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

TL;DR

This paper introduces a Koopman-based differentiable predictive control method that efficiently solves the dynamics-aware economic dispatch problem, significantly reducing computation time while maintaining high solution quality in power grid management.

Contribution

It presents a novel data-driven approach using Koopman operators and differentiable programming to explicitly learn control policies for the DED problem, enabling real-time application.

Findings

Achieves five orders of magnitude reduction in computation time.

Maintains high solution quality comparable to traditional optimization methods.

Demonstrates effectiveness on a 9-bus power grid network.

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 leads to reduced 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 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 using an identified Koopman operator (KO) model of the power system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original online optimization-based DED approach on a 9-bus test power grid network.