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This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

• Estimation of parameter distribution functions of nuclear thermal hydraulic correlations by inverse Bayesian methods. • Montecarlo Markov Chain Methods and the Gibbs sampler applied to inverse calibration methods. • Obtaining a correlation for steam jet penetration in subcooled pool by Bayesian methods. • Study of the influence of the number of parameters of a thermal hydraulic correlation in the convergence of the Markov Chains. • Influence of the noise level on the parameter estimation of nuclear thermal hydraulic correlations. This paper applies Bayesian methods to obtain the joint probability distribution function (PDF) of the unknown parameters of some of the correlation types used in thermal hydraulics, which are conditioned to a set of results of separate effect experiments. The problem is solved by Markov chain Monte Carlo methods using the Gibbs sampler. In this paper it is shown how to obtain the conditional PDFs for each one of the parameters, which define two typical correlations that appear in thermal–hydraulic problems, i) the heat transfer in the walls of a pipe and ii) the penetration length of steam discharged in a subcooled pool. The equations for the conditional probabilities of each one of the parameters are deduced and then used to build the Gibbs sampler which is then programmed in R. The PDFs are obtained for each one of the parameters and from these the maximum a posteriori of each one of the parameters is obtained. The new correlations obtained in this way are compared with the experimental data and show satisfactory performance for the case of the penetration of steam in a subcooled pool. Also, we study two cases one with a small number of experimental data and another one with a large number, it is observed that if the number of parameters is small the Markov chain converges fast to the region of high probability of the parameters for both cases with a small number of data and with large number of data, but if the number of parameters increases then the convergence for the case with small number of data becomes poor, however the convergence continuous being very fast for the case with large number of experimental data. [ABSTRACT FROM AUTHOR]

Fossil fuel-based economies must undergo a deep transition for complete decarbonization. To this end, it is widely recognized that all economies must move towards the electrification of energy end uses. Even though there is a part that cannot be electrified, at least at affordable costs; clear examples are heavy road transport, maritime and air transport, and some industrial processes. As a result of the limitations of electrification in certain energy end uses, the potential use of hydrogen as an alternative energy carrier has been examined in recent decades. Hydrogen is seen as a viable option for the medium to long term. Specifically, efforts are being made in the case of the Canary Islands to move towards a carbon-free energy mix. In fact, the aim is to advance the economy decarbonization by 10 years over the date foreseen for both Europe and the rest of Spain. To this end, the HOMER program has been used to analyze the possibility of producing the necessary hydrogen in a scenario applied to the Canary Islands in which the uses of this energy vector have been maximized. This research considers two possible scenarios, together with a sensitivity analysis under the different uncertain conditions associated with the used technologies. The results have been the estimation of hydrogen production costs for high demands, about 230,000 tH 2 /year, in 2040 for an isolated archipelago under two totally different generation scenarios. The first scenario is totally renewable, while the other is based on nuclear generation by means of high-temperature SMRs. The results show, that it would be possible to produce at a cost in the range of 1 €/kgH 2 using nuclear technology and around 4 €/kgH 2 using renewables, with uncertainty cost ranges of around 40%, i.e. costs between 0.85-1.48 and 3.29–4.99 € kgH 2 respectively. [ABSTRACT FROM AUTHOR]

The extensive use of greenhouse gas-free energy sources is essential to achieve net zero emissions targets in electricity generation by mid-century; if such sources also increase the economic competitiveness by producing cheaper energy, the outlook is even better. With this purpose, the current study presents the combined use of the NuScale design, a promising type of small modular reactor (SMR), along with renewable sources and storage technologies. The widespread use of highly variable generation sources, such as wind and solar PV, poses significant challenges in trying to match electricity generation and demand. Therefore, more reliable generation sources and/or storage technologies combined with these highly volatile sources are necessary to meet the demand with guarantees and affordable costs. This issue is even more pronounced in isolated regions, such as islands, where at least one more reliable generation source and/or substantial energy storage capacity is required. The system, in many cases, is autonomous and needs to be 100% self-sufficient. Furthermore, to achieve the future goal of zero greenhouse gas emissions, it is necessary to eliminate fossil fuels in all areas, particularly in power generation, which significantly contributes to the total greenhouse gas emissions. Therefore, developing techniques to assess the feasibility of the combined use of different carbon-free technologies is required. Using renewable energies with nuclear energy coupled with storage technologies is a very good possibility to achieve these objectives. It was, in all likelihood, the best option in the case of isolated locations where the system must be self-sufficient precisely because of its isolation. The use of nuclear energy is a key part of the analysis if there is no reliable energy capable of covering approximately the off-peak demand, the rest of the systems will have to be greatly oversized, i.e., the power to be installed from renewables and/or storage will be unaffordable. Therefore, it is very useful to study these problems, especially in the case of islands. Specifically, this analysis has been applied to the Island of Grand Canary in Spain. This island has about 1500 km2 and nearly one million inhabitants, with a off-peak electricity demand of about 250 MW and a peak demand value of about 500 MW. The software HOMER was used to analyze and compare different alternatives, estimating the best combination to get the lower Levelized Cost Of Energy (LCOE). The system's total initial investment cost is 1968 M€, the LCOE is 7.8 c€/kWh (25 years), and the payback is around 6.4 years. [ABSTRACT FROM AUTHOR]