Browsing by Author "Cardemil Iglesias, José Miguel"

Now showing 1 - 9 of 9
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    Analyzing regional and local changes in irradiance during the 2019 total solar eclipse in Chile, using field observations and analytical modeling
    (MDPI, 2021) Castillejo Cuberos, Armando; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo
    Solar eclipses are astronomic phenomena in which the Earth’s moon transits between the planet and the Sun, projecting a shadow onto the planet’s surface. As solar power installed capacity increases, detailed studies of this region-wide phenomenon’s effect in irradiance is of interest; how-ever, the literature mainly reports its effects on localized scales. A measurement campaign spanning over 1400 km was pursued for the 2 July 2019 total solar eclipse in Chile, to register the event and establish a modeling framework to assess solar eclipse effects in irradiance over wide regional scales. This work describes the event and presents an estimation framework to decompose atmospheric and eclipse effects on irradiance. An analytical model was applied to study irradiance attenuation throughout the Chilean mainland territory, using satellite-derived and astronomical data as inputs compared to ground measurements in eight stations. Results showed good agreement between model and observations, with Mean Bias Errors of −0.008 to 0.98 W/m2 for Global Horizontal Irradiance and −0.004 to −4.664 W/m2 for Direct Normal Irradiance, with Normalized Root Mean Squared Errors of 0.7–5.8% and 1.4–12.2%, respectively. Energy losses due to obscuration corre-sponded between 20–40% for Global Horizontal Irradiance and 25–50% for Direct Normal Irradi-ance over Chilean territory.
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    Assessing system-level synergies between photovoltaic and proton exchange membrane electrolyzers for solar-powered hydrogen production
    (2024) Arias Olivares, Ignacio Javier; G. Battisti, Felipe; Romero Ramos, J. A.; Pérez, Manuel; Valenzuela, Loreto; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo Alfonso
    This study delves into the techno-economic benefits of integrating Proton Exchange Membrane electrolyzers with photovoltaic systems for hydrogen production, with a keen focus on cost optimization strategies. A comprehensive analysis of several system scales and cost scenarios unveils the critical roles of Proton Exchange Membrane stack systems and the Balance of Plant components in influencing capital expenditures. Notably, the research identifies that incorporating the grid via a complementary Power Purchase Agreement, alongside clipped solar energy, innovatively redistributes cost elements. This approach significantly reduces the levelized cost of hydrogen, thereby enabling the feasibility of hydrogen production in regions characterized by low solar radiation at the cost of high grid electricity penetration. Sensitivity to energy costs, accentuated by different integration schemes, highlights the pivotal role of the stack cost and the Balance of Plant cost reductions in achieving economic viability for large-scale deployments. The study underscores the necessity of holistic cost optimization, revealing that strategic grid support coupled with solar energy enhances the techno-economic performance and broadens the scope for renewable hydrogen production in less favorable locales. These insights offer invaluable guidance to stakeholders, advocating for advanced integration strategies that promise both efficiency and financial sustainability in the burgeoning field of renewable hydrogen production systems.
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    Assessing the integration of solar process heat in the dairy industry: A case study in Chile
    (2024) Fuentes, Francisco; Pailahueque, Nicolás; Muñoz, Iván; Escobar Moragas, Rodrigo Alfonso; Cardemil Iglesias, José Miguel
    Solar heat for industrial processes (SHIP) in the dairy industry has attracted considerable interest during the last few years. The present study assesses the use of solar heat in dairy factories applying Ultra High Temperature pasteurization, using three locations in Chilean central and southern regions as case studies. The analysis utilizes numerical simulations considering annual energy performance, economic feasibility, size of thermal energy storage (TES), and the economic competitiveness for four different solar thermal collector technologies: Flat Plate Collector (FPC), Evacuated Tube Collector, Parabolic Trough Collector (PTC), and Linear Fresnel Reflector. The numerical simulations were conducted using the TRNSYS software for varying meteorological parameters and hourly heat load profile. The integration scheme selected for the implementation of the SHIP systems considers supply level to reheat the boiler's feedwater. The results show that the minimum levelized cost of heat ranges between 78 USD/MWh - 79 USD/MWh, with FPC and PTC showing the lower values. For all the companies analyzed, the minimums values of LCoH were observed when considering specific storage volumes between 75 l/m2-120 l/m2, 2 - 120 l/m 2 , indicating a strong relationship between solar resource availability and financial performance.
