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Python Pandas with timeseries data

Dr. Wagner covers the basics of using Python’s Pandas module to read, manipulate, and analyze time series data commonly encountered in energy system simulation.

Illuminating Science Podcast Interview

Dr. Wagner was a guest on the Illuminating Science podcast, where he and host John Binkley talked in depth about concentrating solar power and the work ongoing at the ESOLab.

Episode description:

Dr. Mike Wagner is an assistant professor of mechanical engineering at the University of Wisconsin Madison.  His work focuses on the optimization of renewable energy production, in particular from concentrating solar plants, in order to maximize efficiency and profitability of those systems.  We talk about how concentrating solar plants work and what advantages they offer when compared to other renewables like wind or photovoltaics.  We also discuss grid frequency and stability and how the increasing presence of variable renewable power sources is creating challenges in terms of maintaining grid frequency and stability.

Useful research outcomes

Overview of Linear and Mixed Integer Programming

This short series of lectures covers the basics of linear and mixed-integer programming with a focus on implementation in the Pyomo modeling toolkit for Python. Videos present background information without going deep into underlying theory. More detail is given on practical implementation methods, solver settings, and solver log interpretation. The series begins with background information on the Simplex method, presents linear programming examples, discusses the branch and bound technique, and extends prior examples to use integer variables. The final videos discuss practical methods for improving complex and slow-solving models.