A Distributed Real-Time Short-Term Solar Irradiation Forecasting Network for Photovoltaic Systems

Solar irradiation forecasting is essential for PV connected electrical grids to maintain reliability, stability, and effective matching of real-time demand to power distribution. This research paper develops and evaluates proposed forecasting methods using wireless sensor networks. Each node of the network is capable of monitoring illuminance data and communicate it through RF and/or WiFi. The nodes are calibrated with respect to irradiance data from an industry-standard pyranometer. Power consumption of each node type is also collected at different operating states. The proposed sensor network can estimate a cloud motion vector or a cloud shadow’s speed and direction from the data collected. By processing the collected data further, a forecasted solar irradiance ramp-down time-of-arrival is possible. The results are evaluated for both artificial and on-site cloud shadows

Spatio-temporal solar forecasting

Current and future photovoltaic (PV) deployment levels require accurate forecasting to ensure grid stability. Spatio-temporal solar forecasting is a recent solar forecasting approach that explores spatially distributed solar data sets, either irradiance or photovoltaic power output, modeling cloud advection patterns to improve forecasting accuracy. This thesis contributes to further understanding of the potential and limitations of this approach, for different spatial and temporal scales, using different data sources; and its sensitivity to prevailing local weather patterns. Three irradiance data sets with different spatial coverages (from meters to hundreds of kilometers) and time resolutions (from seconds to days) were investigated using linear autoregressive models with external inputs (ARX). Adding neighboring data led to accuracy gains up to 20-40 % for all datasets. Spatial patterns matching the local prevailing winds could be identified in the model coefficients and the achieved forecast skill whenever the forecast horizon was of the order of scale of the distance between sensors divided by cloud speed. For one of the sets, it was shown that the ARX model underperformed for non-prevailing winds. Thus, a regime-based approach driven by wind information is proposed, where specialized models are trained for different ranges of wind speed and wind direction. Although forecast skill improves by up to 55.2 % for individual regimes, the overall improvement is only of 4.3 %, as those winds have a low representation in the data. By converting the highest resolution irradiance data set to PV power, it was also shown that forecast accuracy is sensitive to module tilt and orientation. Results are shown to be correlated with the difference in tilt and orientation between systems, indicating that clear-sky normalization is not totally effective in removing the geometry dependence of solar irradiance. Thus, non-linear approaches, such as machine learning algorithms, should be tested for modelling the non-linearity introduced by the mounting diversity from neighboring systems in spatio-temporal forecasting

Solar Forecasting and Integration in Power Systems

Renewable energy resources are becoming critical players in the electricity generation sector, primarily due to viability in combating global warming, effectiveness in reducing pollution caused by fossil fuel based generation, and diversifying energy mix to ensure energy security and sustainability. Solar energy is one of the most common types of renewable energy that has grown rapidly over the past decade and is anticipated to grow even faster in the future. Power supply from renewable resources is forecasted to surpass other types of generation in a foreseeable future. Numerous factors, including but not limited to the dropping cost of solar technology, environmental concerns, and the state and governmental incentives, have made the path for a rapid growth of solar generation. However, increased generation from renewable resources exposes the power system to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. An accurate solar forecasting method, which takes into account generation variability and is able to identify associated uncertainty, can support a reliable and cost-effective deployment. More and more large-scale solar PV farms are expected to be integrated in the existing grids in the foreseeable future in compliance with the energy sector renewable portfolio standards (RPS) in different states and countries. The integration of large-scale solar PV into power systems, however, will necessitate a system upgrade by adding new generation units and transmission lines. This dissertation proposes a forecasting model that aims to enhance the forecasting result and reduce errors. The proposed model utilizes a new approach to overcome some of significant challenges in solar generation forecasting. The model includes different data processing stages in order to ensure the quality of the data before it is fed to the forecasting tool. The model undergoes further enhancement such as forecasting methods combination and multilevel measurements application. Numerical simulations exhibit the merits of the proposed method through testing under different weather conditions and case studies. Moreover, a co-optimization generation and transmission planning model is proposed to maximize large-scale solar PV hosting capacity. The solution of this model further determines the optimal solar PV size and location, along with potential required PV energy curtailment. Numerical simulations study the proposed co-optimization planning problem with and without considering the solar PV integration and exhibit the effectiveness of the proposed model

Implementação de um sistema de previsão de produção fotovoltaica e consumos de um Edifício Inovador

Nos dias de hoje, existe a consciencialização de que a energia fotovoltaica é aquela que apresenta uma maior sustentabilidade energética dos edifícios. O objetivo desta dissertação foi prever a energia fotovoltaica produzida num edifício inovador, tendo por base os dados da temperatura ambiente e da irradiância, de modo a comparar os valores previstos com os valores reais. Para a realização dos sistemas de previsão usamos o software Matlab R2019b e um modelo de redes neuronais do tipo NARX. Foi realizado um pré-processamento dos dados fornecidos com ajustamento do intervalo entre cada amostra (15 minutos) com divisão dos dados em 2 partes (treino e validação). No treino usamos os dados entre 2015 e 2017 e na validação os do ano de 2018. Quanto ao intervalo de medição a considerar para as 2 amostras foi entre as 5h e as 21h, e as redes foram testadas com 5, 8, 10, 12 e 15 neurónios na camada oculta e com conjuntos de treino de 15, 20, 25 e 30 dias de dados. Após definirmos o conjunto de treino e o número de neurónios a aplicar nos preditores da temperatura e da irradiância (variáveis de entrada), estimamos a produção fotovoltaica (variável de saída). As principais conclusões relativamente à produção fotovoltaica demonstraram que, os meses que apresentam valores mais elevados de produção (real e prevista) e com maiores diferenças ao nível da produção total, foram os meses do início do ano, onde os dias são mais curtos. Os que apresentaram valores mais baixos, embora com uma tendência de subida, foram os meses entre a primavera e o outono (dias maiores). Foi também durante estes meses que observamos uma menor diferença na produção fotovoltaica, entre a produção real e prevista, o que demonstra que nesta fase houve um melhor comportamento dos dados usados, e por sinal uma melhor performance que pode ser explicada pela presença de mais dias de sol e dias mais longos

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