• Energy Research

This paper presents the economic feasibility analysis of a 2 MW Ocean Thermal Energy Conversion (OTEC) power plant in the open cycle. The plant can supply 6.35% of the average annual consumption of the electricity demand located at San Andrés Island (Colombia). On the one hand, the work presents the selection of the place to locate an offshore facility considering the technical viability while, on the other hand, the economic viability analysis is performed. The latter considers two scenarios: one without desalinated water production and another one with desalinated water. In this way, it is intended to first determine its construction’s technical requirements to analyse its economic performance. This approach allows us to have a general idea of the implementation costs and the benefits obtained with this type of plant, for the particular case of San Andrés, an island in the Colombian Caribbean with sustained stress on electricity production and freshwater generation. The results obtained show that the technology is viable and that the investment can be recovered in an adequate time horizon.

Research into renewable energy is an active field of research, with photovoltaic and wind being the most representative technologies. A promising renewable energy source is Ocean Thermal Energy Conversion (OTEC), based on the temperature gradient of seawater. This technology has two contradictory features, as its efficiency is relatively low while, on the other hand, its energy source is almost unlimited. OTEC research has focused on optimizing energy extraction, with different techniques having been used for this purpose. This article presents a review of the advances and applications of OTEC technology around the world. Throughout the document, the different uses of deep seawater are analyzed; further, the current systems which generate energy through the marine temperature gradient are reviewed, and the main advantages and disadvantages of each method are highlighted. The technical operations, construction variations, and the projects that have been developed around the world and those which are in the planning phase are also detailed. The two main conclusions are that this technology is still under development, but it is quite promising, especially for regions with little access to drinking water. Second, given the high implementation costs and low conversion efficiency, the development of this technology must be sponsored by governments.

This article presents the creation of bathymetric cartography for San Andrés, Providencia, Santa Catalina, and the Colombian Caribbean to find suitable areas for implementing an ocean thermal energy conversion (OTEC) system. The methodology to obtain the bathymetry uses data collection, digitization, and interpolation. The data are processed using the Surfer® software; in this way, the digital terrain model (DTM) maps are generated for each sector. The objective is to find the minimum distance to reach a depth of 1000 m since this is the distance where a temperature difference of 20 ∘C is obtained. Thus, it is possible to identify suitable areas to locate an onshore or offshore plant in Santa Marta, Barranquilla, southwestern and northwestern San Andrés, the western part of Santa Catalina, and the southwestern part of Providencia.

Risk management represents a challenge for organizations, as it includes environmental, social, and governance (ESG) issues that can negatively impact organizations’ investments. This article shows a general approach for prioritizing organizational risks focused on sustainability, which is applied in a particular case. Based on the analysis of global reports such as the “Global Sustainable Development Report”, “Enterprise Risk Management-Integrating with Strategy and Performance”, and the “Global Risk Report”, five typologies of organizational risks with a focus on sustainability (geopolitical, economic, social, technological, and environmental) that support the concern for sustainability in organizations are characterized, taking into account viability and equitability. Additionally, some sub-risks are proposed for each characterized typology of risk. Subsequently, the application of paired surveys assigned to a group of experts formed by executives from the service sector, auditing and consulting firms, the oil and gas sector, the manufacturing sector, and the financial sector is carried out; the responses obtained are consolidated and used in this study as input for the application of DEMATEL and AHP methods to prioritize risks and sub-risks, respectively. The result obtained via the DEMATEL method is the following risk prioritization: (1) economic, (2) geopolitical, (3) social, (4) technological, and (5) environmental. Using the AHP method, the sustainability sub-risks with the highest level of prioritization for each typology of risk are (1) massive data fraud or theft incident (technological risk), (2) deficit in economic growth (economic risk), (3) water depletion (environmental risk), (4) lack of ethics in the conduct of business (geopolitical risk), and (5) chemical safety (social risk). The sensitivity analysis presents positive and negative values, indicating that the positive results do not generate substantial changes between the characterized sub-risks. On the other hand, the negative results indicate a notable decrease in the relative importance of the sub-risks. It is crucial to highlight that the observed variations remain within realistic limits and reflect the uncertainty inherent in decision-making in a dynamic environment.

This paper deals with the optimization of maximum power point tracking when a photovoltaic panel is modelled as two diodes. The adopted control is implemented using a sliding mode control (SMC) and the optimization is implemented using an improved Pattern Search Method. Thus, the problem of maximum power point tracking is reduced to an optimization problem whose solution is implemented by Pattern Search Techniques, inheriting their convergence properties. Simulation examples show the effectiveness of the proposed technique in practice, being able to deal with different radiations. In addition, improved pattern search method (IPSM) is compared with other techniques such as perturb & observe and Particle Swarm optimization, after which IPSM presents lower energy losses in comparison with the other two algorithms, with the advantage of ensuring the location of the optimal power point in all cases.

En este artículo se propone el uso de un algoritmo de optimización basado en la búsqueda de patrones mejorada, aplicándolo a la estimación de los parámetros modelo de un diodo de una célula fotovoltaica. Los parámetros a estimar son corriente fotovoltaica, la saturación de corriente del diodo, la resistencia serie, la resistencia en paralelo y el factor de idealidad del diodo. La estimación es hecha a partir de los datos obtenidos de una curva conocida, es decir, que la celda fotovoltaica puede caracterizarse y los parámetros son extraídos de los datos de la curva. Los resultados son la identificación de los parámetros y la exactitud del modelo con respecto a la referencia en el punto de máxima potencia (MPP). Adicionalmente se realiza una comparación con el modelo obtenido con tres estimaciones realizadas con el algoritmo de optimización enjambre de partículas (PSO), bajo las mismas condiciones en cantidad de partículas y de iteraciones. El error encontrado muestra la similitud del modelo con la referencia obtenida de acuerdo con el algoritmo IPSM.