Tuesday, February 11, 2014 - 4:00pm to Wednesday, February 12, 2014 - 3:55pm
Event Calendar Category
LIDS Seminar Series
Building and Room Number
Pressing environmental issues and concerns over energy security are driving worldwide interest in renewable energy resources. Unlike conventional generation, however, power from wind and solar resources is inherently variable in its supply. It is non-dispatchable, highly intermittent, and difficult to forecast. This intrinsic variability in supply represents the most important obstacle to the large-scale grid integration of renewables. In this lecture, we will provide a system theoretic perspective of the critical challenges facing deep renewable energy integration and formally examine the role of energy storage in mitigating the attending costs of integration. In particular, we aim to quantify the value of energy storage capacity within existing market constructs. Going beyond numerical sensitivities, which often prove cumbersome to compute and limited in terms of their explanatory power, we derive explicit formulae that reveal a fundamental connection between the value derived from storage capacity and particular spectral properties of the random processes driving the system. These simple expressions not only shed light on the correct measure of statistical variation in quantifying the value of storage, but also provide a tractable empirical method for marginal value calculations from time series data -- requiring minimal to no prior distributional assumptions on the supply processes. We close by discussing the practical implications of our analyses and open research questions.
Eilyan Bitar is currently an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. His research interests include power systems, stochastic control, and mechanism design. Prior to joining Cornell in the Fall 2012, he was engaged as a Postdoctoral Fellow in the department of Computing and Mathematical Science (CMS) at the California Institute of Technology and at the University of California, Berkeley in Electrical Engineering and Computer Science during the 2011-12 academic year. A native Californian, he received both his Ph.D. (2011) and B.S. (2006) from the University of California, Berkeley.