Abstract:
Concentrating Solar Power plants face challenges in achieving and sustaining high performance levels partially due to complexities in plant operations. This study addresses these challenges by developing a computationally efficient, high-fidelity parabolic trough solar field model capable of emulating CSP plant dynamics for use as an operator training simulator and as a tool for optimizing operation strategies. Leveraging a neural network methodology, the model efficiently computes heat absorbed by heat transfer fluid in a solar field with various receiver conditions. The trained neural network model achieves heat absorption error of 0.3% compared to a detailed model while increasing the simulation speed by a factor of 100. The solar field model is validated with data from the operational Solana Solar Generating Station near Gila Bend, AZ (US), and computes temperatures resulting in a mean absolute error of over an entire day including start up and shut down. The model is further validated with respect to net optical efficiency that accounts for time-varying collector defocusing. Lastly, this work concludes with case studies that demonstrate the model’s capabilities both as the engine for a training simulator and as an tool for optimizing solar field control strategies.
Tuman, M. J., & Wagner, M. J. (2025). Neural-network-driven dynamic simulation of parabolic trough solar fields for improved CSP plant operation. Solar Energy, 287, 113203. https://doi.org/10.1016/j.solener.2024.113203
