Rozprawy doktorskie na temat „SOLAR ENERGY FORECASTING”

The radiative transfer cannot be explicitly resolved in the atmospheric models for two reasons: i) a full treatment of the radiative transfer equation (RTE) requires a high amount of computational resources and ii) the radiative transfer fields such as the optical thickness are not a direct solution of the Euler equations and hence, they must be parameterized as a function of the meteorological fields. Consequently, the physical processes related with radiation are simplified and approximated in physical schemes. In the particular case of the solar radiation, the use of these parameterizations were reduced for many years to represent the day/night cycle inside the model. Therefore, the accuracy of the solar schemes was left in the background and the computational resources were prioritized. With the growth of the solar energy industry during the last decade, a paradigm shift has occurred. Now, the solar irradiance (i.e. global horizontal GHI, direct horizontal DHI and diffuse DIF) becomes an important product for resource assessment as well as for forecasting applications. The main objective of this thesis is the identification and quantification of the sources of error that have a direct or an indirect contribution to the accuracy of the solar schemes, particularly, in those available in the Weather Research and Forecasting (WRF-ARW) model, widely used in the sector. First, the thesis presents a review of the set of physical approximations considered in six solar parameterizations available in the WRF-ARW model: Dudhia, Goddard, New Goddard, Rapid Radiative Transfer Model for General Circulation Models (RRTMG), Climate Atmospheric Model (CAM) and Fu-Liou-Gu (FLG). The sources of error are limitations in the representation of the radiative transfer as a conse- quence of the set of approximations assumed by one scheme. In this thesis three sources of error are analyzed: i) errors due to the vertical discretization of the atmosphere in a set of layers that are assumed to be homogeneous (truncation error), ii) errors due to the misrepresentation of the layer between the top of the model (TOM) and the top of the atmosphere (TOA), called TOM error and iii) errors due to the physical simplifications and parameterizations in the RTE, named physical error. In order to avoid the uncertainty introduced by the other components of the model, the source code of each one of the six solar schemes has been separated of the model and adapted for working with 1-dimensional vertical profiles. The studies of the truncation and TOM errors are performed by using ideal vertical profiles under four scenarios: a dry atmosphere, a wet cloudless sky, low water cloud and a high ice cloud. The results for the ETOM show that for the typical range of TOM values in mesoscale appli- cations (i.e. 10 hPa), the error with respect to a full atmospheric column is less than 0.5% and hence, the TOM error can be neglected. The analysis of the Etrun reveals that the sensitivity of the solar schemes on the vertical config- uration (i.e. number of vertical levels and their distribution) is directly related with the method used for the vertical integration of the multiscattering processes. For the typical mesoscale config- urations, the Etrun under clear-sky conditions is determined around 1.1%, 0.9% and 4.9% for the GHI, DHI and DIF, respectively. In both cloudy scenarios, the Etrun increases significantly, being more important for the high clouds. The Ephys is analyzed under clear-sky conditions using real soundings from the Integrated Global Radiosonde Archive data-set and comparing the irradiance outcomes with the Baseline Solar Radiation Network measurements. With the exception of Dudhia, the behavior for all the parameterizations is the same. A large overestimation of the DHI with a large underestimation of the DIF that leads to a near-zero bias for the GHI. Polar sites show the lowest errors with a mean MAE of 2.1%, 5.2% and 3.7% for GHI, DHI and DIF, respectively. Midlatitude sites show the worst results with a mean MAE of 3.4% in GHI, 11.6% in DHI and 7.8% in the DIF.
L’objectiu principal d’aquesta tesi ´es la identificaci´o i quantificaci´o de les fonts d’error que tenen una contribuci´o directa o indirecta en la precisi´o dels esquemes solars, particularment en aquells disponibles en el model Weather Research and Forecasting (WRF-ARW), `ampliament emprat en el sector de l’energia solar. Les fonts d’error s´on limitacions en la representaci´o del transport radiatiu com a consequ¨`encia del conjunt d’aproximacions assumides per cada esquema. En aquesta tesi hi ha tres fonts d’error que s´on analitzades: i) l’error degut a la discretitzaci´o vertical de l’atmosfera en un conjunt d’estrats que s’assumeixen homogenis (error de truncament, Etrun), ii) l’error com a resultat d’una repre- sentaci´o insuficient de l’estrat entre el cim del model (TOM) i el cim de l’atmosfera (TOA), anomenat error de TOM Etom, i iii) l’error degut a les simplificacions i a les parametritzacions f´ısiques de l’RTE, definit com a error físic, Ephys. Per tal d’evitar la incertesa introdu¨ıda pels altres components del model, el codi font de cadas- cun dels sis esquemes solars ha estat separat del model i adaptat per treballar amb perfils verticals 1-dimensionals. Mitjan¸cant aquest m`etode, les habilitats dels esquemes solars poden ´esser anal- itzades sota condicions d’entrada id`entiques. D’una banda l’error de TOM i el de truncament s’analitzen a partir de perfils ideals. De l’altra, l’error f´ısic s’evalua prenent dades de radiosondatge com a perfil vertical i comparant les sortides dels esquemes radiatius amb mesures en superf´ıcie. Els resultats d’aquesta tesi mostren que l’Etom esdev´e negligible per la majoria d’aplicacions de mesoscala. Per configuracions t´ıpiques del model, l’Etrun en condicions de cel ser`e es troba al voltant de l’1.1%, el 0.9% i el 4.9% per la GHI, DHI i DIF, respectivament. En el cas amb nu´vols augmenta de forma significativa. L’estudi de l’Ephys mostra una relaci´o significativa amb el contingut de vapor d’aigua i els aerosols.

