Neural Network-Assisted Model Predictive Control for Implicit Balancing
This work addresses the challenge of accurate market modeling for grid stability and profit in implicit balancing, offering an incremental improvement over existing approximations and machine learning methods.
The paper tackled the problem of modeling the balancing market for implicit balancing in Europe, proposing a data-driven model using an input convex neural network integrated into model predictive control, which improved decision quality and reduced computational time on Belgian data.
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.