News

Tuning of Real-Time Optimization of Heliostat Concentrated Solar Power

This study proposes a method to refine the Real-Time Optimization (RTO) algorithm for better stability and quick convergence, especially when the shape of the sunspot is uncertain.

Read the full study here.

(a) UAV Tortoise Identification. (b) Full Size UAV Image. (c) UAV Flyover Preliminary Results. (d) Controlled ground screw performance test diagram example. (e) Soil Texture Pyramid for Relevant Sites. (f) Initial Foundation Cost Model Results.

Featured Project

Impacts of Heliostat Shading on Desert Habitats, and a Proposed Methodology for Coexistent Field Deployment and Operations with Desert Flora and Fauna

Description:
Completion of an on-ground survey of the Bonanza Solar site by the team’s biologist, in addition to a complete UAV flyover study has been completed. A comparative report between the two methods and an overall characterization of the results, with a focus on the desert tortoise, is in progress. During these evaluations it was noted that the area has experienced drought conditions over the past few years which was speculated to be a contributing factor to a qualitatively higher than usual number of tortoise carcasses discovered by the drone flyover. Efforts have been made to expand a network of partnership with the BLM, FWS, USGS, academic research institutions, and private developers to further develop a large scale, long term, ecological study. Initial data acquired from a previous NREL project was analyzed to determine if temperatures inside and outside a deployment of parabolic troughs would be different. Additional data collection opportunities are being explored to further characterize ground conditions inside and outside a solar deployment.

Project Lead:
Jeremy Sment (Sandia) / Aaron Spieles (Sandia)

Status: 

• On-ground (traditional) and UAV flyover surveys focused on the desert tortoise are complete. 
• Full-scale B.A.C.I scope will be informed by the results from completed survey reports and comparison of methods.

Featured Researcher

Dr. Laura Schaefer  (Rice University)

Laura Schaefer is the Burton J. and Ann M. McMurtry Professor in Mechanical Engineering at Rice University. Her research centers on the analysis, design and optimization of energy systems, with an emphasis on improving energy efficiency and diversification for increased sustainability. Her work with concentrated solar power focuses on integrating small-scale heliostat fields in urban environments. Dr. Schaefer is also engaged in education and outreach activities for CSP, including creation of a survey to investigate the underlying causes for lack of higher rates of student participation in the field.

Recent Publications 

• Hassan, M.S.U., Liaqat, K., Schaefer, L., Zolan, A.J., 2024, "Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach," e-Prime - Advances in Electrical Engineering, Electronics and Energy, 10, 100853, https://doi.org/10.1016/j.prime.2024.100853

Past Featured Researchers