Benchmarking a scalable approximate dynamic programming algorithm for stochastic control of grid-level energy storage

Daniel F. Salas, Warren Buckler Powell

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

We present and benchmark an approximate dynamic programming algorithm that is capable of designing near-optimal control policies for a portfolio of heterogenous storage devices in a time-dependent environment, where wind supply, demand, and electricity prices may evolve stochastically. We found that the algorithm was able to design storage policies that are within 0.08% of optimal on deterministic models, and within 0.86% on stochastic models. We use the algorithm to analyze a dual-storage system with different capacities and losses, and show that the policy properly uses the low-loss device (which is typically much more expensive) for high-frequency variations. We close by demonstrating the algorithm on a five-device system. The algorithm easily scales to handle heterogeneous portfolios of storage devices distributed over the grid and more complex storage networks.

Original languageEnglish (US)
Pages (from-to)106-123
Number of pages18
JournalINFORMS Journal on Computing
Volume30
Issue number1
DOIs
StatePublished - Dec 1 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

Keywords

  • Dynamic programming-optimal control
  • Industries: electric
  • Programming: stochastic

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