Selma Sharaf, MS '25 Civil and Environmental Engineering, Atmosphere/Energy
Graduate Fellow, California ISO
I spent the summer working at the California Independent System Operator (CAISO) within the Market Performance and Advanced Analytics team. CAISO manages the flow of electricity across the high-voltage power lines that make up over 80 percent of California’s electric power grid. CAISO also operates a competitive auction-style wholesale electricity market and oversees transmission planning.
One of my projects involved developing an optimization tool, specifically a simplified offline market clearing model. I built up the model in the R programming language (with an Excel-based input file), gradually incorporating multiple generators, demand bids, an object-oriented programming structure, multi-interval optimization with ramping constraints, and storage. This tool is intended to allow the team to test small changes to market features without running their more complex models.
The second project I worked on is related to modeling the state of charge of storage resources that participate in the CAISO markets. As an increasing amount of storage is added to the grid, CAISO has been developing policies for modeling and managing these resources. In November 2023, the state of charge equation was updated to account for regulation awards. “Regulation up” and “regulation down” products are used to keep the grid balanced at 60 Hz. However, a 10 MW regulation up award, for instance, does not necessarily mean that 10 MW will be discharged. For this reason, hourly multipliers (attenuation factors) are needed to represent the actual percentage utilization of an award. The goal of this project is to use predictive analysis and machine learning to develop a time series model for these attenuation factors based on data such as forecasted demand, renewable generation, and resource deviation.
Throughout the summer, I’ve gained new technical skills as well as useful knowledge about the CAISO markets. I’ve learned about working with SQL databases, developing time-series models, and conducting optimization using R and the “OMPR” package. I’ve had the opportunity to work with very knowledgeable and supportive mentors, Dr. Guillermo Bautista Alderete and Dr. Kun Zhao, both of whom have prioritized my learning. I’ve also engaged in a lot of intern programming at CAISO, including frequent “lunch and learns” and social events such as a virtual paint day.
After the summer, I’ll be continuing my MS in Atmosphere/Energy, and my experience at CAISO will be useful as I undertake research this fall. In the future, I plan to continue working in electricity system planning. This fellowship has been a valuable experience, with CAISO truly at the forefront of many of the opportunities and challenges that are accompanying the energy transition.
