Conventional energy sources are not only finite and depleting rapidly, but are a major source of global warming because they are key contributors of greenhouse gases to the atmosphere. Renewable energy sources are one important approach to these challenges. Distributed micro-generation energy sources are expected to increase the diversity of energy sources for the grid, but also increase the flexibility and resilience of the grid. Furthermore, it could reduce the domestic energy demand from the grid by enabling local consumption of energy generated through renewable sources. The most widely installed renewable energy generation systems in domestic environments, in UK, are based on solar power. However, there is a common recurring issue related to output intermittency of most promising renewable energy generation methods (e.g. solar and wind), resulting in a temporal energy mismatch between local energy generation and energy consumption. Current state-of-the-art technologies/solutions for tackling temporal energy mismatch rely on various types of energy storage technologies, most of which are not suitable for the domestic environments because they are designed for industrial scale application and relatively costly. As such energy storage system technologies are generally not deemed as economically viable or attractive for domestic environments. This research project seeks to tackle the temporal energy mismatch problem between local PV generated energy and domestic energy consumption without the need for dedicated energy storage systems; without affecting the householders comfort and/or imposing operational burdens on the householders. Simulation has been chosen as the major vehicle to facilitate much of the research investigation although data collated from related research projects in the UK and Jordan have been used in the research study. Solar radiation models have been established for predicting the solar radiation for days with clear-sky for any location at any time of the year. This model has achieved a correlation factor of 0.99 in relating to the experimental data-set obtained from National Energy Research Centre Amman/Jordan. Such a model is an essential component for supporting this research study, which has been employed to predict the amount of solar power that could be obtained in different locations and different day(s) of the year. A Domestic Energy Ecosystem Model (DEEM) has been established, which is comprised of two sub-models, namely “PV panels” and “domestic energy consumption” models. This model can be configured with different parameters such as power generation capacity of the photovoltaic (PV) panels and the smart domestic appliances to model different domestic environments. The DEEM model is a vital tool for supporting the test, evaluation and validation of the proposed temporal energy mismatch control strategies. A novel temporal energy mismatch control strategy has been proposed to address these issues by bringing together the concepts of load shifting and energy buffering, with the support of smart domestic appliances. The ‘What-if’ analysis approach has been adopted to facilitate the study of ‘cause-effect’ under different scenarios with the proposed temporal energy mismatch control strategy. The simulation results show that the proposed temporal energy mismatch control strategy can successfully tackle the temporal energy mismatch problem for a 3 bedroom semi-detached house with 2.5kWp PV panels installed, which can utilise local generated energy by up to 99%, and reduce the energy demand from the grid by up to 50%. Further analysis using the simulation has indicated significant socio-economic impacts to the householders and the environment could be obtained from the proposed temporal energy mismatch control strategy. It shows the proposed temporal energy mismatch control strategy could significantly reduce the annual grid energy consumption for a 3 bedrooms semi-detached house and produce significant carbon reductions.

Recently, commercial softwares for PV system simulation released bifacial extensions. While research laboratories have developed their own simulation tools, in both cases it is imperative to display their applicability, as well as continuously assess their accuracy and/or limitations in practice, i.e. for different bifacial PV systems and field conditions. This paper presents a design and energy yield simulation study of two bifacial PV systems installed and operating in Nordic climate conditions, i.e. in Vestby, Norway ( System 1) and in Halmstad, Sweden (System 2). The aim of this study is: • To validate and compare the accuracy of two bifacial PV simulation tools newly featured in the software platforms of PVsyst and Polysun respectively, against real-field energy yield data. Each investigated system is modeled and analyzed with both simulation tools, resulting in four individual case stu dies. Further details on the systems’ monitoring set-up, the data input, modeling steps, and the involved uncertainties are presented in this paper. The results of the four case studies show higher percent deviations (both monthly and hourly data) between simulated energy results and real energy results during winter periods compared to summer periods. System 1 had a lower bifacial gain (around 2%) than System 2 which ranges from 2% in summer periods to 25% during winter. The collected field data had too high of an uncertainty to determine whether the bifacial PV simulation extensions are accurate within a certain tolerance. The reason for higher simulation inaccuracy in the winter is due to: lower production, higher uncertainty in albedo, and more diffuse irradiation. It is recommended for the bifacial PV simulation extensions include options for considering a variable albedo. The bifacial gain in System 2 was higher in the winter because of the higher albedo value given whereas in System 1, the albedo value was kept constant. Further parametric studies should be conducted on the bifacial gain using vertical mounted bifacial PV modules oriented east and west for Nordic climate conditions.

