). Short-listed candidates will be interviewed. Early applications are encouraged.