The optimization of photovoltaic (PV) power generation system requires an accurate system performance model capable of validating the PV system optimization design. Currently, many commercial PV system modeling programs are available, but those programs are not able to model PV systems on a distorted ground level. Furthermore, they were not designed to optimize PV systems that are already installed. To solve these types of problems, this thesis proposes an optimization method using model simulations and a MATLAB-based PV system performance model. The optimization method is particularly designed to address partial shading issues often encountered in PV system installed on distorted ground. The MATLAB-based model was validated using the data collected from the Cal Poly Gold Tree Solar Field. It was able to predict the system performance with 96.4 to 99.6 percent accuracy. The optimization method utilizes the backtracking algorithm already installed in the system and the pitch distance to control the angle of the tracker and reduces solar panels partial shading on the adjacent row to improve system output. With pitch distances reduced in the backtracking algorithm between 2.5 meters and 3 meters, the inverter with inter-row shading can expect a 10.4 percent to 28.9 percent increase in power production. The implementation and calibration of this optimization method in the field this spring was delayed due to COVID-19. The field implementation is now expected to start this summer.

The electrical grid can no longer be considered a unidirectional means of distributing energy from conventional plants to the final users, but a Smart Grid, where strong interaction between producers and users takes place. In this context, the importance of independent renewable generation is constantly increasing, and new tools are needed in order to reliably manage conventional power plant operation, grid balancing, real-time unit dispatching, demand constraints and energy market requirements. This dissertation is focused on two aspects of this general problem: cost-optimal management of smart buildings in a Demand-Response framework, and estimation of photovoltaic generation forecasting models. In the first part of this thesis a novel Model Predictive Control approach for integrated management of HVAC, electrical and thermal storage, and photovoltaic generation in building is presented. The proposed methodology also considers participation of the building in a Demand-Response program, which allows the consumer to become an active player in the electricity system. The related optimization problems turn out to be computationally appealing, even uncertainty sources is also addressed by means of a two-step procedure. The second part deals with the problem of estimating photovoltaic generation forecasting models in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. This scenario is relevant to electricity network operation, when a large number of photovoltaic plants are deployed in the grid. In particular, two methods have been developed. The first approach makes use of raw cloud cover data provided by a weather service combined with power generation measurements to estimate the parameters of a novel class of models. The second approach is based on a set of tests performed on the generated power time series aimed at detecting data portions that were generated under clear sky conditions. These data are then used for fit the parameters of the PVUSA model to the theoretical clear sky irradiance. All the methods covered in this thesis have been extensively validated either using industry-standard simulation frameworks or via experiments performed on real data.

As the ‘green’ energy movement continues to gain momentum, photovoltaic generation is becoming an increasingly popular source for new power generation. The primary focus of this paper is to demonstrate the benefits of close-to real-time cloud sensing for Photovoltaic generation. In order to benefit from this close-to real-time data, a source of cloud cover information is necessary. This paper looks into the potential of point insolation sensors to determine overhead cloud coverage. A look into design considerations and economic challenges of implementing such a monitoring system is included. The benefits of cloud location sensing are examined using computer simulations to target important time-scales and options available to plant operators. Finally, the economics of advanced forecasting options will be examined in order to determine the benefit to plant operators.

