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The Canary Islands are still largely dependent on expensive imported fossil fuels, are stressed by the increasing touristic impact and are extremely vulnerable to climate change due to water scarcity. Water desalinisation is an energy-demanding process and is essential to the sustainable development of these islands. The aim of this study is to explore the potential advantages of a hybrid installation, exploiting two different renewable energy sources, specifically waves and solar, to supply a large desalination plant in Tenerife. The paper ultimately provides a generally applicable procedure for the design of hybrid installations, including three steps: the assessment of available renewable energy sources, the optimal combination of these sources and finally the economic assessment. The wave and solar resources are assessed first, then the hybrid installation is conceptually designed, proposing a criterion for the optimal mixing of the renewable energy sources that can be applied to other resources and other sites. The basic idea is to maximise the exploitation of the renewable power, minimizing the need of the fossil-based back-up system. The costs of the hybrid installation are finally assessed considering the sensitivity to government incentives, showing that the project parity point is reached within the lifetime of typical desalination plants (i.e. 40 years) and can be significantly more attractive in case of Feed-In-Tariffs available in other European countries. [ABSTRACT FROM AUTHOR]

Tourism represents a significant portion of the total economic activity of many islands throughout the world. This high level of tourist activity has a substantial impact on the energy and water demand. Previous research into smart energy systems on islands has demonstrated that there is significant potential for hotels to partake in energy demand shifting. However, the total potential of such shifting is influenced by variables such as air temperature and the level of occupancy of the hotels. The objective of this work is to quantify the sensitivity of these parameters and their impact on the overall effectiveness of demand shifting within hotels. The assessment contained within this paper utilises Mixed Integer Linear Programming to determine the dispatch of supply and demand on a case study hotel in the Canary Islands. The results show that highest reduction of fossil fuels is reached in a fully electrified hotel energy system. The potential is assumed as independent from weather and guests' behaviour. If only PV is used, demand shifting in only one hotel leads to a relative change of the degree of self-sufficiency of 1.6–1.8% but the results visualise a saturation effect for an already high share of renewable energies. • Demand response potential of touristic facilities through coupling electricity and thermal energy systems is shown. • Probabilistic time-series are used to quantify expected mean and deviation. • Both, integration of a trigeneration systems and a fully electrified energy system show highest potentials. • Saturation effect of demand response potential for high shares of renewable energy is observed. [ABSTRACT FROM AUTHOR]

This paper analyses the influence of the variation of meteorological and operational parameters on estimation of the power output of a dispatchable wind farm (WF). The active power set-points (APSPs), established to regulate the wind farm power output (WFPO), are one of the analysed parameters. The WF considered as case study is integrated in the Gorona del Viento wind-hydro power plant (El Hierro-Canary Islands-Spain), which supplies the primary energy demand of the island. Statistical inference between the errors generated by different WFPO estimation models, each fed with different input features, is performed. These models are based on supervised machine learning (ML) regression algorithms, namely support vector regression, random forest, and a combination of the strengths of these two base learning algorithms constructed using stacked regression ensemble techniques. From the results obtained, the following conclusions are drawn: a) There is a marked difference between the errors obtained with the model that only considers wind speed and direction and that which additionally incorporates the APSP parameter, showing the importance of considering this particular parameter; b) the model which incorporates air density and turbulence intensity in addition to the APSP improves the values of all the metrics, independently of the ML technique employed. • The active power set-point (APSP) parameter is very important in WFPO estimation. • Air density, turbulence intensity and APSP parameters improve WFPO estimation. • Eight models are compared for wind farm power output (WFPO) estimation. • Three machine learning techniques implemented in the models are analysed. • Non-parametric statistical inference is used to compare the models. [ABSTRACT FROM AUTHOR]