Current Courses

Data, Environment, and Society (ERG 131 / ESPM 169)

This course teaches students to build, estimate, and interpret models that describe phenomena in the broad area of energy and environmental decision-making. Students leave the course as both critical consumers and responsible producers of data-driven analysis.

The effort is divided between:

  • Learning a suite of data-driven modeling and prediction tools, including linear model selection methods, classification and regression trees, and support vector machines
  • Building the programming and computing expertise to use those tools
  • Developing the ability to formulate and answer resource allocation questions within energy and environment contexts

We work in Python in this course. Students must have taken Data 8 (or equivalent) before enrolling. The course is designed to complement and reinforce Berkeley’s data science curriculum.

Text: James et al., An Introduction to Statistical Learning

Modeling and Control of Power Electronics-Dominated Power Systems (EECS 226D / ERG 226D)

This graduate course covers modeling and analysis of modern power systems with high penetrations of inverter-based resources, including control design for grid-connected and islanded operation.

Texts:

  • Machowski, Bialek, and Bumby, Power System Dynamics: Stability and Control
  • Yazdani and Iravani, Voltage-Sourced Converters in Power Systems

Past Courses and Special Topics

  • UNIFI Seminar Series (Fall 2021) — A special topics seminar on inverter-based resources and power system dynamics. More information here.