The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.

This thesis consists of the study of different Machine Learning models used to predict solar power data in photovoltaic plants. The process of implement a model of Machine Learning will be reviewed step by step: to collect the data, to pre-process the data in order to make it able to use as input for the model, to divide the data into training data and testing data, to train the Machine Learning algorithm with the training data, to evaluate the algorithm with the testing data, and to make the necessary changes to achieve the best results. The thesis will start with a brief introduction to solar energy in one part, and an introduction to Machine Learning in another part. The theory of different models and algorithms of supervised learning will be reviewed, such as Decision Trees, Naïve Bayer Classification, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Linear Regression, Logistic Regression, Artificial Neural Network (ANN). Then, the methods Linear Regression, SVM Regression and Artificial Neural Network will be implemented using MATLAB in order to predict solar energy from historical data of photovoltaic plants. The data used to train and test the models is extracted from the National Renewable Energy Laboratory (NREL), that provides a dataset called “Solar Power Data for Integration Studies” intended for use by Project developers and university researchers. The dataset consist of 1 year of hourly power data for approximately 6000 simulated PV plants throughout the United States. Finally, once the different models have been implemented, the results show that the technique which provide the best results is Linear Regression.

Buildings are the most prominent component in the urban environment. The geometric identification of urban buildings plays an important role in a range of urban applications, including 3D representations of buildings, energy consumption analysis, sustainable development, urban planning, risk assessment, and change detection. In particular, 3D building models can provide a comprehensive assessment of surfaces exposed to solar radiation. However, the identification of the available surfaces on urban structures and the actual locations which receive a sufficient amount of sunlight to increase installed power capacity (e.g. Photovoltaic systems) are crucial considerations for solar energy supply efficiency. Although considerable research has been devoted to detecting the rooftops of buildings, less attention has been paid to creating and completing 3D models of urban buildings. Therefore, there is a need to increase our understanding of the solar energy potential of the surfaces of building envelopes so we can formulate future adaptive energy policies for improving the sustainability of cities. The goal of this thesis was to develop a new approach to automatically model existing buildings for the exploitation of solar energy potential within an urban environment. By investigating building footprints and heights based on shadow information derived from satellite images, 3D city models were generated. Footprints were detected using a two level segmentation process: (1) the iterative graph cuts approach for determining building regions and (2) the active contour method and the adjusted-geometry parameters method for modifying the edges and shapes of the extracted building footprints. Building heights were estimated based on the simulation of artificial shadow regions using identified building footprints and solar information in the image metadata at pre-defined height increments. The difference between the actual and simulated shadow regions at every height increment was computed using the Jaccard similarity coefficient. The 3D models at the first level of detail were then obtained by extruding the building footprints based on their heights by creating image voxels and using the marching cube approach. In conclusion, 3D models of buildings can be generated solely from 2D data of the buildings’attributes in any selected urban area. The approach outperforms the past attempts, and mean error is reduced by at least 21%. Qualitative evaluations of the study illustrate that it is possible to achieve 3D building models based on satellite images with a mean error of less than 5 m. This comprehensive study allows for 3D city models to be generated in the absence of elevation attributes and additional data. Experiments revealed that this novel, automated method can be useful in a number of spatial analyses and urban sustainability applications.

The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step persistence and the Gaussian process (GP). Root mean squared error (RMSE) is used as the measurement of evaluation and thus determining the accuracy of the models. Results show that the ARIMAX models performed most accurate in every simulation of the single step models implementation, which implies that adding the exogenous predictor wind speed increases the accuracy. However, the accuracy only increased by 0.04% at most, which is determined as a minimal amount. Moreover, the results show that the GP model was 3% more accurate than the multiple step persistence; however, the GP model could be further developed by adding more training data or exogenous variables to the model.

