Tutorial on Stochastic Optimization in Energy - Part I: Modeling and Policies

Warren Buckler Powell, Stephan Meisel

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

There is a wide range of problems in energy systems that require making decisions in the presence of different forms of uncertainty. The fields that address sequential, stochastic decision problems lack a standard canonical modeling framework, with fragmented, competing solution strategies. Recognizing that we will never agree on a single notational system, this two-part tutorial proposes a simple, straightforward canonical model (that is most familiar to people with a control theory background), and introduces four fundamental classes of policies which integrate the competing strategies that have been proposed under names such as control theory, dynamic programming, stochastic programming and robust optimization. Part II of the tutorial illustrates the modeling framework using a simple energy storage problem, where we show that, depending on the problem characteristics, each of the four classes of policies may be best.

Original languageEnglish (US)
Article number7097741
Pages (from-to)1459-1467
Number of pages9
JournalIEEE Transactions on Power Systems
Volume31
Issue number2
DOIs
StatePublished - Mar 2016

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Keywords

  • Approximate dynamic programming
  • dynamic programming
  • energy systems
  • optimal control
  • reinforcement learning
  • robust optimization
  • stochastic optimization
  • stochastic programming

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