Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem

Conference · Mon Sep 05 04:00:00 EDT 2022

DOI:https://doi.org/10.23919/ACC53348.2022.9867379· OSTI ID:1894496

King, Ethan ^[1]; Drgona, Jan ^[1]; Tuor, Aaron R. ^[1]; Abhyankar, Shrirang G. ^[1]; Bakker, Craig KR ^[1]; Bhattacharya, Arnab ^[1]; Vrabie, Draguna L. ^[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, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain offline parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies based on identified Koopman operator (KO) model of the system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original optimization-based DED approach on a 9-bus test power grid network.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1894496
• Report Number(s): PNNL-SA-167421
• Country of Publication: United States
• Language: English