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    Assessing the performance of novel molten salt mixtures on CSP applications
    (2024) Starke, Allan R.; Cardemil Iglesias, José Miguel; Bonini, Vinicius R.B.; Escobar Moragas, Rodrigo; Castro Quijada, Matías Daniel; Videla Leiva, Álvaro Rodrigo
    The use of molten salt mixtures as a storage medium in Concentrating Solar Power (CSP) plants has been shown to have a significant impact on increasing the reliability of CSP plants and reducing the levelized cost of energy. In this context, the present work presents the implementation of a detailed simulation procedure that contemplates different design considerations for the Rankine cycle to maximize its efficiency according to the temperature constraints established by utilizing different molten salt mixtures. To achieve this, three commercial CSP plant configurations were considered: an indirect coupling using parabolic trough collectors (PTC) with thermal oil as field heat transfer fluid (HTF) and molten salts as the storage medium, a direct coupling of PTC using molten salt as HTF and storage medium, and a central receiver plant (solar tower). The analysis considers the integration strategy between the solar system and the thermal storage, where all of those configurations considered the integration of the two-tank (hot/cold) approach. The analysis enables the development of an accurate estimation of the economic performance of changing the HTF in CSP plants, as well as the assessment of the parasitic consumption due to freezing protection systems, the effect of increasing the current field temperature, and the effect on the plant’s capacity factor. The results show that the improvement in conversion efficiencies associated with salt mixtures operating at higher temperatures induces a higher electricity generation; however, such improvement is not compensated by the material change costs within the specified considerations.
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    Assessment of time resolution impact on the modeling of a hybrid CSP-PV plant
    (2020) Zurita Villamizar, Adriana; Mata Torres, Carlos Enrique; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo
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    Compatibility assessment of thermal energy storage integration into industrial heat supply and recovery systems
    (2024) Wolde Ponce, Ian; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo
    Thermal energy storage (TES) systems can be used for recovering industrial waste heat and increasing energy efficiency, especially when coupled to batch thermal processes. Stratified water thermal storage tanks are the preferred technology for low-temperature applications, while molten salts are commonly used in medium and high-temperature applications with large storage capacities. No clear consensus exists on the appropriate TES technology for different industrial demands characteristics and their respective heat supply systems for medium and high-temperature applications. The present study analyzes several industrial sectors and their thermal processes, analyzing their temperature ranges, heat demands, and available TES technologies, which are classified by their operational conditions. The study presents two novel indicators for a preliminar compatibility assessment between TES and industrial sectors: a temperature compatibility indicator and exergy efficiency for TES and thermal processes. The results show that low and medium-temperature applications such as food, chemical, or textile industries exhibit high compatibilities with water (over 64%), high-temperature PCM (over 61%), and solid-state TES (100%), whereas molten salts and chemical looping demonstrate lower compatibility (below 24%). The exergy analysis for industrial cases shows that a lower temperature operating range for a TES induces low exergy efficiency. Regarding this scenario, high-temperature cPCM reaches efficiencies of over 44% for mid and high-temperature processes. Conversely, solid-state TES emerges as the most viable option for integration in high-temperature industries, exhibiting an efficiency of 62% with minimal exergy losses. The indicators defined in this study can be used for an early evaluation of TES integration in industrial applications, thus promoting emerging technologies selection through a quantitative comparison of the compatibility metrics.