Aujourdhui, les énergies renouvelables deviennent progressivement des énergies à part entière qui rivalisent avec des énergies fossiles du point de vue coût et performance de production. Cependant, souvent leurs systèmes de conversion de lénergie en électricité souffre dun manque doptimisation qui en font encore des systèmes trop chers et présentant des déficiences importantes en rendement et en fiabilité. Pour cela, bien quil existe de plus en plus de travaux de recherches prouvant la viabilité de ce type de sources comme par exemple, lénergie photovoltaïque (PV) ou lénergie éolienne, beaucoup de réticentes existent pour installer ces systèmes à grande échelle autant en production de masse que chez des particuliers. A côté des autres laboratoires français, le LAAS-CNRS a choisi dapporter sa contribution sur la partie «Système» de la chaîne de conversion. En effet, du fait de lexistence de problèmes de non-optimisation électrique des systèmes et du manque déquipes de recherche sintéressant à ces axes, les points à résoudre se situaient alors autant sur la partie conversion électrique que thermique du générateur PV. Les premiers travaux entrepris se sont donc focalisés sur loptimisation de la partie conversion électrique. Pour cela, en sappuyant sur la création, le développement et lévolution constante du site de démonstration de 1kW crête PV entièrement instrumenté au sein même du LAAS, différentes architectures de conversion électriques dédiées au PV, ont été développées, notamment en collaboration avec lUniversité Rovira i Virgili de Tarragone (URV) et lUniversité Polytechnique Catalane de Barcelone (UPC). Très rapidement, nous nous sommes aperçus que, même si les systèmes PV faisaient des progrès considérables, ils ne pourraient à eux seuls représenter une source dénergie fiable. En effet, les variations de production étant fortement couplés aux données météorologiques, la production ne pouvait pas forcément être assurée lorsque lutilisation se n faisait sentir. Nous avons donc pensé à coupler les systèmes PV à dautres sources dénergie ainsi à travers des moyens de stockage. La maturité des études sur le photovoltaïque montre, quant à elle, de nouveaux débouchés, notamment sur les systèmes embarqués et les microsystèmes de très faibles puissances. Nous avons donc développé un nouvel axe de recherche depuis 2000 au sein du LAAS-CNRS sur les micro-sources et micro-convertisseurs intégrés dédiés aux microsystèmes. En effet, aujourdhui, les études menées sur loptimisation de convertisseurs statiques dénergie peuvent se généraliser à un certain nombre dapplications vis à vis de leur alimentation. Les objectifs sont de minimiser la taille et le volume tout en limitant les coûts de développement des nouveaux produits et en réduisant notamment les phases de prototypage réel. En effet, quel que soit le type dapplication visée (militaire, spatial, télécommunications, etc&), les nouvelles alimentations doivent être compactes, semi-intégrées ou bien, dans un futur proche, totalement intégrées. Pour cela, elles doivent être modélisables avec une grande précision, en vue doptimiser, dès leur conception, les contraintes de coût, de montée en fréquence et de puissance massique. En résumé, le but, dans les années futures, est datteindre de forts rendements de conversion sur les nouvelles alimentations devant avoir des tailles compatibles avec leurs applications, dans la droite ligne des travaux accomplis.

Made available in DSpace on 2016-12-12T20:27:37Z (GMT). No. of bitstreams: 1 Joao Cabral.pdf: 4385545 bytes, checksum: c09296f90add051ed37bd87320b15421 (MD5) Previous issue date: 2013-03-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This Master Thesis proposes a high gain non-isolated DC-DC converter applied on the processing of PV energy. The proposed converter is a boost converter with quadratic characteristic and with central point at its output. Basic operations and electric characteristics of a PV cell are described, including the procedures to determine its parameters. A model for numeric simulation is presented. A review of the high gain non-isolated DC-DC converters topologies is presented. Shows the converter´s transfer functions and the control strategy adopted as well as the design of control circuits. The control system is consisting of three loops, an internal loop of input current control, an external loop of total output voltage control and an additional loop of voltage unbalance control. The simulation and experimental results are shown to validate the analysis developed and demonstrate the performance of the control system adopted. / Esta Dissertação de Mestrado propõe um conversor CC-CC não isolado de elevado ganho para aplicação no processamento de energia solar fotovoltaica. O conversor proposto é um boost com característica quadrática e com ponto médio na sua saída. Descrevem-se o funcionamento básico e as características elétricas de uma célula fotovoltaica, incluindo-se o procedimento da determinação dos parâmetros e da modelagem dos módulos fotovoltaicos, apresentando-se um modelo para simulação numérica. Apresenta-se uma revisão de topologias de conversores CC-CC não isolados com elevado ganho estáticos. Apresentam-se as funções de transferência do conversor e a estratégia de controle adotada bem como o projeto dos circuitos de controle. O sistema de controle composto por três malhas de controle, uma malha interna de corrente de entrada, uma malha externa de tensão total e uma malha adicional de equalização de tensão. Resultados de simulação e experimentais são apresentados para validar as análises desenvolvidas e demonstrar o desempenho do sistema de controle adotado.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.