Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage

Daniel F. Salas () and Warren B. Powell ()
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Daniel F. Salas: Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540
Warren B. Powell: Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540

INFORMS Journal on Computing, 2018, vol. 30, issue 1, 106-123

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.

Keywords: dynamic programming-optimal control; industries: electric; programming: stochastic (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:30:y:2018:i:1:p:106-123

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