Concentrating solar power plants, specifically central receiver type systems and their heliostat field, are struggling with negative reputation in the USA, due to perceived underperformance and reliability issues. This is in part due to a lack of standards for performance assessment as well as overly simplified techno-economical models. A better understanding of influences and losses along the solar radiation path from the sun, across the solar collector to the receiver, increases the fidelity of heliostat efficiency assessment as well as solar field performance predictions. Such data are currently scarce and require a complete set of metrology capabilities to evaluate direct solar irradiance, sun shape, atmospheric attenuation, reflectance, collector shape, slope errors and total beam dispersion. In preparation for establishing a 3rd party metrology platform in collaboration with Sandia National Labs, NLR conducted a scoping study on available metrology. We present an extensive overview of techniques and commercial systems for each category. Our work includes an analysis to increase understanding of strengths and limitations of the many techniques used for surface shape and slope measurement. This applies to a controlled, indoor or outdoor laboratory environment assessing a single heliostat.

This study presents a multi-stage, cross-validation comparison of three software packages for Monte Carlo ray tracing (MCRT) applied to central tower concentrating solar power (CSP) systems. The three packages evaluated are: (1) SolTrace, an open-source tool developed by the National Renewable Energy Laboratory (NREL); (2) Solstice, an open-source program created by CNRS-PROMES and Meso-Star, with enhancements for CSP applications (called solsticepy) from the Australian National University; and (3) TieSOL, a commercial software developed by Tietronix. This investigation extends previous ray tracing comparisons by incorporating models of multi-facet heliostats within a commercial-scale solar field, taking into account zoned focal lengths and canting configurations. Receiver flux distributions were compared across the tools using a series of case studies, including single-heliostat scenarios, isolated blocking situations, and comprehensive full-field simulations. The case studies were designed to diagnose differences across the models at varying levels of complexity, and to identify and resolve discrepancies as additional parameters were introduced. Key factors examined in the analysis include sun positions, heliostat location, facet and canting focus, and aimpoint strategies. The comparison aims to improve the accuracy and reliability of these tools while providing benchmark cases for validating future optical modeling tools.

Optics plays a major role in the effectiveness of concentrating solar power (CSP) technologies. The nonintrusive optical (NIO) approach developed by the National Renewable Energy Laboratory uses uncrewed aircraft system (UAS)-based imaging to survey heliostats in a commercial-scale power tower CSP plant and characterize their optical errors. The image processing algorithm uses photogrammetry to calculate the camera position for each image frame, and the accuracy of the estimated optical errors is highly sensitive to the calculated camera position accuracy. In this study, we simulate a series of case studies in PYTHON to examine the impact of different parameters of the sensitivity of the camera calculation, including the number of facet corners used as control points for the photogrammetric calculation, precision error in the detected pixel locations of the facet corners in the image, and precision error of the canting and mounting positions of the facets of the heliostat. The case studies consider heliostat geometry based on three commercial designs to serve as representative examples of different possible sizes of heliostats that the NIO method could be applied to. The results show that increasing the number of control points can improve accuracy for heliostats with many facets, pixel precision has a significantly larger impact on camera calculation accuracy than facet canting and mounting errors, and the camera distance and focal length must be chosen to ensure adequate pixel accuracy on the heliostat surface depending on the size of heliostat. Based on the results, recommendations for the allowable values of each parameter are provided to achieve the required NIO optical error estimation accuracy depending on the size of heliostat.

Concentrating solar power is not a widely deployed or known technology area, and the heliostat workforce community in the United States is currently small, with knowledge and expertise not widely available. The resource, training, and education (RTE) topic within the Heliostat Consortium (HelioCon) was established to address this. RTE encompasses resources, practices, and programs to ensure that (1) newcomers to the heliostat development community have an adequate knowledge base and training to conduct R&D efforts, (2) outsiders to the fi eld are provided with resources and opportunities to join the workforce, and (3) the workforce community is a productive, healthy, and fulfilling environment for all workers. In the fi rst year of the project, a roadmap study was conducted, in which the major gaps in RTE were identified by consulting experts in the industry, with the top gap being the lack of public accessibility to concentrating solar-thermal power (CSP) knowledge. To address this, the HelioCon team has been developing a centralized web-based resource database, containing a reference library, educational videos, lists of components suppliers and software/metrology tools, a power tower plant database, and information on existing standards/guidelines.

NREL is presenting an indoor optical measurement system for large heliostat mirrors. This is a target-based deflectometry system, which uses a thin printed patterned targets and a moving camera to scan large mirrors, measuring the surface slope. This system is compared to existing fringe deflectometry and photogrammetry measurement systems, and the computer vision techniques that make it possible are described. While it cannot achieve the resolution of a fringe deflectometry system, the simplicity and low cost of setup make it well suited for the CSP industry.

