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

Conference · Fri Jul 02 04:00:00 EDT 2021

OSTI ID:1819913

King, Ethan ^[1]; Bakker, Craig KR ^[1]; Bhattacharya, Arnab ^[1]; Chatterjee, Samrat ^[1]; Pan, Feng ^[1]; Oster, Matthew R. ^[1]; Perkins, Casey J. ^[1]

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1. BATTELLE (PACIFIC NW LAB)

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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1819913
• Report Number(s): PNNL-SA-159920
• Country of Publication: United States
• Language: English