Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants

This paper proposes several data-based models for solar fields (SF) of concentrating solar power (CSP) plants. The proposed models estimate SF thermal output from solar resource data and aim to replace first-principles-based SF models typical in CSP generation schedulers. The advantages of the first models as opposed to the second ones are the ease in their development and their adaptability to changes in the plant, facilitating the forecast of SF thermal output. This information is very useful in order to take advantage of the operational flexibility of CSP plants, which is the main attribute that differentiates them from other renewable sources. The applicability of these models is studied in the optimal generation self-scheduling problem of a CSP plant participating in a day-ahead energy market. A simulation-based economic study has been performed using realistic data, where several scheduling strategies are evaluated in a 50 MW parabolic-trough plant with thermal storage. The scheduling strategies differ only in the model used for the SF. The study shows that the data-based models provide results very similar to those obtained with a first-principles-based SF model. Therefore, they can be used as a viable alternative, while significantly simplifying the design and development of the generation scheduler. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

Several data-based models for solar fields in concentrating solar power plants are proposed in this paper to simplify the design and development of the generation scheduler and improve the accuracy of forecasting SF thermal output. The economic study shows that these models provide results very similar to those obtained with first-principles-based models.