Given the chaotic nature of the weather, solar radiation is highly variable in space and time, and reliable forecast of solar power production is of paramount importance for the large-scale integration of greater shares of solar energy in the power generation and distribution systems. Solar power forecasting contributes to minimize the supply system risks associated to the solar power production fluctuations and to maximize the economic revenues by scheduling the power delivery according to the expected production and the market situation. In this context, nowadays, forecast of solar power production are a basic tool of Transmission System Operators (TSOs) for grid management. It is also fundamental for solar power plants operators for energy trading and plant operation and management. Among the methodologies for predicting surface solar radiation, NWP models stand out as the most powerful tool for forecasting horizons beyond about 5 hours. Their physical foundation allows them to provide comprehensive weather forecasts, maintaining the spatial and temporal coherence over large extended regions and short to medium-range periods. In particular, these models can provide solar irradiance forecast, as well as forecast of ancillary variables of interest for solar energy applications. The
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spatial and temporal scales of the forecasts provided by these models are in the range of 10-25 km and 1-3 hours, in the case of the global circulation models, and only few kilometers and sub-hourly, in the case of the regional models. Within the latter type of NWP models, the WRF model stands out as certainly one of the most advanced. It has an extensive set of parameterizations that provide the model a great flexibility to be adapted for a specific task and to the geophysical characteristics of a particular region. It is also one of the most used models around the world, provided it is a community model being under continuous research and development. Hence, it is appropriate choice for the purposes of this research.
In general, NWP models are not specifically devised for solar energy applications. As a consequence, there are still few research works, such as the one attempted here, assessing the performance of this type of models on the specific task of solar radiation forecasting.
The starting point of this research is addressed in Chapter 3. In this initial study, a comprehensive evaluation of GHI and DNI forecasts reliability provided by the WRF model is conducted. The analysis is carried out by comparing the model forecasts against ground measurements from several radiometric stations located in the region of Andalusia (southern Spain). Time period of analysis is one year. The time resolution is the usual hourly-base, while the spatial resolution is 3 km. This high spatial resolution is a markedly difference compared to the coarser spatial resolutions achieved by the GCM -usually greater than 10 km-. The study includes different aspects affecting solar radiation forecasting (clouds, aerosols, etc.). In addition, no post- processing was applied in order to focus the analysis only on the model performance. In particular, the skill of the prediction was evaluated for different sky conditions, namely: complete overcast, cloudy, clear sky and all-sky. Seasonal and year-around independent analyses were conducted. Additionally, model skill for different forecasting horizons (24h, 48h and 72h) was assessed. Furthermore, unlike GHI, which is directly provided by the model, DNI was derived from the model outputs as it was not already an output variable included in the WRF
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version 3.2.1, used in this study. The current versions of WRF already provide DNI and diffuse components. However, this was the first study that analyzed DNI forecasts derived from WRF in the context of solar energy applications. Nonetheless, today it is still not properly studied the relative reliability of DNI forecasts of the NWP models vis-à-vis DNI forecasts derived by means of an external model, such as all-sky separation models or radiative transfer models as the one used here. From a practical stand point this could be important, both for using the most reliable forecasts, as well as for having a valuable reference for DNI forecast assessment. Notwithstanding, the goal should be to enhance the NWP model performance.
As a result of the evaluation, it was found that, in general, the WRF model tends to overestimate solar irradiance, under all sky conditions and for all analyzed periods. For cloudy conditions, it was found that this was mainly due to the fact that the WRF model underpredicts the cloud amount. In the case of clear sky conditions, this result suggests that the AOD values used in the model underestimate the actual values. As it was expected, errors for DNI were found to be markedly higher than those obtained for GHI. This is due to the greater sensibility of DNI, overall to the presence of clouds, but also to the uncertainty of the aerosol load. Results also showed that the model performed generally better than the trivial persistence model, except in periods where the presence of clouds was more significant, when performance was similar. Thus, summer presents better results, showing that the presence of clouds is the most important factor in the estimation of the solar irradiance by far. In summary, it was concluded that cloud forecast is still a big issue regarding solar radiation forecasting. Therefore, substantial improvements about cloud representation in NWP models should be obtained in the future. This will help to obtain the stringent requirement of the solar energy industry.
The next step in the solar radiation forecasting analysis was the intercomparison of the approach proposed here, based on the WRF NWP model, against other approaches based on different regional and global NWP models. This analysis was conducted in an extensive
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benchmarking exercise described in Chapter 4. This work established a reference framework that allowed evaluating the WRF solar radiation forecasts respect to the forecasts of other NWP models. The study was carried out only for GHI mainly because of the lack of DNI observations. It should be noted that, unlike the approach followed here, some models participating in this benchmarking exercise used post-processing procedure to enhance their performances. In this sense, this benchmarking study is also interesting in order to know the possibilities of improving the WRF model estimates. Indeed, results show that the post-processing has a great potential to enhance forecasts with large systematic deviations, as it is the case of WRF according to previous results obtained in Chapter 3. In general, it was concluded that the performance of GCMs was better than those of the regional models. Overall, the WRF model errors were significantly larger than the errors of the ECMWF model, which obtained the best results. It was concluded that one of the reasons was the low bias errors provided by the GCMs; unlike WRF, which showed a significant overestimation. Another interesting result of the benchmarking study suggested that the performance differences between the regional models and the GCMs has more to do with the regional models themselves than with the initial and boundary conditions used -provided by GFS model in this study-. In this regard, additional detailed studies are required to confirm this assertion. However, contrary to expected, it was found that the horizontal spatial resolution did not play an important role in the GHI forecasting reliability. This issue motivated a further research, which was presented in Chapter 5.
