Conselho Nacional de Desenvolvimento Científico e Tecnológico / Electrical energy generation with photovoltaic cells is being more utilized. Not only on large scale systems, but also in small ones connected to the grid. Parallel operating with the great generators from power companies, in a non-centralized way of operation, supplying low power, installed in houses, commerce establishments, industry, with the goal to minimize the loss in transmission, for being installed at the same consumption place. This work presents a FPGA device controller of a four transistor flyback inverter for maximum power point in photovoltaic systems. Despite this system has low power it contributes to search simple and low cost alternatives for generating of electrical power in a decentralized manner, which does not use battery banks connected parallel to network of energy near to consumers. / Geração de energia elétrica a partir de painéis fotovoltaicos vem sendo cada vez mais utilizada, não somente em sistemas fotovoltaicos de grande porte, como também em pequenos sistemas conectados a rede CA. Interligada paralelamente aos grandes geradores da concessionária de energia de forma descentralizada em sistemas de pequeno porte e baixas potências, instalados em residências, estabelecimentos comerciais, indústria, com o objetivo de minimizar perdas por transmissão por estarem instalados nos próprios locais. Este trabalho apresenta um controle num dispositivo FPGA de um inversor flyback a quatro transistores para máxima potência em sistemas fotovoltaicos. Apesar da baixa potência este contribui para a busca de alternativas simples e de baixo custo para geração de energia elétrica de forma descentralizada, não utilizando bancos de bateria conectados paralelamente a rede de energia próxima aos consumidores. / Mestre em Ciências

The industry sector accounts for a vast amount of the world’s total energy use, as much as 37% during 2018. Using energy in a sustainable way is necessary from both an environmental and an economical perspective, and it is therefore relevant to take measurements that result in a more efficient use of energy. One way for industries to become more energy efficient is to recover the waste heat, which is energy that otherwise would go to waste. This report aims to find a method to recover and reuse the low temperature waste heat available at the life science company Cytiva’s production site in Uppsala, Sweden. The proposed solution will be to use the waste heat for heating a vertical greenhouse. The study will examine whether this is feasible, and also how installing photovoltaics affects the energy use. Furthermore, the environmental impact of operating the greenhouse with waste heat is also investigated by calculating the CO2 equivalent. The low temperature waste heat that Cytiva provides relevant for this study is 6683 kW, which will be used to heat up the greenhouse. Simulations in the software IDA ICE will be used to construct and simulate a model of the vertical greenhouse. Results from the simulations show that the chosen size, 25 x 50 x 35.5 meters, gives a good approximation according to the wanted temperature range, 18.3-32.2°C. Furthermore, the results imply that the total energy use, 790 652 kWh, and average power, 90.26 kW is less than the available waste heat and there is a large amount that still is unused. The CO2 equivalent is calculated to be 29 317 kg. A sensitivity analysis is made to evaluate the window-to-wall ratio as well as the size of the entire greenhouse. It showed that both parameters are critical and makes a big difference in the simulations.

The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, several discrepancies exist in a PV system when implemented for real-world applications. Among several other existing problems related to Photovoltaics, in this work, we deal with maximum power point tracking (MPPT) under Partial Shading (PS) conditions. MPPT is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex.

