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

TL;DR

This paper introduces an uncertainty-aware data-driven predictive controller for hybrid power plants that effectively manages weather uncertainties to meet electricity demand, demonstrated on a real-world energy system.

Contribution

It proposes a novel uncertainty-aware subspace predictive control method tailored for hybrid power plants, enhancing demand tracking under weather variability.

Findings

Successfully tracks real-world demand profiles despite weather uncertainties

Demonstrates effective coordination of wind, solar, and storage components

Shows potential as an intelligent forecasting tool for hybrid power plants

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.