TY - JOUR
T1 - Benchmarking a scalable approximate dynamic programming algorithm for stochastic control of grid-level energy storage
AU - Salas, Daniel F.
AU - Powell, Warren Buckler
N1 - Funding Information:
Funding:This research was supported, in part, by National Science Foundation [Contract ECCS-1127975], and the SAP Initiative for Energy Systems Research. SupplementalMaterial: The online supplement is available at https://doi.org/10.1287/ijoc.2017.0768.
Funding Information:
This research was supported, in part, by National Science Foundation [Contract ECCS-1127975], and the SAP Initiative for Energy Systems Research.
Publisher Copyright:
© 2017 INFORMS.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Dynamic programming-optimal control
KW - Industries: electric
KW - Programming: stochastic
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U2 - 10.1287/ijoc.2017.0768
DO - 10.1287/ijoc.2017.0768
M3 - Article
AN - SCOPUS:85041895165
VL - 30
SP - 106
EP - 123
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
SN - 1091-9856
IS - 1
ER -