Neural Network-Assisted Model Predictive Control for Implicit Balancing

TL;DR

This paper introduces a neural network-enhanced model predictive control approach for implicit balancing in power grids, improving decision accuracy and computational efficiency by modeling market uncertainties with convex neural networks.

Contribution

It presents a novel data-driven market model using input convex neural networks integrated into MPC, addressing previous modeling limitations and enhancing grid stability management.

Findings

Improved MPC decision quality with the new model.

Reduced computational time in market simulations.

Enhanced accuracy in capturing market uncertainties.

Abstract

In Europe, balance responsible parties can deliberately take out-of-balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit, a practice called implicit balancing. Model predictive control (MPC) is widely adopted as an effective approach for implicit balancing. The balancing market model accuracy in MPC is critical to decision quality. Previous studies modeled this market using either (i) a convex market clearing approximation, ignoring proactive manual actions by TSOs and the market sub-quarter-hour dynamics, or (ii) machine learning methods, which cannot be directly integrated into MPC. To address these shortcomings, we propose a data-driven balancing market model integrated into MPC using an input convex neural network to ensure convexity while capturing uncertainties. To keep the core network computationally efficient, we incorporate attention-based input gating mechanisms to remove irrelevant data. Evaluating on Belgian data shows that the proposed model both improves MPC decisions and reduces computational time.