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    Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models
    (2024) Rodríguez, Eduardo; López Droguett, Enrique; Cardemil Iglesias, José Miguel; Starke, Allan R.; Cornejo-Ponce, Lorena
    The evaluation of solar radiation is essential for large-scale solar energy systems, as assessing economic feasibility early on depends on accurate solar radiation data. Accurate sensors are needed to characterize the solar resource. Due to a scarcity of solar radiation data, numerical models are commonly used to estimate solar radiation components using meteorological variables that are simple or cheap to measure. In recent years, the use of machine learning (ML) algorithms has gained significant popularity in the estimation of solar radiation components. In this study it is proposed a post-processing approach using the separation model outcomes as input variables to estimate the diffuse fraction. Three ML models are employed (XGBoost, Random Forest, and Multilayer Perceptron) to boost the accuracy in terms of three statistical indicators: nRMSE, nMBE, and . The employed technique takes a distinctive approach by using reference stations to train the machine learning models and, afterward, make the assessment at the site under study. The results show an improvement in terms of precision of individual separation model outcomes. Thus, the proposed methodology may serve as a reliable approach for estimating solar radiation components in cases where historical data for a specific place of interest is not accessible.
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    Irradiance separation model parameter estimation from historical cloud cover statistical properties
    (2024) Castillejo Cuberos, Armando; Cardemil Iglesias, José Miguel; Boland, J.; Escobar Moragas, Rodrigo Alfonso
    Irradiance separation models allow the decomposition of Global Horizontal Irradiance into Diffuse Horizontal and Direct Normal Irradiances. These models need fitting to the irradiance characteristics of the location of interest using locally measured ground data. For locations that only measure Global Horizontal Irradiance, current state of the art establishes the use of parameters obtained for another location that measures the three components, with similar climate characteristics. Nevertheless, this results in a lack of localized character for estimates and requires fitting model parameters for all possible climates, which can be infeasible given data availability. This work presents a novel approach based on the hypothesis that the separation model's parameters are a function of the statistical properties of satellite-derived cloud cover estimates. The proposed methodology was evaluated in 23 sites covering all main Koppen-Geiger climatic types and different cloud coverage properties using the Boland-Ridley-Lauret diffuse fraction model. The model performs similarly as locally adjusting the model, with Root Mean Square Errors of 0.087-0.15 diffuse fraction units versus 0.077-0.127 for locally optimized parameters, and offers adequate performance across climates and cloud characteristics. These results encourage future research by generalizing parameter estimation for other diffuse fraction models. The main applications for this research are the estimation of irradiance components where no local data is available for model fitting and the enhancement or complementarity of satellite estimates of surface irradiance. Furthermore, it allows the estimation of missing irradiance components due to equipment failure in locations with insufficient data for a representative, locally adapted model.
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    Temporal upscaling of solar radiation components using an analytical model for variability modeling
    (2024) Castillejo Cuberos, Armando; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo Alfonso
    To properly design systems that harness the solar resource, reliable measurements or estimates of its availability are needed. Often, this data is only available at temporal resolutions that are not sufficiently fine to capture the short-term fluctuations of the solar resource, creating uncertainty in the design and performance assessment of solar-powered systems. Synthetic irradiance generation techniques aim to create high temporal resolution estimates starting from lower resolution data, however, a literature review on the topic shows opportunities for improvement regarding the generality of the generation and evaluation approaches. This work proposes novel methods to estimate a quantifiable indicator of short-term variability from hourly data, a new temporal upscaling methodology for time series of arbitrary origin and destination temporal resolution, based on explicit mathematical functions and sufficient generality for application in different contexts, and an improved assessment methodology. The method was applied tor ground and satellite upscaling up to 1 min, resulting in time series with characteristics consistent with those of a higher temporal resolution and exhibit minimum deviations against the original time series characteristics. This method has applications not only for upscaling of low temporal resolution data, but also for gap-filling techniques and the upscaling of hourly solar radiation forecasts.