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. Professor Anderson is working to integrate renewable energy into existing energy markets. She is very interested in wind energy, which she wants to help harvest more effectively. Additionally, she studies rapidly evolving energy technologies, changing policy, and fickle markets. Given her focus on systems modeling and optimization, she is poised to overcome some of the obstacles to a sustainable, renewable energy future.
Professor Anderson’s research interests focus on the application of systems modeling and optimization to energy and the environment. Current projects include mitigation of wind generation uncertainty through the use of other renewable energy sources, the cost of wind energy uncertainty on existing power systems, and the implications of process uncertainties in biofuels production outcomes.