Heliostat-based concentrating solar-thermal power (CSP) systems can offer immense potential to provide low-cost, dispatchable renewable thermal and electrical energy to help achieve 100% decarbonized energy infrastructure in the United States. Heliostats are a major determinant of both capital cost and performance of state-of-the-art commercial molten salt towers and Generation 3 CSP systems. In 2021, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) launched the Heliostat Consortium (HelioCon), a five-year initiative to advance heliostat technologies. The HelioCon mission is threefold: (1) establish strategic core testing and modeling capabilities and infrastructure at national labs; (2) support heliostat technology development in relevant industries; and (3) serve as a central repository to integrate industry, academia, and other stakeholders for heliostat technology research, development, validation, and deployment. In this Perspective, HelioCon presents a roadmapping study on advancing heliostat technologies, intended as a central reference for the entire CSP community.

NREL has completed stage 1 development of an indoor optical measurement tool for fully assembled heliostats and single facets. This tool began as an indoor version of NREL’s outdoor Non-Intrusive Optical (NIO) measurement technique [1]. It uses similar techniques to other available tools (deflectometry, photogrammetry, etc.), but is designed to require very little infrastructure, labor, and time to set up and collect surface slope and canting measurements, making it a valuable tool for quality assurance and laboratory measurement of heliostat optics. It accomplishes this by using computer vision, photogrammetry, and multiple images stitched together to minimize the printed target size and required setup precision. This adaptable setup is useful for taking measurements at a variety of heliostat pointing angles, and for measuring fully assembled heliostats on the assembly line. In this paper, we describe the methodology behind the measurement system, present an initial analysis of its uncertainty and sensitivity, and compare it with established optical measurement systems.

The Heliostat Consortium for Concentrating Solar-Thermal Power (HelioCon) began in 2021, funded by the U.S. Department of Energy's Solar Energy Technologies Office to advance U.S. heliostat technologies over the next five years. This report provides detailed information on progress the HelioCon team has made since its founding, including expanding the number of partnerships with industry, research, education, and other institutions; increasing our staff; providing information to a growing audience through our web presence; and participating in national and international conferences with industry leaders.

Heliostat-based concentrating solar-thermal power (CSP) systems can offer immense potential to provide low-cost, dispatchable renewable thermal and electrical energy to help achieve 100% decarbonized energy infrastructure in the United States. Heliostats are a major capital cost technology and a performance-dominating component of state-of-the-art commercial molten salt towers and Generation 3 CSP systems. In 2021, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) launched the Heliostat Consortium (HelioCon), a five-year initiative to advance heliostat technologies. The HelioCon mission is threefold: (1) establish strategic core testing and modeling capabilities and infrastructure at national labs; (2) support heliostat technology development in relevant industries; and (3) serve as a central repository to integrate industry, academia, and other stakeholders for heliostat technology research, development, validation, and deployment. In this report, HelioCon presents a roadmapping study on advancing heliostat technologies, intended as a central reference for the whole CSP community.

A newly developed in situ non-intrusive optical (NIO) approach has been developed to survey various types of heliostat optical errors for a concentrating solar power (CSP) tower plant. To measure mirror surface slope error, facet canting error, and heliostat tracking error at a sub-milliradian accuracy, NIO requires several reflection images scanned over each individual heliostat. For a utility-scale plant that typically includes more than 10,000 heliostats, an unmanned aerial system (UAS) is crucial for efficient implementation of the NIO method. In this paper, we develop a flight path generation/optimization algorithm to plan more efficient UAS paths to collect NIO data over a utility-scale heliostat field. The algorithm considers NIO data requirements, all potential constraints, optimization within each subfield, and operational flexibility. Case studies are presented to illustrate the feasibility and robustness of the developed flight path algorithm. The path planning algorithm may also find applications elsewhere, such as drone-driven imaging under extreme conditions.

This paper focuses on applications of unmanned aerial vehicles (UAVs) for measuring optical error of heliostats in concentrating solar power (CSP) plants. In CSP, there is a need to measure solar-field optical errors, which is critical for future production improvement as well as for operations and maintenance of a heliostat field. This latter need is particularly challenging because of the large number of heliostats (over 10,000 for a utility-scale power plant) that individually track the sun in the field. To address this issue, a camera-equipped UAV, with an optimized drone flight path developed and uploaded to it, collects images of a precise reflection of the tower on each heliostat to evaluate optical error sources without interrupting plant operation. Generation of the drone path for capturing the reflected images is affected by a number technical and realistic constraints, which include the camera angle used to capture the image, the blocking of the camera view due to surrounding heliostats, the location of the camera in reference to the target heliostat, and the target heliostat position with reference to the tower. The effect of these constraints on calculating the camera position will be discussed in detail in this article. An effective drone-path algorithm is generated to fulfil the need of image collection under various constraints.