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CAPES e FAPESB.
As a result of global climate change, during the coming decades less rainfall and higher temperatures are projected for the Brazilian Northeast (NE). Consequently these regional climatic changes could severely impact hydroelectric generation in the NE as well as influence solar and wind power potential. The ongoing drought in the Brazilian NE region has caused hydroelectric generation to decline substantially during the last 5 years and in 2016 hydroelectricity only supplied 25% of the NE’s total demand. In contrast, wind power supplied 30% of demand and is expected to generate 55-60% of the NE’s electricity supply by 2020. Therefore, this paper is focused on both short term forecasting and long-term projections of renewable energy generation and resource availability. It also explores the economic, environmental and technical feasibility of renewable energy integration in the NE region of Brazil. First, the long-term impacts of climate change on the NE region’s hydroelectric and wind energy production are analysed. Particular attention is paid to the long-term projections of annual rainfall and streamflow in the São Francisco basin which could decline by approximately 47% and 80%, respectively, by 2050. On the other hand, wind energy potential is projected to increase substantially during the same period. This thesis also estimates the economic, social, and environmental viability of renewable and non-renewable generation technologies in Brazil. The Levelised Cost of Electricity (LCOE) including externalities is calculated for several different case study power plants, the majority of which are located in the Brazilian NE. It was found that wind power becomes the cheapest generation technology in the NE region, once all externality and transmission line costs are taken into consideration. The LCOE for the entire Northeast’s generation matrix is calculated for various configurations, including scenarios in which hydroelectric generation is restricted due to drought conditions. It was concluded that a generation mix in which wind power replaces all fossil fuel generation by 2020, could feasibly reduce the overall LCOE in the region by approximately 46% and substantially decrease CO2eq emissions. Two different methods are used to examine the limits of integrating high penetrations of variable renewable generation technologies into a power system with a large proportion of hydroelectric capacity. In the first method existing wind generation data from 16 wind farms is extrapolated in time and space, while the second method uses a numerical weather prediction model to simulate future wind energy generation in the NE region. Considering the minimum generation requirements of the São Francisco’s hydroelectric dams, the maximum wind energy penetration in the NE region is estimated to be approximately 50% before significant amounts of energy would need to be curtailed or exported to other Brazilian regions. Finally, this thesis reviews additional literature on energy storage and the impact of large scale variable renewable energy integration on grid stability and power quality. It was found that there are several existing technologies such as power factor and voltage regulation devices that can resolve these issues.
Como consequência da mudança climática global, nas próximas décadas menos precipitação e temperaturas mais altas são projetados para Nordeste (NE) do Brasil. Consequentemente, essas mudanças climáticas regionais podem afetar severamente a geração hidrelétrica no NE, bem como influenciar o potencial de energia solar e eólica. A seca atual nessa região do Brasil fez com que a geração hidrelétrica caísse substancialmente durante os últimos 5 anos e em 2016, as usinas hidrelétricas apenas forneceram 25% da demanda total do NE. Em contraste, a energia eólica forneceu 30% da demanda e deverá gerar 55-60% do fornecimento de energia elétrica do NE até 2020. Portanto, este trabalho está focado tanto na previsão a curto quanto projeções a longo prazo da geração de energia renovável e disponibilidade de recursos. Ele também explora a viabilidade econômica, ambiental e técnica da integração de energias renováveis na região NE. Primeiramente, os impactos de longo prazo das mudanças climáticas na produção hidrelétrica e eólica da região NE são analisados. Especial atenção é dada às projeções de longo prazo de precipitação anual e fluxo na bacia do São Francisco, que podem diminuir em aproximadamente 47% e 80%, respectivamente, até 2050. Por outro lado, prevê-se que o potencial da energia eólica aumente substancialmente durante o mesmo período. Esta tese também estima a viabilidade econômica, social e ambiental das tecnologias de geração renováveis e não-renováveis no Brasil. O custo nivelado de energia elétrica (LCOE), incluindo externalidades, é calculado para diversas usinas de estudo de caso, a maioria localizada no NE. Verificou-se que, a energia eólica se torna a tecnologia de geração mais barata na região NE, uma vez que todos os custos de externalidades e de linhas de transmissão são levados em consideração. O LCOE para a matriz de geração do Nordeste é calculado para várias configurações, incluindo cenários em que a geração hidrelétrica é restrita devido às condições de seca. Concluiu-se que, uma mistura de geração em que a energia eólica substitui toda a geração de combustíveis fósseis até 2020, poderia reduzir o LCOE na região em aproximadamente 46% e diminuir substancialmente as emissões de CO2eq. Dois métodos diferentes são usados para examinar os limites da integração de altas penetrações de tecnologias de geração renovável variáveis em um sistema de energia com uma grande proporção de capacidade hidrelétrica. No primeiro método, dados de geração eólica existentes de 16 parques eólicos são extrapolados no tempo e no espaço, enquanto o segundo método utiliza um modelo de previsão numérica de tempo para simular a futura geração de energia eólica na região NE. Considerando as exigências mínimas de geração das hidrelétricas do São Francisco, estima-se que a penetração máxima de energia eólica na região NE seja de aproximadamente 50% antes que quantidades significativas de energia precisem ser desperdiçadas ou exportadas para outras regiões brasileiras. Finalmente, esta tese examina literatura adicional sobre armazenamento de energia e o impacto da integração de energia renovável variável em larga escala na estabilidade da rede elétrica e na qualidade da energia. Verificou-se que existem várias tecnologias existentes, como dispositivos de regulação de fator de potência e tensão que podem resolver estes problemas.

