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.
@article{arxiv.2502.13333,
title = {An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants},
author = {Manavendra Desai and Himanshu Sharma and Sayak Mukherjee and Sonja Glavaski},
journal= {arXiv preprint arXiv:2502.13333},
year = {2025}
}