Energy self-consumption is one of the strategies used for the optimization of renewable energy integration in electrical systems within a framework of sustainable energy policy development. Renewable energy self-consumption additionally contributes to the promotion of distributed generation. The aim of the present study is to develop a hybrid territorial planning model for the siting of areas suitable for the joint exploitation of wind and solar energy targeted principally at self-consumption. The methodology employed was based on the analytic hierarchy process (AHP) and geographical information systems (GIS), and the general area considered was the island of Gran Canaria (Spain). This island has an isolated electrical system. The case study involved locating areas close to populated settlements which are generally cut off from areas commonly marked out for large-scale wind and solar energy exploitation. The areas located with the model were differentiated according to the municipality they were in. The model that has been developed can be applied to any territory. The results obtained with the model can then be incorporated into territorial planning documents and/or national and regional and/or municipal files with the aim of optimizing the integration of renewable energy for self-consumption and advancing distributed electrical energy systems.

The scarcity of water resources on the island of Gran Canaria (Canary Islands, Spain) is such that 88% of the water supply for human consumption comes from seawater desalination plants. This type of process has a high specific energy consumption. Gran Canaria has an isolated electrical system of low robustness. In this paper, a geothermal plant is designed and integrated into a system that already has non-dispatchable renewable generation (wind and photovoltaic) in order to meet, based on a self-consumption regime, the energy demand of a high-capacity desalination plant. The aim is for the diversified renewable system to improve the stability and management of renewable electrical energy generation. Geothermal plant production can adapt to the energy balance between demand and non-dispatchable renewable generation. The geothermal plant’s design is based on an organic Rankine cycle and its resulting power is 4.16 MW. Its integration in the renewable generation system significantly improves the contribution of renewables in meeting the desalination plant’s energy demand and therefore reducing its dependency on the island’s electrical system. The mean cost of electrical energy generation with the diversified renewable system is 57.37 EUR/MWh, considerably lower than the mean cost of conventional generation on Gran Canaria of 153.9 EUR/MWh.

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Due to the low dispatchability of wind power, the massive integration of this energy source in electrical systems requires short-term and very short-term wind farm power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the prediction hour) chosen for the input layer parameters; and/or incorporating in the input layers data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecast horizon, the greater the degree of improvement.

Difficulties are commonly detected in students with respect to the acquisition of certain specific competencies in a particular topic. One strategy to optimize the assimilation of knowledge and improve the learning results of students in a specific topic is through the use of the active learning process. Active learning can serve to facilitate autonomous and collaborative learning in specific topics as a complement to in-person classes. In this chapter, a method to improve comprehension and learning is developed and applied, using for this purpose both autonomous and collaborative works. The case study presented is undertaken for one of the subjects in the area of systems engineering and automation in one of the public universities of Canary islands (Spain). Different specific topics of the subject were selected. To check the effect of the application of the proposed method, a statistical analysis was performed. For this objective, t-test and the p-value statistical were used. As results, it was found that 100% of the students who presented some difficulty in relation to the general subject obtained higher relative results in the specific topics that they worked on when employing the proposed method, compared with their global result in the subject.