Forecasting for concentrated solar thermal power plants in Australia

Up to 50% of electricity needs in Australia could be supplied by solar power. At these high levels of solar power generation, solar forecasting is necessary to manage the impact of solar variability. However, there has been little research on using solar forecasting in Australia. This study used modelling to investigate the benefits of using short-term and long-term solar forecasts to operate a concentrated solar thermal (CST) plant for a year at four sites that covered different climate zones within the Australian National Electricity Market. Using 1-hour ahead short-term forecasts increased net value by $0.90-$2.07 million for a CST plant with storage, and by $0.76-$3.10 million for a CST plant without storage. It also improved reliability by reducing the equivalent forced outage rate by 21-38 percentage points for a CST plant with storage, and by 16-42 percentage points for a CST plant without storage. Using 1-hour forecasts achieved 59%-94% of the net value achievable if the 48-hour forecast were perfect. At each site, the highest net value and reliability were achieved by a CST plant with storage and using 1-hour forecasts, thus a CST plant should have both storage and short-term forecasts. If only one can be used, then a CST plant with storage and without 1-hour forecasts achieves higher net value, whereas a CST plant without storage and with 1-hour forecasts achieves higher reliability. These results demonstrated that using short-term forecasts is beneficial for CST plants that operate in electricity markets that allow updated bids to be submitted at short-term time frames. The results can be used to estimate the return on investment in obtaining short-term forecasts for operating a CST plant. Furthermore, the research method can be adapted into a tool for estimating value to assist CST plant project planning

Redes de sensores para la predicción solar a corto plazo en el marco de las microgrids y smartcities

En los últimos años, la potencia fotovoltaica instalada global ha crecido notablemente, llegando a superar el 20\% de la demanda energética en varios países. Esto se debe en parte a la reducción de costes de esta tecnología y la política de promover el uso de energías renovables. La producción de la energía fotovoltaica depende directamente de los niveles de radiación solar incidente sobre los paneles, que se trata de un recurso externo y variable. La irradiancia solar fluctúa principalmente por dos factores, pero la mayor variabilidad está asociada a la presencia de nubes, y estas variaciones tienen una duración que va desde unos pocos segundos hasta varios minutos. Debido al funcionamiento del mercado eléctrico y a la nula inercia en la producción energética de estos sistemas, los productores fotovoltaicos necesitan de predicciones precisas en diferentes horizontes temporales con el fin de maximizar la energía ofertada en el mercado, incrementando de este modo la integración de la misma. Por otra parte, también necesitan datos en tiempo real para una gestión más óptima del sistema fotovoltaico. Las predicciones a corto plazo se emplean para el sistema de control y balance de la producción energética, y a medio plazo para la programación y venta de energía en el mercado eléctrico, sin embargo, los sistemas actuales de predicción son escasos y caros para ser contemplados en sistemas de media y pequeña escala. Numerosos estudios han intentado cubrir la necesidad de predicción a corto plazo estimando espacio-temporalmente el campo de irradiancia con cámaras de cielo completo e imágenes de satélite, sin embargo, estos métodos están limitados por la problemática de la conversión de imagen a irradiancia. Investigadores influyentes en este área creen que las redes de sensores de irradiancia pueden jugar un papel fundamental en este contexto, ofreciendo en tiempo real varias medidas espaciales y con la alta resolución temporal necesaria. La información espacio-temporal capturada por la red permitiría estimar el campo de irradiancia y analizar su evolución, capturando incluso los eventos más rápidos. Las tecnologías inalámbricas han evolucionado en el marco de las ciudades inteligentes y el internet de las cosas, apareciendo tecnologías que se adecuan a diferentes escenarios. El interés mostrado en estos sistemas ha producido un abaratamiento de los módulos de comunicaciones inalámbricas, gracias a la economía de escala. Las redes de sensores podrían beneficiarse de estas tecnologías inalámbricas, ofreciendo a su vez un ahorro en costes del despliegue respecto a su equivalente cableado y una mayor flexibilidad para integrar nuevos nodos en la red. Por ello, esta tesis se pretende estudiar el potencial de estas redes inalámbricas como fuente de información crítica para la gestión a corto plazo de sistemas fotovoltaicos, y la explotación de los datos de la misma, implementando y desarrollando algoritmos con estos datos con fines de predicción de la producción y para la operación óptima de estos sistemas.In recent years, global installed photovoltaic power has grown significantly, exceeding 20% of energy demand in several countries. This is partly due to the cost reduction of this technology and the policy of promoting the use of renewable energies. Photovoltaic energy production depends directly on the levels of solar radiation incident on the panels, which is an external and variable resource. Solar irradiance fluctuates mainly due to two factors, but the greatest variability is associated with the presence of clouds, and these variations range in duration from a few seconds to several minutes. Due to the functioning of the electricity market and the lack of inertia in the energy production of these systems, PV producers need accurate forecasts at different time horizons in order to maximize the energy offered in the market, thus increasing the integration of the same. On the other hand, they also need real-time data for more optimal PV system management. Short-term forecasts are used for the energy production control and balancing system, and medium-term forecasts are used for scheduling and selling energy in the electricity market, however, current forecasting systems are scarce and expensive to be contemplated in medium and small-scale systems. Numerous studies have attempted to address the need for short-term forecasting by estimating the spatio-temporal irradiance field with full-sky cameras and satellite imagery, however, these methods are limited by the problems of image-to-irradiance conversion. Influential researchers in this area believe that irradiance sensor networks can play a key role in this context, providing various spatial measurements in real time and with the necessary high temporal resolution. The spatio-temporal information captured by the network would allow estimating the irradiance field and analyzing its evolution, capturing even the fastest events. Wireless technologies have evolved within the framework of smart cities and the internet of things, with the emergence of technologies that are suitable for different scenarios. The interest shown in these systems has led to a reduction in the cost of wireless communications modules, thanks to economies of scale. Sensor networks could benefit from these wireless technologies, offering savings in deployment costs compared to their wired equivalent and greater flexibility to integrate new nodes in the network. Thus, this thesis aims to study the potential of these wireless networks as a source of critical information for the short-term management of photovoltaic systems, and the exploitation of the data from it, implementing and developing algorithms with this data for production prediction purposes and for the optimal operation of these systems

Cloud motion estimation in seviri image sequences

Determination of atmospheric dynamic characteristics from remote sensing imagery is fundamental in weather and climate studies. The SEVIRI radiometer, on board the MSG, with its 12 bands and 15 minutes sensing capability provides an important amount of information for cloud tracking. In this work, we have first conducted a detailed evaluation of twelve region matching techniques in order to select those providing the best results. For this performance evaluation, databases of synthetic and real sequences have been used. Next, the best metrics have been incorporated in a new methodology that includes a preliminary stage that segments cloudy structures to initialize the optimum motion estimation parameters (template size and search window dimensions). Also a study region mask is generated to disable the application of the motion estimation algorithm in unreliable areas, thus, eliminating erroneous vectors and decreasing the computation times