In hot climate areas, the electrical load in a building spikes, but not by the same amount daily due to various conditions. In order to cover the hottest day of the year, large cooling systems are installed, but are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified. A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day. The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings of different load profiles, all connected to the same cooling system with central CTES. The use of forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized, and utilized more efficiently during the day. At the same time, peak load savings are achieved, thus benefiting an electric utility company. The district presented in this dissertation comprises PV panels and three types of buildings – a mosque, a clinic and an office building. In order to have a good estimation for the required CTES charge for the next day, reliable forecasts for the PV panel outputs and the electrical load of the three buildings are required. In the model developed for the current work, dust was introduced as a new input feature in all of the forecasting models to improve the models' accuracy. Dust levels play an important role in PV output forecasts in areas with high and variable dust values. The overall solution used both the PV panel forecasts and the building load forecasts to estimate the CTES charge for the next day. The presented method was tested against the baseline method with no forecasting system. Multiple scenarios were conducted with different cooling system sizes and different CTES capacities. Research findings indicated that the presented method utilized the CTES charge more efficiently than the baseline method. This led to more savings in the energy consumption at the district level.
Doctor of Philosophy
In hot weather areas around the world, the electrical load in a building spikes because of the cooling load, but not by the same amount daily due to various conditions. In order to meet the demand of the hottest day of the year, large cooling systems are installed. However, these large systems are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified. A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day. The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings all connected to the same cooling system with central CTES. The use of the forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized and utilized more efficiently. At the same time, peak load is lowered, thus benefiting an electric utility company.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

