An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants

2025-02-18

AI Summary

To address the challenge of coordinated power regulation in hybrid photovoltaic-wind-storage power plants (HPPs) under meteorological uncertainty, this paper proposes an uncertainty-aware, data-driven subspace predictive controller. The method innovatively integrates uncertainty quantification into the subspace predictive control framework, enabling real-time, adaptive compensation for the stochastic nature of wind and solar generation through dynamic identification of multi-source renewable system dynamics. Evaluated on a 4-MW real-world HPP, the controller achieves precise tracking of actual load profiles, reducing tracking error by 37% and significantly enhancing both short-term power prediction accuracy and dispatch robustness under weather-induced disturbances. The core contribution lies in establishing a novel subspace control paradigm that unifies data-driven modeling with probabilistic uncertainty representation—marking the first integration of rigorous uncertainty quantification into subspace predictive control for renewable energy systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components and meet a desired power demand despite the presence of weather uncertainties. An uncertainty-aware data-driven predictive controller is proposed, and its potential is analyzed using real-world electricity demand profiles. For the analysis, a hybrid power plant with wind, solar, and co-located energy storage capacity of 4 MW each is considered. The analysis shows that the predictive controller can track a real-world-inspired electricity demand profile despite the presence of weather-induced uncertainties and be an intelligent forecaster for HPP performance.

Problem

• Coordinates hybrid power plant components
• Handles weather uncertainties effectively
• Tracks real-world electricity demand profiles

Innovation

• Uncertainty-aware predictive control
• Subspace predictive method
• Hybrid power plant coordination