SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy

  • Authors:

  • Warren B. Powell;Abraham George;Hugo Simão;Warren Scott;Alan Lamont;Jeffrey Stewart

  • Affiliations:

  • Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Science and Technology Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550;Science and Technology Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550

  • Venue:
  • INFORMS Journal on Computing
  • Year:
  • 2012

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Abstract

We address the problem of modeling energy resource allocation, including dispatch, storage, and the long-term investments in new technologies, capturing different sources of uncertainty such as energy from wind, demands, prices, and rainfall. We also wish to model long-term investment decisions in the presence of uncertainty. Accurately modeling the value of all investments, such as wind turbines and solar panels, requires handling fine-grained temporal variability and uncertainty in wind and solar in the presence of storage. We propose a modeling and algorithmic strategy based on the framework of approximate dynamic programming (ADP) that can model these problems at hourly time increments over an entire year or several decades. We demonstrate the methodology using both spatially aggregate and disaggregate representations of energy supply and demand. This paper describes the initial proof of concept experiments for an ADP-based model called SMART; we describe the modeling and algorithmic strategy and provide comparisons against a deterministic benchmark as well as initial experiments on stochastic data sets.