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Solar field developers include innovative solutions to optimize the energy production of their plants. Simulation tools play a significant role in the design and testing phases as they provide estimations of this yield in different conditions. Transient processes, like passing clouds and solar field start-up, are specifically challenging to optimize and estimate using such simulation tools. Solar fields are subject to high degree of both temporal and spatial variability in the energy input and a detailed estimation can be achieved by simulating subsystems within acceptable time and computational power. Hence, such simulation tools cannot be utilized for tests under realistic operation conditions. The Virtual Solar Field is a computationally efficient simulation tool that allows a detailed transient simulation of parabolic trough solar fields based on single-phase fluids. Using this tool, developers could reproduce a transient test case with exactly the same disturbances to provide fair comparisons between different configurations. In this paper, an evaluation process based on numerical simulations using the Virtual Solar Field is presented. The economic benefit of novel innovative control concepts can be assessed according to the presented scheme. This is demonstrated by evaluating the potential benefit of availability of spatial DNI nowcasts on the control of parabolic trough solar fields. Results show that nowcasting can increase the economic revenue of commercial power plants by up to 2.5% per day. This proves the feasibility of installing such systems.

One of the greatest challenges facing humanity in the 21st century is the transition to a fully decarbonized society. The most abundant energy resource available to mankind is the Sun. Concentrated solar thermal power (CSP) plants with thermal energy storages (TES), such as parabolic trough (PT) power plants, could provide a renewable source of dispatchable energy capable of balancing fluctuations in electrical grids caused by intermittent sources. Yet, whether PT power plants are going to play an important role in the future is mainly a question of cost. One of the ways to increase the competitiveness of PT power plants is the optimization of solar field controller. PT solar fields are complex spatially extended thermo-hydraulic facilities, which concentrate the direct normal irradiance (DNI) on receiver tubes. The operation of these solar fields is strongly affected by spatial and temporal variabilities of DNI, mainly caused by clouds. State of the art PT solar field controllers have only a limited awareness of the current spatial and temporal DNI variability, since these controllers have only access to irradiance measurements of one or a handful of pyrheliometers. All sky imager (ASI) based nowcasting systems can provide spatial DNI information with an adequate temporal and spatial resolution for PT solar field controller. These ASI-based nowcasting systems consist of cameras which take hemispherical images of the sky. The common working principle of ASI-based nowcasting systems includes cloud detection, -geolocation, -tracking and assessment of the corresponding current and immediate future solar irradiance. In the past years a manifold of distinct ASI-based nowcasting systems have been developed. The potential of nowcasting systems for control optimization is often highlighted in the literature. However, to the best of the author’s knowledge, there have been no investigations to date, which have analyzed the applicability of nowcasting systems with their corresponding uncertainties for the optimization of CSP power plants. The first objective of this thesis is the development of a real time capable ASI-based nowcasting system, qualified to describe complex but frequent multi-layer cloud conditions. Therefore, a system is developed which treats each detected cloud as an individual object with attributes such as geolocation, motion vector and transmittance. The processing steps 3-D cloud modeling, -tracking and the determination of the cloud transmittance are developed, benchmarked and combined to a modular nowcasting system, which creates DNI maps for lead times up to 15 minutes ahead. These DNI maps have a spatial extension up to 64 km² with a spatial resolution ≤ 20 m as well as an intra minute temporal resolution. The entire system is validated over two complete years with three spatially distributed reference DNI measurements. Furthermore, the same dataset is used to develop a real-time capable uncertainty analysis with an average coverage factor of 68.3%, taking into consideration spatial variations within the DNI maps. An additional dataset of one year is used to validate the uncertainty analysis. Both, the system validation as well as the uncertainty analysis indicate a strong dependency of the nowcast quality with the prevailing weather conditions. Secondly, this thesis investigates the applicability of DNI maps for the optimization of PT solar field controller, under consideration of the uncertainties. Solar field simulations are performed using the so called Virtual Solar Field (VSF), a detailed dynamic simulation tool. In a first step the DNI maps of the nowcasting system are classified in one of 7 combined spatial and temporal DNI variability classes. For each of the classes optimized control parameters are determined. Class depended control strategies with distinct objectives are benchmarked with a state of the art solar field controller. Results of detailed simulations over 22 days as well as performance estimations over two years indicate an overall significant benefit of roughly 2% in revenue for the novel class depended control strategies.

