Toward scalable methods for managing uncertainty under high penetration of renewable energy resources
The relationship between greenhouse gas emissions and climate change is well known, and as a result our incentive to incorporate cleaner sources into global energy systems continues to grow. This is evidenced by EPA’s Clean Power Plan that, for the first time, has specifically targeted greenhouse gas emissions from existing power plants. This paradigm shift is moving a complex system along the spectrum from large, centralized generation to smaller, distributed, and less controllable sources. This change in resource characteristics demands new operational methods that can manage increasing uncertainty and variability in both demand and supply.
One example is the unit commitment decision, which aims to select the optimal generation schedule for the future, based on forecasted system conditions. This non-convex problem is subject to the myriad constraints of the power system, and the introduction of uncertainty can make the dimensionality intractable for traditional methods. In this seminar, we will discuss the challenges introduced by this uncertainty and some ways that it can be incorporated into the the unit commitment decision in a computationally tractable way.
Lindsay Anderson is an Assistant Professor in the Department of Biological and Environmental Engineering and the Norman R. Scott Sesquicentennial Faculty Fellow. She earned Bachelors and Masters degrees in Environmental Engineering, before becoming enamored of computational models and pursuing a PhD in Applied Mathematics.
Her interests include the integration of renewable energy in energy systems and markets, as she strives in develop effective methods for managing uncertainty to maximize efficacy of renewable resources.
Professor Anderson is working to integrate renewable energy into existing energy markets.