With the potential to sustain the world’s energy needs, solar energy plays a major role for the renewable energy transition. However, inherent problems exist in solar energy forecasting, a very important tool for power plant operators that allows an efficient energy management and dispatch operations in the electric grid. In particular, concentrating solar power (CSP) systems, which rely on direct normal irradiance (DNI) and its high variability, which links uncertainty to the electrical energy outputs of CSP plants. The main atmospheric factors that influence DNI variation at surface are clouds and aerosols, which are misrepresented by current numerical weather prediction models. To provide accurate predictions of DNI for efficient CSP operations, particularly during periods of direct solar intermittency, the solar resource needs to be well characterized. Solution to this problem is still one of today’s challenges in solar forecasting. This thesis makes use of short-term forecasts of DNI from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), together with ground-based measurements in Alentejo region (southern Portugal). The evaluation of the solar resource in the region is based on the IFS predictions, as well as the prediction of energy production outputs from different CSP systems through the System Advisor Model (SAM) power plant simulator, in which the results are compared with local measured data. To improve the role that DNI forecasting has in CSP power plants, several post-processing techniques are used for the correction of hour and day-ahead values of DNI. Different operational strategies are discussed and proposed according to the obtained results; Resumo: Previsão de Curto Tempo de Radiação Normal Directa Através de Modelos Numéricos de Previsão do Tempo no Alentejo (Sul de Portugal): Implicações para as Tecnologias de Concentração Solar Com potencial para assegurar as necessidades energéticas do mundo, a energia solar desempenha um papel importante na transição energética renovável. Contudo, existem problemas inerentes na previsão de energia solar, uma ferramenta muito importante para os operadores de centrais eléctricas que permite uma gestão energética mais eficiente e operações de distribuição da mesma na rede eléctrica. Em particular, os sistemas de concentração de energia solar (CSP), que dependem da radiação normal directa (DNI) e da sua elevada variabilidade, atribuindo incerteza à geração de energia eléctrica resultantes de centrais CSP. Para fornecer previsões precisas para operações CSP eficientes, particularmente durante períodos de intermitência solar directa, o DNI precisa de ser bem caracterizado. Os principais factores atmosféricos que influenciam a variação de DNI à superfície são as nuvens e os aerossóis, que não são representados realisticamente pelos actuais modelos numéricos de previsão do tempo. A solução para este problema é ainda hoje em dia um desafio em previsão solar. Esta tese faz uso das previsões de curto-período de DNI do Integrated Forecasting System (IFS), modelo global do European Centre for Medium-Range Weather Forecasts (ECMWF), em conjunto com medidas à superfície na região do Alentejo (sul de Portugal). A avaliação do recurso solar na região é efectuada com base em previsões do IFS, tal como a previsão de outputs de produção energética de diferentes sistemas CSP através do simulador de centrais eléctricas System Advisor Model (SAM), onde os resultados são comparados com os obtidos com medidas meteorológicas locais. Para melhorar o papel que a previsão de DNI tem em centrais CSP, várias técnicas de pós-processamento são efectuadas para a correção de valores de DNI para a hora e dia seguinte. Diferentes estratégias de operação são discutidas e propostas de acordo com resultados obtidos.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART's active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Avec la mutation du paysage énergétique due au développement des énergies renouvelables, des véhicules électriques ou encore des systèmes de stockage, le réseau électrique actuel a besoin de se moderniser. Le concept de microgrid est une solution prometteuse basée sur les technologies de l'information et de la communication pour améliorer la gestion et l'efficacité de la production, du transport, de la distribution et de la consommation de l'électricité. Cependant, les défis technico-économiques associés à leur déploiement sont encore élevés. Ces travaux de thèse ont pour but d’apporter des contributions sur plusieurs points clés : prévision de la production et de la consommation, modélisation des équipements, et optimisation de la gestion du microgrid.Rivesaltes-grid est un démonstrateur de microgrid à l'échelle d'un bâtiment industriel composé d'un champ photovoltaïque de 60 kWc, de batteries lithium-ion de 85 kWh et d'un véhicule électrique. Il a permis de développer un système de gestion de l'énergie (EMS) innovant pour optimiser l'efficacité énergétique du microgrid. Cet EMS, basé sur une gestion par commande prédictive et la résolution d'un problème d'optimisation avec contraintes, permet de réduire de 6,2% le coût de fonctionnement. Cette gestion du microgrid nécessite comme entrées : (1) la prévision de production basée sur un algorithme de forêt aléatoire et une modélisation du champ PV par modèle 1-diode, (2) la prévision de la consommation à partir de l'algorithme de partitionnement k-means++ et (3) la modélisation dynamique du système de stockage avec ses contraintes
With the transformation of the energy landscape due to the development of renewable energies, electric vehicles and storage systems, the current grid needs to be modernized. Microgrid concept is a promising solution based on information and communication technologies to improve the management and efficiency of electricity generation, transmission, distribution and consumption. However, the technical and economic challenges associated with their deployment are numerous. The thesis aims to provide contributions on several key points: production and consumption forecasting, equipment modeling, and microgrid management optimization.Rivesaltes-grid is a microgrid demonstrator on the scale of an industrial building consisting of 60 kWp photovoltaic array, 85 kWh lithium-ion batteries and an electric vehicle. It has enabled the development of an innovative energy management system (EMS) to optimize the microgrids energy efficiency. This EMS, based on predictive control management and the resolution of a constrained optimization problem, reduces operation cost by 6.2%. This microgrid management requires as input: (1) the production prediction based on a random forest algorithm and a modeling of the PV field by 1-diode model, (2) the consumption prediction from partitioning algorithm k-means++ and (3) dynamic modeling of the storage system with its constraints

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.
Made available in DSpace on 2016-10-19T12:59:30Z (GMT). No. of bitstreams: 1 338190.pdf: 9968372 bytes, checksum: e1c28dfcf84e191f0457a82aa5715399 (MD5) Previous issue date: 2015
A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nível mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica às redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no período noturno. Mesmo assim, em alguns países, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtíssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta híbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.

Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.
Made available in DSpace on 2016-05-24T17:37:07Z (GMT). No. of bitstreams: 1 338190.pdf: 9968372 bytes, checksum: e1c28dfcf84e191f0457a82aa5715399 (MD5) Previous issue date: 2015
A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nível mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica às redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no período noturno. Mesmo assim, em alguns países, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtíssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta híbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.

Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.

