Friday, November 16, 2018
11:00am-12:00pm
MS2172
Medical Sciences Building
Stochastic Modeling and Optimization for Offshore Wind Integration: How proper modeling exposes the challenges of high penetrations of renewables
Warren B. Powell, Dept. of Operations Research and Financial Engineering Princeton University
Abstract
Many have assumed that the solution to climate warming requires converting to an energy system based heavily on renewables. In this talk, I will describe an in-depth study of the integration of high levels of energy from offshore wind farms using a model called SMART-ISO. The problem required the proper modeling of the power grid. In addition, we put considerable effort into the proper modeling of the variability and uncertainty of wind over multiple time scales.
We then paid careful attention to the accurate representation of the timing of decisions for planning energy generation. This included capturing the sequencing of decisions and information over multiple horizons, including day ahead, hour ahead, 15 minutes, 5 minutes and real-time. Then, we addressed the problem of making robust decisions in the presence of the uncertainty of wind. We were able to show that stochastic programming, popular with academics and the national labs, was fundamentally flawed. Instead, we illustrate the power of a strategy widely used in industry that we call a parametric cost function approximation. We close by demonstrating that even at relatively modest levels of energy from wind, the uncertainty of wind will require surprisingly high levels of spinning reserves.
Biosketch
Warren B. Powell is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981 after receiving his BSE from Princeton University and Ph.D. from MIT. He is the founder and director of the laboratory for Computational Stochastic Optimization and Learning (CASTLE Labs), which spans contributions to models and algorithms in stochastic optimization, with applications to energy systems, transportation, health and medical research, business analytics, and the laboratory sciences (see www.castlelab.princeton.edu). He has pioneered the use of approximate dynamic programming for high-dimensional applications in freight transportation, where his projects have twice been recognized as an Edelman finalist, and one won the Daniel Wagner prize. This research led him into the field of optimal learning for optimizing expensive functions using the knowledge gradient. A Fellow of Informs, he has served in a range of service positions spanning the Society for Transportation and Logistics, the Informs Computing Society, and the Informs Optimization Society. He has two books and over 200 papers, and is working on a new book “Optimization under Uncertainty: A Unified Framework.” He has produced 50 graduate students and post-docs, and has supervised almost 200 undergraduate senior theses (see http://tinyurl.com/powellacademictree).
