Models for the Optimization and Evaluation of Photovoltaic Self-Consumption Facilities

The results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. Different models of machine learning are proposed to predict these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the multilayer perceptron, with an average absolute error of 0.55 and an absolute relative error of 3%

Modelos para la predicción del autoconsumo en sistemas fotovoltaicos conectados a red

CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia SolarRESUMEN: En este trabajo se presentan los resultados obtenidos para la modelización y optimización de instalaciones fotovoltaicas de autoconsumo. Se han obtenido las curvas de autoconsumo y autosuficiencia para diferentes perfiles de consumo horario en función de la potencia pico instalada y el tamaño de la batería. El estudio se ha realizado para tres ciudades españolas con diferentes condiciones climáticas. Para la generalización de los resultados se proponen diferentes modelos de aprendizaje automático que permiten estimar estos parámetros. Las variables de entrada de estos modelos están relacionadas con la configuración de la instalación, su ubicación y el tipo de perfil de consumo. El modelo que arroja mejores predicciones en el parámetro de autosuficiencia es Random Forest, que en la validación cruzada tiene un error relativo del 5%. Para la predicción del autoconsumo, el modelo que mejor se comporta es el Perceptrón Multicapa, con un error absoluto promedio de 0.55 y un error relativo del 3%.ABSTRACT: The results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. In order to generalize the obtained results, different models of machine learning are proposed to estimate these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the Multilayer Perceptron, with an average absolute error of 0.55 and a relative error of 3%.info:eu-repo/semantics/publishedVersio

Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy)

Modelos para la evaluación y optimización de instalaciones fotovoltaicas de alto consumo.

instalación fotovoltaica de autoconsumo en función de los parámetros de la instalación como son la potencia pico y la capacidad de la batería, del tipo de consumo y del emplazamiento. Para ello, se ha realizado un detallado análisis del funcionamiento de sistemas fotovoltaicos de autoconsumo en viviendas con y sin medidas de ahorro energético, para distintos perfiles de consumo y con y sin sistemas de almacenamiento y, como resultado, se ha propuesto un modelo para la estimación del tamaño óptimo de una instalación fotovoltaica en función del emplazamiento. También se ha propuesto un modelo que permite conocer cuál será la autosuficiencia de una instalación de autoconsumo. Los datos de entrada a este modelo están relacionados con la configuración de la instalación (potencia pico y capacidad de batería), el tipo de consumo (se han definido para ello cuatro tipos de consumo típicos) y el emplazamiento de la instalación (energía recibida y temperatura ambiente). Por último, se ha realizado un análisis y evaluación de la influencia de la resolución temporal utilizada en la estimación de los parámetros que evalúan el funcionamiento de una instalación fotovoltaica de autoconsumo. Los modelos propuestos y los resultados obtenidos permiten determinar los distintos escenarios que definen las condiciones de optimización de este tipo de sistemas. Fecha de lectura de Tesis Doctoral: 25 de junio de 2019.La autogeneración de energía eléctrica utilizando tecnología fotovoltaica es una oportunidad y una herramienta imprescindible para alcanzar los objetivos relacionados con el cambio climático a los que España se ha comprometido, reducir el impacto medioambiental de la generación eléctrica y democratizar el uso y gestión de la energía, situando al ciudadano en el centro del sistema. Así, los sistemas fotovoltaicos de autoconsumo se consideran prioritarios en la transición a un modelo energético sostenible centrado en el consumidor, son distribuidos, permiten la generación de electricidad en el punto de consumo, contribuyen a electrificar la demanda, reducir las emisiones, utilizan un recurso de coste cero (energía solar) y contribuyen a una edificación más sostenible (edificios de energía cero. El principal objetivo de este trabajo de investigación era diseñar estrategias y modelos que permitan conocer y optimizar el consumo energético en viviendas unifamiliares con instalación fotovoltaica de autoconsumo, teniendo en cuenta los perfiles de producción de la instalación fotovoltaica, los distintos perfiles de consumo residencial y la ubicación y configuración de la instalación fotovoltaica. La realización de este trabajo de investigación ha permitido, por una parte, diseñar estrategias que permiten conocer y optimizar el consumo energético en viviendas -tanto pisos como viviendas unifamiliares- con instalación fotovoltaica de autoconsumo, en función del perfil de consumo de la vivienda y la ubicación de la misma; por otra, proponer un modelo para estimar la autosuficiencia esperada de una instalación fotovoltaica de autoconsumo en función de los parámetros de la instalación como son la potencia pico y la capacidad de la batería, del tipo de consumo y del emplazamiento

Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.