One of the key challenges the world will need to face during the 21st century is global warming and the consequent climate change. Its presence is indisputable, and decarbonizing the gird emerges as one of the required pathways to achieve global sustainable objectives. Solar energy power plants have the potential to revert this situation and solve the problem. One way to harness this energy is through Concentrated Solar Power plants. The major advantage and potential of this technology is its ability to integrate cost-effective Thermal Energy Storage (TES), which is key with such an inherently intermittent resource. On the other hand, the drawback is the high current Levelized Cost of Energy (LCOE). The other main way to harness that highlighted solar energy is the use of Photovoltaic panels, which have recently achieved very competitive LCOE values. On the other hand, the storage integration is still a very pricey option, normally done with Battery Energy Storage Systems (BESS). As a conclusion, a hybrid power plant combining the LCOE of the PV and the TES of the CSP emerges as the key way of achieving a very competitive solution with a big potential. This master thesis aims at exploring the possibilities of a hybrid CSP and PV power plant with a sCO2 power cycle, integrated in the primary, secondary and tertiary electricity markets. To achieve this purpose, firstly, a Python-based Energy Dispatcher was developed to control the hybrid power plant. Indeed, the Dispatcher is the tool that decides when to produce, when to store… following an optimization problem. This can be formulated mathematically, and that was done and integrated into the Python code using Pyomo, a software for optimization problems. As a result, the Dispatcher achieved an effective control of the plant, showing intelligent decisions in detailed hourly analyses. The results were very promising and included optimization functions as maximizing the profitability of the plant or the total production, among others. To proceed with the Techno-economic assessment of the hybrid plant, the electricity markets were studied. The main source of income of any power plant is normally the revenue from selling electricity to the grid, but since there are several markets, there are also other possibilities. In this thesis, it was assessed from a Techno-Economic perspective how the performance and optimal design of the plants vary when providing different services extra to selling electricity to the grid. The conclusion was that even though the Net Present Value (NPV) achieved working on the spot market was already very high, the extra value added from participating in the secondary or tertiary markets was indisputable. Indeed, the profits attained in those markets were between two and four times higher than the ones of the spot market. This is a specific case, but a trend was identified: these hybrid power plants have a huge possibility and a bright future on the service markets. As a consequence, this thesis shows the huge potential of hybrid power plants integrated in the grid participating in several markets. It also lays the foundation for future studies in other locations, under different conditions and with different technologies, among others.

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.

Yes / In order to improve the performance of the reverse osmosis (RO) desalination plant powered by photovoltaic (PV), two cooling systems were proposed in this study to cool the PV and preheating the RO feed water as well. In the cooling design (1), the cooling fluid flows in direct contact with the back surface of the PV through channels of half circular cross-sections. While in the design (2), it flows through channels of squar cross-sections fixed on the PV back surface. Two nano-fluids were also tested as cooling fluid: H2O/CuO and H2O/Al2O3, in addition to distilled water for the purpose of comparison. The effect of changing the weight concentration of the nano-fluid (0.05, 0.1, and 0.15%) on the PV performance was also investigated. The results showed that the PV integrated with the cooling design (1) achieves better performance compared to design (2) at all studied cooling fluids. The improvements in the electric efficiency of the PV integrated with design (1) reached 39.5, 34.8 and 27.3 % when CuO and Al2O3 nano-fluids and distilled water were used as cooling fluid, respectively, compared to the uncooled PV. Based on the obtained experimental results, the PV integrated with design (1) was selected to power the RO with H2O/CuO nano-fluid of weight concentration 0.15% and flow rate 0.15 kg/s being used as the coolant. The RO powered by the improved PV was tested at different salinities of brackish water when the preheating technique was implemented. The results showed that the proposed PV-RO desalination system produces 366 l/day when brackish water of salinity 3000 ppm was used.

This thesis discusses the modeling of a micro-grid with photovoltaic (PV)-wind-fuel cell (FC) hybrid energy system and its operations. The system consists of the PV power, wind power, FC power, static var compensator (SVC) and an intelligent power controller. Wind and PV are primary power sources of the system, and an FC-electrolyzer combination is used as a backup and a long-term storage system. A simulation model for the micro-grid control of hybrid energy system has been developed using MATLAB/Simulink. A SVC was used to supply reactive power and regulate the voltage of the hybrid system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network-Sliding Mode Control (RBFNSM) and a General Regression Neural Network (GRNN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by RBFNSM, and the PV system uses GRNN, where the output signal is used to control the DC/DC boost converters to achieve the MPPT.