Following results obtained in Chapter 4, the next step in the research plan was to analyze the role of the horizontal spatial resolution. This is an important aspect concerning solar resource forecasting from the stand point of model-output’s optimization. Additionally, the effect of the spatial average of solar radiation in the reliability of the WRF solar irradiance forecast was also assessed. This simple post-processing method is extensively used, because it usually reduces the absolute error, which is directly related to the deviations of the expected power production. Both issues were analyzed in Chapter 5. This study was
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conducted for both components: GHI and DNI, although only results for DNI were shown. However, the main conclusions are valid for both variables.
On the one hand, the role of horizontal spatial resolution was examined in order to clarify its effect on the reliability of the solar irradiance forecast. Contrary to what would be expected, the results showed that an increment in spatial resolution does not necessarily enhance the reliability of the WRF-based DNI forecasts. Only under clear sky conditions the performance is better in the case of higher spatial resolutions, when the topographic features play a major role. This suggests an important role of clouds in this trend. On the other hand, in the second part of Chapter 5, an ad-hoc post-processing method is applied to the WRF raw outputs. It consisted of a smoothing filter based on spatial average –or spatial aggregation- of solar irradiance. Results clearly showed that the spatial averaging of solar irradiance notably reduces the forecasting errors, improving the WRF performance. Moreover, the results were more reliable for coarser resolutions and for a spatial aggregation covering around 100×100km. Similar results were obtained in other studies using other NWP models.
From the results of chapters 4 and 5 it was clearly concluded that a higher spatial resolution does not guarantee a better NWP model performance regarding solar irradiance forecasting. This does not mean that the representation of cloudiness is better at coarser model spatial resolutions. There are other aspects that have to be taken into account, such as the model representation of clouds itself, or even the evaluation process itself, since the modelled solar irradiance values represent the average over the entire grid cell extension, which is then compared to point-wise observations. Another important factor to be considered is the so-called double-penalty error, which accounts for the fact that clouds have to be correctly represented both in space and time in order to not produce penalties in the error scoring. That means that clouds should be in the exact position in the precise moment with respect to the observations gathered in the radiometric station. This is related with the effectiveness of spatial averaging at reducing the random
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component of error -i.e. MAE and RMSE- since spatial averaging reduces the impact of double-penalty errors. This is because, somehow, it is as if increasing the size of the clouds prevents many errors in which the cloud is generated close to the validation point but not at the exact position. Alternatively, in general, the goodness of spatial averaging at reducing random errors is closely related with the spatial autocorrelation of solar radiation in the neighborhood of the evaluation site in such a way that the lower spatial autocorrelation, the smaller averaging size is required to achieve the same reduction of random errors. Additionally, there exists an averaging size for which this reduction is optimal (i.e., the maximum possible) (in our experiments is about 100x100 km) and it also depends on the spatial autocorrelation structure. Therefore, the optimal distance varies with the synoptic weather conditions and topographic configuration of the validation site -among other things- which are the factors determining the spatial autocorrelation structure of solar radiation. Finally, it is worth to mention that, although the spatial averaging of solar radiation may reduce the random errors of the model estimates, it may also distort the probability distribution of solar radiation as simulated by the model. This may be a limiting factor of this post-processing approach for those applications in which a good representation of the long-term data distribution is critical. Therefore, it can be concluded that the convenience of achieving high spatial resolutions –with the huge computational effort required- and/or using a spatial average post- processing depends on the final application. For instance, based on these results and those of Chapter 4, it can be concluded that the use of the WRF model is recommended for plant operation, instead of using GCMs. For these applications, higher temporal resolutions are more suitable. In addition, data variability is important for the modeling of power production. However, for energy trading, where the time resolution is typically 1 hour, it seems to be more convenient to use a GCM, as the IFS model of the ECMWF. This is because deviations – and consequent penalties- are linked to the forecast errors, particularly the MAE. Nonetheless, the potential of WRF model is much greater than any of the GCMs in the sense of having much more flexibility. In this regard, it should be mentioned the promising results recently
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obtained by some authors with the implementation of a special version of WRF devoted to solar energy applications.
In summary, the aimed objectives for this part of the thesis work were attained. It was obtained that the WRF model is able to provide fair solar radiation forecasts that can be used in the solar energy industry. Nevertheless, it was also concluded that the model has a problem of misrepresentation of cloud fraction and/or cloud amount. In practice, this can be partially emended by the application of post-processing methods to the raw model outputs, which overall reduce or eliminate the systematic error. However, from the stand point of the scientific research, the next step should be the enhancement of the capabilities of WRF to improve the representation of clouds and aerosols. Ultimately, an important effort has to be done in order to improve the immense potential of this extraordinary tool that is the WRF model.