arXiv subjects

Nikolas Recke

Publications and source records attributed to Nikolas Recke.

2 recordsLinked to original sources

arXiv2026

Scenario-based Data-Enabled Predictive Control: Robustification via the Scenario Approach

This paper proposes Scenario-Based Data-Enabled Predictive Control (Scenario-DeePC), which integrates the scenario optimization framework into Data-enabled Predictive Control (DeePC) to provide probabilistic guarantees on constraint satisfaction under uncertainty. In contrast to existing methods, the uncertainty is characterized directly from data by constructing empirical disturbance scenarios from observed prediction errors, keeping the method fully consistent with the data-driven philosophy of DeePC and free of distributional assumptions. We establish the supporting theory, including a distribution-free probabilistic guarantee on constraint satisfaction and recursive feasibility of the receding-horizon scheme. An adaptive extension collects scenarios online, enabling the controller to adjust to changing noise characteristics, disturbances, and operating-point-dependent model mismatches. The approach is demonstrated on a linear Boeing 747 model and a nonlinear two-tank system, showing a significant reduction in constraint violations compared to standard DeePC, while maintaining comparable tracking performance in nominal conditions and improving tracking accuracy in the nonlinear setting.

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arXiv2026

Distributionally Robust Model Predictive Control for Virtual Power Plants

This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the interaction between buildings, battery storage, and renewable generation, all influenced by exogenous weather and market signals. The proposed approach integrates data-driven forecasting with quantile-based uncertainty quantification to construct time-varying Wasserstein ambiguity sets that adapt to forecast dispersion and distributional shifts. This yields a tractable DR-MPC formulation that incorporates predictive distribution information directly into real-time decision making. The method is evaluated using real weather and market data from a Nordic case study across two seasonal scenarios. The results show that DR-MPC improves economic performance relative to standard forecast-based MPC when the ambiguity radius is chosen appropriately, with consistent gains of up to 0.8% for small radii across both seasonal scenarios. Larger radii become overly conservative and reduce revenue, underscoring the importance of proper radius selection. These findings demonstrate the practical value of distributionally robust optimization for uncertainty-aware VPP operation.

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