Cette thèse propose principalement, un algorithme État-Flow MPPT basé P&O, amélioré avec deux degrés de liberté, dans lequel le système événementiel (MPPT) de comportement est modélisé par le décrivant en terme de transition entre les états, sous certaines conditions. Secondairement, un algorithme étendu MPPT, base d'exploitation en parallèle de l'état-débit est en outre proposé d'être une solution difficile pour le contrôle indépendant du système hybride, où la caractéristique de contrôle continu peut se présenter au cours d'un certain état de travail tout en discrète, est indiquée le long des transitions d'état. Deux configurations possibles pour le système hybride sont proposées : deux convertisseurs DC/DC séparés, et un convertisseur de sortie unique à double entrée (DISO) de configurations. Enfin, il est proposé, un comportement du système DC modélisation utilisant État-Flow, menant à l'ensemble de la conception de la stratégie de commande qui concernent RES MPPT, RES et la coordination BESS, la stabilité du système d'alimentation et de régulation de la tension du bus DC. La simulation et les résultats expérimentaux valident l'efficacité et l'applicabilité de l'algorithme proposé. Les deux résultats montrent la supériorité du MPPT basé proposé État-Flow pour réduire les oscillations de puissance RESs à l'état d'équilibre dans diverses conditions d'exploitation, en plus de son démarrage plus rapide, et l’opération de transition sans divergence de la MPP, selon des conditions météorologiques variables. / This thesis primarily proposes, an improved P&O based State-Flow MPPT algorithm featuring two degree of freedom, in which the event driven system (MPPT) behaviour is modelled by describing it in terms of transitions among states under certain conditions. Secondarily, an extended parallel operating State-Flowbased MPPT algorithm is further proposed to be a challenging solution for the independent control of the hybrid system, where continuous control characteristic can present during a certain working state while discrete one is indicated along state transitions. Two possible configurations for the hybrid system are proposed; two separate DC/DC converters and dual input single output converter (DISO) configurations. Finally it is proposed, DC system behaviour modelling using State-Flow leading to the whole control strategy design which concern RESs MPPT, RESs and BESS coordination, power system stability and DC bus voltage regulation.Simulation and experimental results validate the effectiveness and applicability of the proposed algorithm, both results show the superiority of the proposed State-Flow based MPPT in reducing the RESs power oscillations at steady-state in various operating conditions in addition to its faster start-up and transition operation without divergence from the MPP during sudden varying weather conditions.

The global climate and energy crisis has amplified the need for renewable energy sources, withsolar photovoltaic (PV) systems expected to play a significant role in the future energy mix. In this context, distributed energy systems (DES) are identified as part of the solution to address climate and energy challenges.With the increasing demand for photovoltaic energy sources, there is a growing requirement forefficient Business Case Tools (BCT) to analyze investments in distributed solar PV installations.A two-part model, consisting of a solar model and spot price data, was developed based onparameters such as solar radiation, location, angle, orientation, system losses, installedcapacity, and historical spot price data. The model was integrated with Power BI for investment calculations and visualization of results. The developed model provides approximations for solar PV system electricity production, which were validated against selected installations in allelectricity areas of Sweden. The validation revealed an average relative absolute error of 14.72 percent for the model. The conclusion drawn is that BCT can be utilized to analyze and visualize solar PV investments at specific locations in Sweden. The results indicate that Power BI, as a BCT, has limitations indynamic data collection but performs well in executing calculation of investments and visualizingthe results. Well-developed BCT can facilitate decision-making through real-time calculations and contribute to smoother implementation of distributed systems by providing detailed insightsinto their financial characteristics. Further research is needed to develop a model specificallytailored for distributed installations with storage capabilities. / Världen befinner sig i en global klimat- och energikris vilket ökat behovet av och efterfrågan på förnybara energikällor. Solceller förväntas utgöra en betydande del av den framtida energimixen. I kombination med detta identifieras distribuerade energisystem (DES) som endel av lösningen på klimat- och energifrågan. I takt med den ökade efterfrågan på fotovoltaiska energikällor ställs större krav på effektiva Business Case Tools (BCT) för att analysera investeringar i distribuerade solcellsanläggningar. En modell bestående av två delar, en solmodell och spotprisdata,utvecklades utifrån parametrarna solstrålning, plats, vinkel, riktning, systemförluster, installerad effekt samt historiska spotprisdata. Modellen sammankopplas med Power BI föratt utföra investeringskalkyler och visualisera resultatet. Den utvecklade modellen gerapproximationer för solcellsanläggningars elproduktion, vilket validerades mot utvaldaanläggningar i Sveriges samtliga elområden. Enligt valideringen uppgår modellens genomsnittliga relativa absoluta fel till 14,72 procent. Slutsatsen dras att BCT kan användas för att analysera och visualisera solcellsinvesteringar på specifika platser i Sverige. Resultatet visar att Power BI som BCT har brister när detkommer till dynamisk datainsamling, men genomför och visualiserar investerings kalkyler med enkelhet. Välutvecklade BCT kan användas för att underlätta beslutsfattande genomrealtidsberäkningar och kan bidra till en smidigare implementering av distribuerade systemgenom att belysa deras finansiella karaktär på ett detaljerat sätt. Fortsatt forskning krävs föratt ta fram en modell anpassad för distribuerade anläggningar med lagringsmöjligheter.

