Energy and Uncertainty: Models and Algorithms for Complex Energy Systems

Abstract

The problem of controlling energy systems (generation, transmission, storage, investment) introduces a number of optimization problems which need to be solved in the presence of different types of uncertainty. We highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, we describe a modeling framework based around five fundamental dimensions which is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where we identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and lookahead policies. This organization unifies a number of competing strategies under a common umbrella.