Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation

Development and validation of coupled thermal-electric transient model of a photovoltaic system

Installed capacity of renewable energy systems keeps growing worldwide in response to climate and socio-economic changes. Among these, photovoltaic power can increase the solar energy contribution in the global mix of energy sources in the near future. Crystalline (mono or poly-crystalline) silicone is the material used in the most widespread photovoltaic modules. Their efficiency depends on several factors and thus modeling the conversion of solar radiation into electricity is crucial and can have various applications, from the study of the different parameters that affect this conversion to the design, maintenance and power output forecasting of photovoltaic systems. To that end, various models have been proposed in the literature, many resorting to empirical data and based on specific modules/systems. In this work, a coupled thermal-electric model was developed for crystalline silicon modules and validated with experimental data from four photovoltaic technologies. The model was developed to be used without resorting to system measurements but only using data provided by the manufacturers. To avoid biases towards a specific technology or location empirical data is not used in its development. The thermal model is based on the energy conservation principle and the heat transfer processes that occur in illuminated photovoltaic modules. The electrical model used is the single diode - five parameters equivalent electrical circuit. The proposed model is transient thus providing the temporal variation of the temperature of the module and electric power output simultaneously at an imposed time step, which is an advantage for modeling at high temporal resolutions with applications in inverter operation and electric grid stability. Moreover, the model outputs can be easily averaged or integrated in order to obtain mean values of system operation. The validation of the model was done with 10 minute data of global tilted irradiance, air temperature, wind speed and direction, temperature of the modules, power output of arrays of four different crystaline silicone technologies with peak power ranging from 1904.8 to 2000 W located in Koriyama, Japan (37.4495; 140.3144). The data used resulted in 24543 data points for each photovoltaic system without shadowing of the photovoltaic cells caused by other rows or snow deposition. The results show a slight overestimation of photovoltaic temperature (up to 2.52°C in mean bias error) and power output (up to 6.44% in relative mean bias error) for all systems. Although in terms of generated power, when comparing the developed model with the single diode - five parameters equivalent electric circuit model but using the measured temperature of the modules, the proposed model showed better estimations for all systems but one

Progresses in the development of an integrated forecasting model of solar radiation and photovoltaic power output without using onsite measurements

Renewable resources, and consequently the generated energy, are especially variable, which makes finding an accurate balance between electricity generation and consumption at any moment challenging in the absence of reliable large capacity energy storage systems. Thus, having an accurate forecasting of the generated energy allows for a more efficient management of the electric grid comprising various energy sources. This work presents the study of all fundamental models necessary for the forecasting of photovoltaic power output when there is no measuring instrumentation on site, namely: weather forecasting model, direct normal irradiance forecast improvement model, transposition model, photovoltaic module temperature and power output model and inverter model. The weather forecasting model used in this work is the numerical weather prediction model of the European Centre for Medium-range Weather Forecasts which produces forecasts twice a day with temporal resolution of 1 hour and 0.125° of horizontal resolution in a global grid. Methods for temporal and spatial downscaling are applied to obtain 10-minute values of the forecasted variables for the desired location. The forecasts of direct normal irradiance (DNI) show higher errors (157.16 W/m2 of mean absolute error - MAE - for forecast day 1) than global horizontal irradiance (GHI, MAE of 63.63 W/m2) and thus a corrective algorithm based on artificial neural networks (ANN) was developed to improve these forecasts achieving an MAE of 130.94 W/m2 for forecast day 1. The transposition model converts DNI and diffuse horizontal irradiance (DIF) into irradiance on the tilted plane (GTI). This is done by using transposition coefficients on the direct, diffuse and reflected component of solar irradiance. In this work some of the most employed analytic models for the determination of the diffuse transposition coefficient are compared, being the modified Bugler model selected. In the case of photovoltaic power plants, which are composed of various rows of panels, there is sometimes obscuring of the sun by the front rows over the second and subsequent rows affecting the beam radiation received by these. There is also obscuring of the sky dome affecting the diffuse radiation and obscuring of the reflected radiation from the ground between rows. In this work a transposition model for rows other than the first based on the works of Varga and Mayer (2021) and Tschopp et al. (2022) was developed and is now being evaluated. The photovoltaic power output is very dependent not only on the irradiance on the solar panels but also on their temperature. Thus, a model to determine the temperature of the panel is essential when there are no measurements available. Most models used in the literature are steady-state and empirical which means they can be biased towards a specific technology or location. Besides comparing the most commonly used empirical models, a physical transient model for the determination of the photovoltaic panel temperature was developed. The integration of these models with various non-empirical power output models was evaluated. Finally, the efficiency of the power inverter is considered to obtain the power output supplied to the electric grid

Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model

To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future

PHOTOVOLTAIC POWER SYSTEMS PROGRAMME Photovoltaic and Solar Forecasting: State of the Art

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