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

arXiv: https://arxiv.org/abs/2203.08984 · HTML: https://mlsanity.com/paper/2203.08984/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach](https://mlsanity.com/paper/2208.02319/) (2022)
- [Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning](https://mlsanity.com/paper/2304.10702/) (2023)
- [Differentiable Predictive Control for Large-Scale Urban Road Networks](https://mlsanity.com/paper/2406.10433/) (2024)
- [Deep Koopman Economic Model Predictive Control of a Pasteurisation Unit](https://mlsanity.com/paper/2511.04437/) (2025)

## Abstract (arXiv)

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.