Abstract

A Unified Framework for Stochastic Optimization in Energy
Warren B. Powell
Dept. of Operations Research and Financial Engineering
Princeton University

Energy systems offer a variety of forms of uncertainty that have to be accommodated to ensure a reliable source of power. The modeling of these sequential decision problems under uncertainty has lacked the kind of canonical framework long enjoyed by deterministic problems. I will introduce a modeling framework that is completely general, which involves three mathematical challenges: 1) machine learning (there are up to five classes of learning problems), 2) uncertainty modeling, and 3) designing policies, which are functions for making decisions. There are two fundamental strategies for creating policies, each of which further divides into two subclasses, creating four classes of policies. These four (meta)classes of policies are universal, in that any method used to solve a sequential decision problem will be drawn from this set. The four classes are illustrated in the context of several applications in energy systems. An energy storage application is then used to demonstrate that each of the four classes of policies might be best depending on the characteristics of the data.