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020; Aachen 1 Online-Ressource (148 Seiten) : Illustrationen, Diagramme (2020). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020, One of the greatest challenges facing humanity in the 21st century is the transition to a fully decarbonized society. The most abundant energy resource available to mankind is the Sun. Concentrated solar thermal power (CSP) plants with thermal energy storages (TES), such as parabolic trough (PT) power plants, could provide a renewable source of dispatchable energy capable of balancing fluctuations in electrical grids caused by intermittent sources. Yet, whether PT power plants are going to play an important role in the future is mainly a question of cost. One of the ways to increase the competitiveness of PT power plants is the optimization of solar field controller. PT solar fields are complex spatially extended thermo-hydraulic facilities, which concentrate the direct normal irradiance (DNI) on receiver tubes. The operation of these solar fields is strongly affected by spatial and temporal variabilities of DNI, mainly caused by clouds. State of the art PT solar field controllers have only a limited awareness of the current spatial and temporal DNI variability, since these controllers have only access to irradiance measurements of one or a handful of pyrheliometers. All sky imager (ASI) based nowcasting systems can provide spatial DNI information with an adequate temporal and spatial resolution for PT solar field controller. These ASI-based nowcasting systems consist of cameras which take hemispherical images of the sky. The common working principle of ASI-based nowcasting systems includes cloud detection, -geolocation, -tracking and assessment of the corresponding current and immediate future solar irradiance. In the past years a manifold of distinct ASI-based nowcasting systems have been developed. The potential of nowcasting systems for control optimization is often highlighted in the literature. However, to the best of the author’s knowledge, there have been no investigations to date, which have analyzed the applicability of nowcasting systems with their corresponding uncertainties for the optimization of CSP power plants. The first objective of this thesis is the development of a real time capable ASI-based nowcasting system, qualified to describe complex but frequent multi-layer cloud conditions. Therefore, a system is developed which treats each detected cloud as an individual object with attributes such as geolocation, motion vector and transmittance. The processing steps 3-D cloud modeling, -tracking and the determination of the cloud transmittance are developed, benchmarked and combined to a modular nowcasting system, which creates DNI maps for lead times up to 15 minutes ahead. These DNI maps have a spatial extension up to 64 km² with a spatial resolution ≤ 20 m as well as an intra minute temporal resolution. The entire system is validated over two complete years with three spatially distributed reference DNI measurements. Furthermore, the same dataset is used to develop a real-time capable uncertainty analysis with an average coverage factor of 68.3%, taking into consideration spatial variations within the DNI maps. An additional dataset of one year is used to validate the uncertainty analysis. Both, the system validation as well as the uncertainty analysis indicate a strong dependency of the nowcast quality with the prevailing weather conditions. Secondly, this thesis investigates the applicability of DNI maps for the optimization of PT solar field controller, under consideration of the uncertainties. Solar field simulations are performed using the so called Virtual Solar Field (VSF), a detailed dynamic simulation tool. In a first step the DNI maps of the nowcasting system are classified in one of 7 combined spatial and temporal DNI variability classes. For each of the classes optimized control parameters are determined. Class depended control strategies with distinct objectives are benchmarked with a state of the art solar field controller. Results of detailed simulations over 22 days as well as performance estimations over two years indicate an overall significant benefit of roughly 2% in revenue for the novel class depended control strategies., Published by Aachen

The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts.

Kurzzeitprognosen der Solarstrahlung für die nächsten 15 Minuten bergen ein großes Potenzial zur Betriebsoptimierung von solarthermischen Kraftwerken, PV-Systemen als auch des Netzbetriebs. Kameragestützte Prognosesysteme erfuhren in den letzten Jahren einen signifikanten Entwicklungsschub, begünstigt durch die Verfügbarkeit von günstigen hochauflösenden Kameras, der Fortschritte bei den verfügbaren Speicher- und Rechenkapazitäten und dem stetigen Wachstum des solaren Anteils im weltweiten Energiemix. Das Potenzial für räumlich aufgelöste Kurzzeitprognosen der Globalstrahlung wurde u.a. in [1] untersucht. [2] und [3] beschreiben Methoden für Direktstrahlungsprognosen, welche bis zu vier unabhängige 2D Wolkenschichten berücksichtigen. In [4] werden zwei Kameras mit Fischaugenobjektiven für eine detaillierte 3D Wolken-Photogrammmetrie eingesetzt. Im Rahmen dieser Arbeit wurde ein Echtzeit- kurzzeitprognose-System für räumlich hochaufgelöste (5mx5m) DNI Karten basierend auf zwei bis vier Standard Überwachungskameras (Mobotix Q24 oder Q25) entwickelt [5]. Im vorgestellten System wird jede sichtbare Wolke als individuelles 3D Objekt in einem virtuellen Raum modelliert. Jedem Wolkenobjekt im Raum werden die Attribute Höhe, Position, Dicke und Transmissionsgrad zugewiesen. Ein individueller Bewegungsvektor wird für jedes Wolkenobjekt ermittelt. Mittels dieses Systems können Kurzzeitprognosen der Direktstrahlung (DNI) auch bei komplexen mehrschichtigen Wolkenzugsbedingungen erstellt werden.