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Concentrated Solar Power (CSP) and geothermal energy systems are outlined as two of the most promising technologies for sustainable and reliable electricity generation. Several studies in the technical literature have pointed out that the hybridization of solar and geothermal energy sources could lead to a reduction of the levelized cost of energy (LCOE) of geothermal systems, as well as improving the capacity factor of CSP systems. However, the technical literature shows that the integration of solar thermal collectors does not present a positive impact in all scenarios analyzed. The present study aims to further analyze the competitiveness of the hybridization of solar and geothermal systems under high irradiation conditions such as those observed in the Andean region in northern Chile. The evaluation was carried out by coupling a thermodynamic model in Engineering Equation Solver (EES) with a solar thermal model in the System Advisor Model (SAM). The assessment considers the configuration of an existing geothermal plant, considering the design constraints associated with the actual operating conditions of the plant. The analysis is based on an energy and exergy assessment, allowing us to identify the efficiency of the subsystems introduced for the hybridization and assess the competitiveness of the hybrid schemes by an economic assessment in terms of the LCOE. The results show that the hybrid schemes allow a reduction of the LCOE of a geothermal stand-alone plant by about 10 USD / MWh , increasing the competitiveness of the geothermal system. However, a large variation on such a reduction is observed depending on the size of the solar field and the storage tank of the solar system. [ABSTRACT FROM AUTHOR]

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. • Oversized PV fields for clipping enhance hydrogen production versatility over PPA-powered PEM schemes. • Electricity and stack cost cuts key to LCOH 2 reduction in all scenarios. • LCOH 2 as low as 1.62 USD/kgH 2 achieved with clipped energy use. • Grid and clipped energy integration hit LCOH 2 lows of 1.57 USD/kgH 2 , aiding areas with low solar radiation. [ABSTRACT FROM AUTHOR]

Abstract: Overheating, high cooling energy demand and glare are recurrent problems in office buildings in Santiago. It is necessary to know the influence of different architectural strategies in energy demands to meet thermal comfort of the users and energy efficiency. Based on dynamic simulations, cooling and heating demand are estimated for an office building located in Santiago. It is shown that the size of envelope''s glazed areas highly influences the energy demand. A totally glazed façade building might reach up to 155kWh/m2 year for total cooling and heating demands. On the other hand, in a building with a window-to-wall ratio (WWR) of 20%, external solar protection and selective glazing, demand might be as low as 25kWh/m2 year. If night ventilation is applied during cooling periods an additional reduction of 37% can be achieved. A WWR of 20% is enough to keep a useful daylight around 80% of the time throughout the year. Main conclusions are: (i) for the climate conditions of Santiago, completely glazed façades are not recommended, even with selective glazing; (ii) night ventilation shows to be highly effective to reduce cooling demand; and (iii) lower WWRs with solar protection can achieve a better daylight performance than larger WWRs due to prevention of glare. [Copyright &y& Elsevier]

Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters' performance. [ABSTRACT FROM AUTHOR]

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; however, 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 corresponded between 20–40% for Global Horizontal Irradiance and 25–50% for Direct Normal Irradiance over Chilean territory. [ABSTRACT FROM AUTHOR]

The water–energy–food nexus has captured the attention of many researchers and policy makers for the potential synergies between those sectors, including the development of self-sustainable solutions for agriculture systems. This paper poses a novel design approach aimed at balancing the trade-off between the computational burden and accuracy of the results. The method is based on the combination of static energy hub models of the system components and rule-based control to simulate the operational costs over a one-year period as well as a global optimization algorithm that provides, from those results, a design that maximizes the solar energy contribution. The presented real-world case study is based on an isolated greenhouse, whose water needs are met due to a desalination facility, both acting as heat consumers, as well as a solar thermal field and a biomass boiler that cover the demand. Considering the Almerian climate and 1 ha of tomato crops with two growing seasons, the optimal design parameters were determined to be (with a solar fraction of 16% and a biomass fraction of 84%): 266 m 2 for the incident area of the solar field, 425 kWh for the thermal storage system, and 4234 kW for the biomass-generated power. The Levelized Cost of Heat (LCOH) values obtained for the solar field and biomass boiler were 0.035 and 0.078 €/kWh, respectively, and the discounted payback period also confirmed the profitability of the plant for fuel prices over 0.05 €/kWh. Thus, the proposed algorithm is useful as an innovative decision-making tool for farmers, for whom the burden of transitioning to sustainable farming systems might increase in the near future. [ABSTRACT FROM AUTHOR]

A simplified mathematical model of parabolic-trough solar thermal power plants, which allow one to carry out an energetic characterization of the main thermal parameters that influence the solar field performance, was evaluated through a comparison of simulation results. Two geographical locations were selected to evaluate the mathematical model proposed in this work—one in each hemisphere—and design considerations according with the practical/operational experience were taken. Furthermore, independent simulations were performed using the System Advisor Model (SAM) software, their results were compared with those obtained by the simplified model. According with the above, the mathematical model allows one to carry out simulations with a high degree of flexibility and adaptability, in which the equations that allow the plant to be energetically characterized are composed of a series of logical conditions that help identify boundary conditions between dawn and sunset, direct normal irradiance transients, and when the thermal energy storage system must compensate the solar field energy deficits to maintain the full load operation of the plant. Due to the above, the developed model allows one to obtain satisfactory simulation results; referring to the net electric power production, this model provides results in both hemispheres with a relative percentage error in the range of [0.28–8.38%] compared with the results obtained with the SAM, with mean square values of 4.57% and 4.21% for sites 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

Multi-junction solar cells in concentrator photovoltaic systems (CPV) offer the potential for highly efficient, low-cost electricity generation. However, these systems are typically designed and rated against reference spectra or for standard atmospheric conditions which are often not representative of the real deployment environments. Indeed, it has been shown that the use of unrealistic spectra can impact annual energy yield estimations by up to 75%. Hence there is a need to routinely generate spectrally resolved direct normal irradiance (DNI) estimates that are based on real observations so that CPV systems can be optimised appropriately for different locations.Here we will first present an automated scheme to estimate spectrally resolved DNI based on the integrated use of a radiative transfer model, AERONET ground-based observations and analyses from the European Centre for Medium Range Weather Forecasting Copernicus Atmospheric Monitoring Service (ECMWF CAMS). Our approach takes particular care to account for aerosol effects, circumsolar irradiance and other relevant atmospheric parameters. The results are tested against ground-based observations from Santiago, Chile, and has shown that broadband DNI can be simulated with an average bias of less than 2%. We will then present our preliminary work on integrating the above spectrally resolved DNI estimation scheme with satellite retrieved aerosol optical depths from the Oxford-RAL Aerosol and Cloud (ORAC) retrieval scheme using (A)ATSR / SLSTR observations, and Solcore, a semiconductor solver for CPV power output estimations. This will enable a global, long-term assessment of CPV solar energy generation, and even the possibility of near-real-time power estimates. [ABSTRACT FROM AUTHOR]