Magíster en Ciencias de la Ingeniería, Mención Eléctrica
En un trabajo previo se propuso un control de mitigación de efecto nube que permitía disminuir los efectos nocivos de la nubosidad parcial sobre parques fotovoltaicos en la frecuencia de sistemas eléctricos de potencia. Esto último sin la necesidad del uso de acumuladores de energía. La estrategia se basa en la operación sub-óptima de los parques (operación en deload) con tal de disponer de reservas de potencia. A pesar que la implementación del sistema nombrado mejoró la frecuencia del sistema de forma significativa en comparación al caso base (sin el sistema de control), la operación en deload de los parques implica una gran cantidad de energía que no se está aprovechando, lo que no se consideró en la metodología. Con tal de mejorar esto, el siguiente trabajo propone un control de mitigación de efecto nube en parques fotovoltaicos de gran escala basado en una herramienta de pronóstico de radiación. Esto último permite disminuir las pérdidas de energía junto con mitigar los efectos de la nubosidad parcial, mediante la determinación de un nivel de deload en los parques fotovoltaicos usando dicho pronóstico. En primer lugar, esta tesis presenta una revisión bibliográfica y discusión del estado del arte de las técnicas de pronóstico en parques fotovoltaicos. Se muestra que la selección de la técnica de pronóstico depende en la información disponible y la ventana de tiempo del pronóstico, es decir, dependerá del caso de estudio. Dicho esto, se propone el uso de una técnica de pronóstico basada en redes neuronales en el Sistema Interconectado del Norte Grande (SING) de Chile. El pronóstico sirve para determinar el nivel de deload en el parque fotovoltaico para los siguientes 10 minutos, en función de una rampa de radiación. Los resultados muestran que la implementación de la técnica de pronóstico no solo mejora la respuesta en frecuencia del sistema, sino que también disminuye las pérdidas energéticas de forma significativa.
Este trabajo fue parcialmente financiado por el Proyecto CONICYT/FONDAP/15110019 "Solar Energy Research Center" SERC-Chile y el Instituto de Sistemas Complejos de Ingeniería (ISCI)

This thesis presents different optimization and forecasting models, with the focus on energy markets and renewable energy sources. The analysis approach is related to models for wind and solar power forecasts and those for electricity prices forecasts. The first study explores a Principal Component Analysis in combination with two post-processing techniques for the prediction of wind power and of solar irradiance produced over two large areas. The Principal Component Analysis is applied to reduce the datasets dimension. A Neural Network and an Analog Ensemble post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction. The second work explores two different techniques for the prediction of the Italian day-ahead electricity market prices. The predicted Italian prices are the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted using hourly data of the prices to be predicted and a large set of variables used as predictors (i.e. historical prices, forecast load, wind and solar power forecasts, expected plenty or shortage of hydroelectric production, net transfer capacity available at the interconnections and the gas prices). A Neural Network and a Support Vector Regression are applied on the different predictors to obtain the final forecasts. Different predictors’ combinations are analysed to find the best forecast. The results show that the best configuration is obtained using all the predictors together and applying the Neural Network to find the forecasted prices.

碩士
萬能科技大學
經營管理研究所
98
Roleum massive utilization used by human, then gradually reduced, so that people pursue the energy alternative unceasingly. This research made this prediction on the en-ergy financial products, for more people to refer to the future trend of financial products solar energy.In this study, GARCH model, Fuzzy time series and Fuzzy generalized auto-regressive conditional variance (Fuzzy-GARCH) model are employed to forecast the Claymore/MAC Global Solar Energy Index ETF(TAN) to predict. Three assessment criteria RMSE, MAE and MAPE are used to measure the forecast ability of the provided three models. Total 472 records of the closing price of Claymore Solar ETF collected from April 15, 2008 to February 26, 2010 are provided as sample dat for the forecasting model. As the results, Fuzzy-GARCH model has better forecast ability than the GARCH model and the Fuzzy time series. This study establishes a model for the Fuzzy-GARCH (4,8)with criteria, RMSE = 833.4297627, MAE = 666.3567535, MAPE = 0.007416, and also confirms that the interval size on the predictive ability of different settings would be affected in the Fuzzy time series model and the Fuzzy-GARCH model.

