Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper addresses limitations in implicit balancing market models used within Model Predictive Control (MPC) by introducing a novel data-driven approach. They use an input convex neural network (ICNN) with attention-based input gating to model the balancing market, ensuring convexity for MPC integration while capturing market uncertainties. Evaluation on Belgian data demonstrates improved MPC decision-making and reduced computational time compared to existing methods.
Achieve better implicit balancing decisions and faster computation by using neural networks inside your MPC controller.
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.