In this work, a mathematical computer simulation model is used to predict the possible energy generated from different Waves Energy Converters (WECs) in the Canary Islands. The Monte Carlo Method is the computer simulation model proposed to predict the generated energy. The Waves Energy Converter systems analyzed in the study were, the Aqua Buoy, Wave Dragon and Pelamis converters. The models were implemented and validated, with the dataset of Gran Canaria deep water buoy. This buoy belongs to a network of buoys belonging to Spain’s State Ports and they cover a dataset period of 22 years. The research has concluded that it is possible to affirm that the achieved model is a strong tool to compute the possible energy of any WECs, when the power matrix is known. The model based on the Monte Carlo simulation can be used in isolated islands of the Atlantic Ocean and can be extrapolated to other regions with the same characteristics.

In this research, a renewable energy hybrid system (PV-Wind) is modeled to compare different design options based on their economic and technical features. The energy requirements of a Reversible Osmosis desalination plant located on the island of Tenerife with a water production capacity of up to 20,000 m³/day was considered. The system is connected to the electricity grid. The HOMER software, version 2.75 was used to produce optimum strategies for renewable energy. The assumptions input into the model were: the technical specifications of the devices, electricity demand of the desalination plant, as well as the solar radiation and the wind speed potentials. Numerous arrangements were considered by the software, version 2.75. The optimal results were obtained based on the use of renewable energy. The data used in the study were recorded in Tenerife in the Canary Islands. The experience of this research could be transferred to other Atlantic islands with similar renewable energy sources (specifically the wind) and water scarce conditions.

Fuel storage facilities concentrate large inventories of flammable substances, so that a single primary accident may escalate, through thermal radiation, into a cascading sequence involving several tanks (domino effect). This work presents the probabilistic core of a digital twin for such facilities, understood as a probabilistic replica of the facility with a fast query interface (live-data assimilation is not yet implemented): a permanently available, interactively queryable model that characterises, in probabilistic terms, the thermal radiation field generated by accident scenarios in a storage facility and derives from it an integrated assessment of its safety and economic risk. The framework combines (i) a Monte Carlo simulation of ${10}^8$ facility-years, in which primary accidents and their fire-driven escalation are sampled using validated models of pool fires, thermal radiation and equipment failure; (ii) a point-wise condensation of the resulting radiation intensity into Gamma distributions fitted by maximum likelihood, whose parameter maps embody the frequency of every fire configuration; and (iii) a machine-learning surrogate, based on Generalized Regression Neural Networks, that renders the probabilistic field continuous over the terrain and evaluable in a fraction of a millisecond. On this backbone, the model delivers mutually consistent, receptor-level indicators: annual exceedance frequencies of harm thresholds (contour maps), the expected number of fatalities, distinguishing sheltered and outdoor population, and the Expected Annual Loss of every asset. In a demonstration on a five-tank facility storing light naphtha, jet fuel and diesel within a mixed industrial–residential complex, 16.5% of the accidents escalated to multiple tanks, the individual risk at the most exposed third-party location lay more than one order of magnitude below the ${10}^{-6}$ per year land-use criterion, and 97.5% of the expected losses were borne by the facility itself. The scope is limited to pool fires in fixed-roof atmospheric tanks, in wind-free conditions and without credited safety barriers.