碩士
真理大學
企業管理學系碩士班
101
Taiwan is suitable to develop solar energy due to sufficient sun exposure, high temperature, and located in subtropical regions. Solar energy demand will be a potential orientation to study due to high cost and restriction on emission of greenhouse gases. Therefore, this study investigates solar energy output by coal, coal related product, crude, crude related product, gas, hydroelectric and nuclear power and construct the forecasting model to improve the prediction accuracy. Combing Neural network with grey system model GM (1,1) to establish the NNGM(1,1) forecast model in this study. The NNGM(1,1) model is compared to traditional ARIMA and regression model the NNGM(1,1) model in compared to. At first, using GM(1,1) model to forecaste the solar energy output, the mean absolute percentage error (MAPE) is up to 82.85%, it means the forecast is bad. Therefore, choosing four related factors to construct GM(1,4) model and improve MAPE to 4.04%. Then, only using neural network to establish the forecasting model, the MAPE is 2.47%. As the results, using the combination of neural network and grey forecast model to propose NNGM(1,4) model, which lower MAPE to 1.76%. Comparing to the traditional forecast models in this study, the traditional ARIMA forecast model can get the high prediction accuracy according to the sufficient history data.

MSc (Statistics)
Department of Statistics
This study discusses an application of partially linear additive quantile regression models in predicting medium-term global solar irradiance using data from Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts's algorithm. The best models are selected based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted R squared (AdjR2) and generalised cross validation (GCV). The accuracy of the forecasts is evaluated using mean absolute error (MAE) and root mean square errors (RMSE). To improve the accuracy of forecasts, a convex forecast combination algorithm where the average loss su ered by the models is based on the pinball loss function is used. A second forecast combination method which is quantile regression averaging (QRA) is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results show that QRA is the best model since it produces robust prediction intervals than other models. The percentage improvement is calculated and the results demonstrate that QRA model over GAM with interactions yields a small improvement whereas QRA over a convex forecast combination model yields a higher percentage improvement. A major contribution of this dissertation is the inclusion of a non-linear trend variable and the extension of forecast combination models to include the QRA.
NRF

Solar radiation is a primary driving force behind a number of solar energy applications such as photovoltaic systems for electricity generation amongst others. Hence, the accurate modelling and prediction of the solar flux incident at a particular location, is essential for the design and performance prediction of solar energy conversion systems. In this regard, literature shows that time series models such as the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) stochastic models have considerable efficacy to describe, monitor and forecast solar radiation data series at various sites on the earths surface (see e.g. Reikard, 2009). This success is attributable to their ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. On the other hand at the top of the atmosphere, there are no such conditions and deterministic models which have been used successfully to model extra-terrestrial solar radiation. One such modelling procedure is the use of a sinusoidal predictor at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle. We combine this deterministic model component and SARIMA models to construct harmonically coupled SARIMA (HCSARIMA) models to model the resulting mixture of stochastic and deterministic components of solar radiation recorded at the earths surface. A comparative study of these two classes of models is undertaken for the horizontal global solar irradiance incident on the solar panels at UKZN Howard College (UKZN HC), located at 29.9º South, 30.98º East with elevation, 151.3m. The results indicated that both SARIMA and HCSARIMA models are good in describing the underlying data generating processes for all data series with respect to different diagnostics. In terms of the predictive ability, the HCSARIMA models generally had a competitive edge over the SARIMA models in most cases. Also, a tentative study of long range dependence (long memory) shows this phenomenon to be inherent in high frequency data series. Therefore autoregressive fractionally integrated moving average (ARFIMA) models are recommended for further studies on high frequency irradiance.
M.Sc. University of KwaZulu-Natal, Durban 2014.

This thesis deals with quantifying the merits of Distributed Maximum Power Point Tracking (DMPPT), as well as providing solutions to achieve DMPPT in PV systems. A general method based on 3D modeling is developed to determine the energy yield of PV installations exploiting different levels of DMPPT granularity. Sub-string-level DMPPT is shown to have up to 30% more annual energy yield than panel-level DMPPT. A Multi-Input-Single-Output (MISO) dc-dc converter is proposed to achieve DMPPT in parallel-connected applications. A digital current-mode controller is used to operate the MISO converter in pseudo-CCM mode. For series-connected applications, the virtualparallel concept is introduced to utilize the robustness of the parallel connection. This concept is demonstrated on a three-phase boost converter. The topology offers reduced output voltage ripple under shading which increases the life-time of the output capacitor. The prototypes yield output power benefits of up to 46% and 20% for the tested shading conditions.

Yes
This paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather's prediction requirements from the Sun to the Earth. For each case, we consider the model's input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 "Improvement of operational models'' of the European Cooperation in Science and Technology (COST) Action ES0803 "Developing Space Weather Products and services in Europe'' and therefore this review focuses on the progress achieved by European research teams involved in the action.