This project will adopt data-driven analysis to establish the correlation with DA-RT price bias and analyze the underlying risk for solar producers. The project will develop interpretable clustering algorithms to classify and explain data correlations, make projects on future risk evolution when solar penetration in the system increases. A further target for this project, depending on the student’s progress, is to develop new physical option contracts for flexibility resources for mitigating such market risk, which will help the utilization of alternative flexibility resources such as energy storage.
- Gather and format data from California ISO OASIS platform (or from another ISO).
- Perform data analysis and design data visualizations.
- Establish correlation models and perform future projects for the next 20 years.
- Perform risk and value analysis for energy option contract design.
Direct Supervisor: Bolun Xu
The student should be familiar with a common scientific languages such as Matlab, Julia,
Python, and is expected to have experience with at least on of the following areas:
- Machine learning basics: clustering, regression;
- Electricity markets, energy systems, renewable energy;
- Economics and risk management.