Solar photovoltaic (PV) energy is variable. The output power can change considerably in a matter of minutes, imposing challenges on the control of systems connected downstream. The power from these systems can be smoothed using electric storage, potentially increasing the system cost. An alternative is to deliberately curtail the power before it starts to change. This strategy relies on ultra-short-term forecasting to determine the curtailment point. Unfortunately, forecasting is prone to errors and high uncertainty even in the very short-term, leading to control errors. We propose an active power curtailment control strategy for a stand-alone solar photovoltaic system powering an electrolyzer. Our work's novelty relies on the controller's ability to deal with large forecasting errors and high uncertainty, combining artificial intelligence for predicting the power ramps and fuzzy logic to account for imperfect prediction. We validated our approach using a hardware emulator of the photovoltaic system, power converter and electrolyzer. Under clear sky conditions, the curtailment results in unnecessary energy loss, while under variable irradiance, the controller successfully smooths the power ramps within 10% of the PV system's nominal power. Although our approach was designed for a stand-alone system, its concept can be directly applied to grid-connected systems as well.

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Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems.

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Hydrogen is not only the most common element in the universe, but also a versatile molecule which is an option to decarbonize sectors that are not easily electrified or are energy intensive, such as long-distance transport, chemical industry, and metallurgy among others. Hydrogen gas is typically obtained from fossil fuels, and only a small fraction is produced nowadays from electrolysis, or the process of using water and electricity to produce gaseous hydrogen.
If the electricity for powering the electrolysis process comes from renewable sources, the produced gas will have no associated greenhouse emissions. This is the so-called green hydrogen, which is the base for decarbonization of carbon intensive industries. This work investigates the potential of stand-alone green hydrogen production from solar energy, covering the whole design process, from an allocation and feasibility analysis, to system control. To do so, this thesis is separated in two parts. The first part focuses on the preliminary assessment phase of photovoltaic (PV) systems and the solar resource, while the second covers the integration of PV and electrolysis systems finalizing with a control strategy for these systems.
Chapter 2 presents a methodology for analyzing potential sites for PV deployment, including information on the degradation of the site. This provides the designer with additional information beyond the purely technical and economical layers that are typically considered in this type of study. The more degraded a site is, the more suitable it is for deploying new PV projects, avoiding pristine natural areas. This, combined with mitigation measures can minimize the environmental impact of new PV projects.
An analysis of the efficiency loss of PV systems is discussed in Chapter 3. In particular, the efficiency loss caused exclusively by quick variations in irradiance, as a consequence of passing clouds. These abrupt and quick changes affect not only the solar modules, but components downstream, such as the maximum power point tracker. The implemented algorithm might be sensitive to these changes and move the operating point of the PV module away from its maximum power point, leading to energy loss.
Predicting quick changes of irradiance is a topic covered in Chapter 4. Using sky images and artificial intelligence, it is possible to predict ultra-short-term irradiance. The proposed method is an ensemble of models, each trained on a particular sky condition. Because each model is highly specialized, once the sky condition is determined, the model that performs best on each sky type is employed, leading to lower prediction errors, more precise predictions and lower training data needed. Yet, an accurate prediction is a topic for further research.
The integration of PV with hydrogen systems is introduced in Chapter 5, which presents a literature review on integration methods for PV and electrolyzers as well as the main challenges for operating these systems in a variable manner.
Moving to the design phase, Chapter 6 proposes a sizing procedure, based on Particle Swarm Optimization to minimize the energy that cannot be used by the hydrogen equipment (electrolyzer and compressor), aiming at the maximum energy utilization in the system. Horizontally-placed PV modules provide a good compromise between efficiency, hydrogen production and cost.
Once the system has been designed, Chapter 7 puts together all the topics covered in this dissertation proposing a control strategy for an optimally-sized stand-alone PV electrolyzer systems, without electrical storage. The control is based on prediction of irradaince changes using sky-images. From Chapter 4 it was clear that the prediction using sky images is far from perfect, yet this is needed for control. To solve this problem, the strategy proposed in Chapter 7 relies on information on the uncertainty of the prediction and uses fuzzy logic to account for imperfect predictions. This strategy can effectively smooth power changes without the need of additional storage components.@en

This review provides a comprehensive overview of the dynamics of low-temperature water electrolyzers and their influence on coupling the three major technologies, alkaline, Proton Exchange Membrane (PEM) and, Anion Exchange Membrane (AEM) with photovoltaic (PV) systems. Hydrogen technology is experiencing considerable interest as a way to accelerate the energy transition. With no associated CO2 emissions and fast response, water electrolyzers are an attractive option for producing green hydrogen on an industrial scale. This can be seen by the ambitious goals and large-scale projects being announced for hydrogen, especially with solar energy dedicated entirely to drive the process. The electrical response of water electrolyzers is extremely fast, making the slower variables, such as temperature and pressure, the limiting factors for variable operation typically associated with PV-powered electrolysis systems. The practical solar-to-hydrogen efficiency of these systems is in the range of 10% even with a very high coupling factor exceeding 99% for directly coupled systems. The solar-to-hydrogen efficiency can be boosted with a battery, potentially sacrificing the cost. The intermittency of solar irradiance, rather than its variability is the biggest challenge for PV-hydrogen systems regarding operation and degradation.@en

The development of clean hydrogen and photovoltaic (PV) systems is lagging behind the goals set in the Net Zero Emissions scenario of the International Energy Agency. For this reason, efficient hydrogen production systems powered from renewable energy need to be deployed faster. This work presents an optimization procedure for a stand-alone, fully PV-powered alkaline electrolysis system. The approach is based on the Particle Swarm Optimization algorithm to obtain the best configuration of the PV plant that powers the electrolyzer and its compressor. The best configuration is determined with one of three indicators: cost, efficiency, or wasted energy. The PV plant needs to be oversized 2.63 times with respect to the electrolyzer to obtain minimum cost, while for high efficiency, this number increases by 2%. Additionally, the configuration that minimizes cost, wasted energy or maximizes efficiency does not correspond to the configuration that maximizes the annual PV yield. Optimizing for cost results also leads to the best operation of the electrolyzer at partial loads than optimizing for efficiency or wasted energy.

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Irradiance variability is one of the main challenges for using photovoltaic energy. This variability affects the operation of maximum power point trackers (MPPT) causing energy losses. The logic of the Perturb-and-Observe MPPT algorithm is particularly sensitive to quick irradiance changes. We quantified the existing relation between irradiance variations and efficiency loss of the logic of the Perturb-and-Observe MPPT algorithm, along with the sensitivity of the MPPT to its control parameters. If the algorithm parameters are not tuned properly, its efficiency will drop to nearly 2%. Irradiance variability causes a systematic energy loss of the algorithm that can only be quantified by ignoring the hardware components. With this, we aim to improve the energy yield estimation by providing an additional efficiency loss to be considered in the calculations.@en