# Neural Network-Assisted Model Predictive Control for Implicit Balancing

arXiv: https://arxiv.org/abs/2604.01805 · HTML: https://mlsanity.com/paper/2604.01805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism](https://mlsanity.com/paper/2401.00015/) (2023)
- [Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme](https://mlsanity.com/paper/2505.05153/) (2025)
- [Probabilistic forecasting of power system imbalance using neural network-based ensembles](https://mlsanity.com/paper/2404.14836/) (2024)
- [Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search](https://mlsanity.com/paper/2411.04011/) (2024)
- [Model Predictive Control-Guided Reinforcement Learning for Implicit Balancing](https://mlsanity.com/paper/2510.04868/) (2025)
- [Gaming Strategies in European Imbalance Settlement Mechanisms](https://mlsanity.com/paper/2505.14133/) (2025)

## Abstract (arXiv)

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.