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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]

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]