Lao, Liyun - Associate SupervisorThis thesis proposes a novel electric grid in the airside to meet zero-emission targets for ground movement operations in future airports, as mandated by Aeronautics Research performance target in Europe's (ACARE) FlightPath 2050. The grid delivers power from a renewable energy source through a flexible powerline using an autonomous electric taxiing robot (A-ETR) based on the concept of Energy As A Service (EAAS) for taxiing large aircraft and charging stations for ground vehicles. Four layers of optimisation are required to realise the viability of this new grid. The first optimisation layer involves creating an analytical model of the A-ETR using real-world data from Cranfield University's Airport based solar PV system and its Boeing 737 research aircraft and optimising its performance and efficiency using vehicle-level data-driven machine learning- based optimisation. As a result, the proposed grid achieves zero-emission taxiing and a 91% reduction in fuel compared to a standard baseline. The second layer optimises energy management in the microgrid using machine learning-based forecasting models to predict PV output and optimise charging and discharging cycles of A-ETR batteries to match solar resources and electricity rates. The result shows that the support vector regression (SVR) model best predicted PV output and optimised BESS charge/discharge cycles to achieve zero-emission airport ground movement operations while reducing the microgrid operating costs. However, ground traffic and load profiles increase as the model expands to include commercial airports. Therefore, the third optimisation layer develops a machine learning-based data-driven energy prediction optimisation to ensure microgrid resilience under the increased load. The model employs the Facebook Prophet algorithm to enhance the precision of energy demand prediction for airport ground movement operations across three- time horizons. The results facilitate the generation of reliable forecasts for clean energy production and ground movement energy demand at the airport. A fourth layer of optimisation has been developed to address the limitations of solar PV energy, which depend on the weather and cannot be dispatched, as well iii as the increase in airport traffic. The layer uses wind power and data from a "green" airport to complement PV power output. This model uses the stochastic model predictive control-based cascade feedforward neural network (SMPC- CFFNN) to optimise power flow between the microgrid and RES sources and support V2G capabilities. The results demonstrate that a Zero-emission microgrid for ground movement at green airports can be achieved through optimal power flow management and time optimisation. Reliability and resilience are crucial for a proposed microgrid ecosystem. We consider different network configurations to connect the existing airport grid. Two microgrid architectures, LVAC and LVDC, are compared based on their point of common connections (PCC) to evaluate the technical and economic implications on the airport's distribution network. We verify and validate the model's performance in terms of power quality, short circuit fault levels, system protection requirements, voltage profile, power losses, and equipment/system overloading to determine the optimal architecture. The results indicate that the A-ETR can provide ancillary services to the grid and enable novel emergency response systems. The comprehensive results from the multi-layered system-level optimisation approach adopted in this thesis not only validate the novelty of the proposed study but also serve to provide compelling evidence for its potential to provide viable solutions to the electrification challenges for future green airports by creating an ecosystem between airport ground operations and on-site renewable energy generating sources.PhD in Energy and Powe

As Energias Renováveis na Transição Energética : Livro de Comunicações do XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar

CIES2020: XVII Congresso Ibérico e XIII Congresso Ibero-Americano de Energia Solar, Lisboa, Portugal: LNEG, 3-5 Novembro, 2020.RESUMO: O CIES2020, reúne sob o lema da “As Energias Renováveis na Transição Energética”, refletindo uma conjuntura de mudança necessária e urgente em todos os sectores das nossas Sociedades, no nosso comportamento no uso da “Energia”, quer em termos individuais, nas famílias nas empresas e sobretudo na mudança de paradigma dos Sistemas Energéticos que impactam a todos os níveis, nas Cidades, nos Edifícios, nos Transportes, e onde o papel das Energias Renováveis assume um papel prioritário e principal, na luta contra as alterações climáticas, a descarbonização energética na defesa do Planeta e da sustentabilidade das futuras gerações. O CIES2020, apresentou-se com 3 tópicos principais: 1) As Energias Renováveis na Tran sição Energética; 2) As Energias Renováveis no Desenvolvimento Sustentável das Comunidades e 3) As Energias Renováveis a Sociedade e a Economia . Tentámos assim abranger todas as áreas tecnológicas das Energias Renováveis, as suas aplicações e utilizações, bem como os novos desafios futuros que estão a acontecer em termos de Inovação Tecnológica e respetivos impactos na Sociedade.info:eu-repo/semantics/publishedVersio