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Title: Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator

Abstract

The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that reduces overall generation costs. However, in contrast to T-ED, DED is a nonlinear, non-convex optimization problem that is computationally prohibitive to solve. We introduce a machine learning-based operator-theoretic approach for solving the DED problem efficiently. Specifically, we develop a novel discrete-time Koopman Operator (KO) formulation that embeds domain information into the structure of the KO to learn high-fidelity approximations of the generator dynamics. Using the KO approximation, the DED problem can be reformulated as a computationally tractable linear program (abbreviated DED-KO). We demonstrate the high solution quality and computational-time savings of the DED-KO model over the original DED formulation on a 9-bus test system.

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1819913
Report Number(s):
PNNL-SA-159920
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Twelfth ACM International Conference on Future Energy Systems (e-Energy '21), June 28-July 2, 2021, Virtual, Online
Country of Publication:
United States
Language:
English

Citation Formats

King, Ethan, Bakker, Craig KR, Bhattacharya, Arnab, Chatterjee, Samrat, Pan, Feng, Oster, Matthew R., and Perkins, Casey J. Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator. United States: N. p., 2021. Web.
King, Ethan, Bakker, Craig KR, Bhattacharya, Arnab, Chatterjee, Samrat, Pan, Feng, Oster, Matthew R., & Perkins, Casey J. Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator. United States.
King, Ethan, Bakker, Craig KR, Bhattacharya, Arnab, Chatterjee, Samrat, Pan, Feng, Oster, Matthew R., and Perkins, Casey J. 2021. "Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator". United States.
@article{osti_1819913,
title = {Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator},
author = {King, Ethan and Bakker, Craig KR and Bhattacharya, Arnab and Chatterjee, Samrat and Pan, Feng and Oster, Matthew R. and Perkins, Casey J.},
abstractNote = {The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that reduces overall generation costs. However, in contrast to T-ED, DED is a nonlinear, non-convex optimization problem that is computationally prohibitive to solve. We introduce a machine learning-based operator-theoretic approach for solving the DED problem efficiently. Specifically, we develop a novel discrete-time Koopman Operator (KO) formulation that embeds domain information into the structure of the KO to learn high-fidelity approximations of the generator dynamics. Using the KO approximation, the DED problem can be reformulated as a computationally tractable linear program (abbreviated DED-KO). We demonstrate the high solution quality and computational-time savings of the DED-KO model over the original DED formulation on a 9-bus test system.},
doi = {},
url = {https://www.osti.gov/biblio/1819913}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {7}
}

Conference:
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