<p dir="ltr">Transitioning to a carbon-neutral future will require a broad portfolio of green energy generation and storage solutions. With the abundant availability of solar radiation across the Earth’s surface, energy generation from photovoltaics (PVs) will be an important part of this green energy portfolio. While silicon-based solar cells currently dominate the PV market, temperatures exceeding 1000 °C are needed for purification of silicon, and batch processing of silicon wafers limits how rapidly Si-based PV can be deployed. Furthermore, silicon’s indirect band gap necessitates absorber layers to exceed 100 µm thick, limiting its applications to rigid substrates.</p><p dir="ltr">Solution processed thin-film solar cells may allow for the realization of continuous, high-throughput manufacturing of PV modules. Thin-film absorber materials have direct band gaps, allowing them to absorb light more efficiently, and thus, they can be as thin as a few hundred nanometers and can be deposited on flexible substrates. Solution deposition of these absorber materials utilizing molecular precursor-based inks could be done in a roll-to-roll format, drastically increasing the throughput of PV manufacturing, and reducing installation costs. In this dissertation, solution processed synthesis and the characterization of two emerging direct band gap absorber materials consisting of earth abundant elements is discussed: the enargite phase of Cu₃AsS₄ and the distorted perovskite phase of BaZrS₃.</p><p dir="ltr">The enargite phase of Cu₃AsS₄ (ENG) is an emerging PV material with a 1.42 eV band gap, making it an ideal single-junction absorber material for photovoltaic applications. Unfortunately, ENG-based PV devices have historically been shown to have low power conversion efficiencies, potentially due to defects in the material. A combined computational and experimental study was completed where DFT-based calculations from collaborators were used inform synthesis strategies to improve the defect properties of ENG utilizing new synthesis techniques, including silver alloying, to reduce the density of harmful defects.</p><p dir="ltr">Chalcogenide perovskites are viewed as a stable alternative to halide perovskites, with BaZrS₃ being the most widely studied. With a band gap of 1.8 eV, BaZrS₃ could be an excellent wide-bandgap partner for a silicon-based tandem solar cell.Historically, sputtering, and solid-state approaches have been used to synthesize chalcogenide perovskites, but these methods require synthesis temperatures exceeding 800 °C, making them incompatible with the glass substrates and rear-contact layers required to create a PV device. In this dissertation, these high synthesis temperatures are bypassed through the development of a solution-processed deposition technique.A unique chemistry was developed to create fully soluble molecular precursor inks consisting of alkaline earth metal dithiocarboxylates and transition metal dithiocarbamates for direct-to-substrate synthesis of BaZrS₃ and BaHfS₃ at temperatures below 600 °C.</p><p dir="ltr">However, many challenges must be overcome before chalcogenide perovskites can be used for the creation of photovoltaic devices including oxide and Ruddlesden-Popper secondary phases, isolated grain growth, and deep level defects. Nevertheless, the development of a moderate temperature solution-based synthesis route makes chalcogenide perovskite research accessible to labs which do not have high temperature furnaces or sputtering equipment, further increasing research interest in this quickly developing absorber material.</p>