The application of marine current turbines for electricity generation could offer a distinct advantage over other renewable energy sources due to the regular and predictable nature of this resource. This paper details the application of Analytical Hierarchy Process (AHP) as a possible tool for decision makers to better understand the environment and the impacts of the marine current turbines. The best areas for generating energy from the currents were found using a tridimensional model (TELEMAC3D). In addition to applying the energy conversion module, these regions were also evaluated for energy production, which was then applied to the AHP. Several databases (Transmission and Transport, Socioeconomic, Conservation Units, Endangered Species and Geological Information) were compared to minimize decision deviation. The results showed the viability of the northern region of the Southern Brazilian Shelf (SBS) as a possible area to harvest energy from the currents, as much of the studied region was limited by human activities in the coastal zone and sensitive biological resources.
The continuous growth of the world population increases the demand and competition for energy, requiring an immense effort for nonrenewable energy sources availability. Therefore, in addition to promoting the development of new technologies, global policies for the generation of renewable and clean energy are being strengthened. Several methods of energy conversion have been developed over the years, especially the turbine-based current energy converters. These converters have demonstrated high energy generation capacity and are already in operation.
The policies for the installation of Marine Current Turbines (MCT) were compiled by examining a wide range of literature related to sustainability [
These important parameters were considered in other studies in the USA [
In Brazil, no coastal zones have been mapped to determine the energetic potential viable for conversion using hydrokinetic turbines. Recent studies showed two areas (
The Southern Brazilian Shelf (SBS), located between 28˚S and 35˚S (
The region is located near the Brazil-Malvinas Confluence zone and is known for high spatial and temporal variability and also for the convergence of several water masses [
The high seasonality of the wind fields [
The methodology of this work was based on the usage of a tridimensional numeric model (TELEMAC3D) to forecast energy results. A multi-criteria analysis was applied to the sustainability parameters.
The TELEMAC system, developed by the Laboratoire National d Hydraulique Environnement of the Company Eletricit de France (CEDF), was used for the hydrodynamic simulations. The TELEMAC3D model solves the Navier-Stokes equations by considering local variations in the free surface of the fluid, neglecting density variations in the mass conservation equation and considering the hydrostatic pressure and Boussinesq approximations. The model is based on finite element techniques to solve the hydrodynamic equation [
A time step of 90 s and a Coriolis coefficient of −7.70 × 10−5 rad·s−1 (at 32˚S) were used in all of the simulations. The horizontal turbulence process was performed using the Smagorinsky model. This closure turbulent model is generally used for maritime domains with large-scale eddy phenomena. It calculates the mixing coefficient by considering the size of the mesh elements and the velocity field [
The mixing length model for buoyant jets assessed the vertical turbulence processes and provided a better representation of the stratification. This model considers density effects via a damping factor that depends on the Richardson number to calculate the vertical diffusion coefficients.
The power of the ocean currents can be transformed using converters. Similar to the technology of wind converters, a submerged rotor is forced to rotate by the fluid surrounding it. A recent evaluation of the equipment available to capture hydrokinetic energy found that 76 pieces of equipment, including turbines, were in operation or were in the early stages of research [
The hydrodynamic simulations used in this study were produced using the TELEMAC3D model. The investigations involving energy conversion from the currents into electrical power were performed with the energy module [
Parameters | Value |
---|---|
Start-In Speed | 0.2 m/s |
Cut-In Speed | 1.5 m/s |
Efficiency Coefficient | 0.35 |
Nominal Power | 170 kW |
Turbine Height | 14 m |
Turbine Ray | 10 m |
The water body boundaries used in this study were the Guaíba River, the Camaquã a River and the São Gonçalo Channel (
The oceanic boundary was forced by the astronomical tides, water levels, current velocity, salinity and temperature fields. The salinity and temperature fields used as the initial conditions were obtained from the Ocean Circulation and Climate Advanced Modeling Project2 (OCCAM).
The numerical model was initialized from the remaining parameters with a water level of 0.75 m, the approx- imate average tide in the region [
The study was based on a two-year simulation (1998 and 1999). The first year represented anomalous conditions due to the ENSO influence, with moderate to high discharges occurring over the entire year. The second year represented normal conditions, when the freshwater discharge of the Patos Lagoon followed its natural pattern.
Monteiro et al. [
The results of these calibration and validation tests indicated that the TELEMAC3D model can be used for studies of the SBS with an acceptable degree of accuracy. As a result, the values for many physical parameters (such as the wind influence coefficient, friction coefficient and turbulence models) were available and were used to conduct this study.
The AHP is widely used for multi-criteria analysis [
The assigned preference values are synthesized to determine a ranking of the relevant factors in terms of a numerical value which is equivalent to the weights of the factors, where a value of 1 expressed equal importance
and a value of 9 was given to those factors that had an extreme importance over another factor. Therefore the eigenvalues and eigenvectors of the square preference matrix are calculated, revealing important details about patterns in the data matrix.
After the preference matrix is performed, each pair-wise match receives a consistency index, which is directly calculated from the matrix with the Equation (2), where λmax is the greatest eigenvalue of the preference matrix, while n is the order of the matrix.
Therefore [
This methodology was applied using the Geographic Information System (GIS) Software, which is a tool for geoprocessing information with high liability. The criteria/factors were divided into 3 themes called: 1) Positive Factors; 2) Negative Factors; and 3) Restraining Factors. Each group was filled with information from Internet geodatabases, such as ANEEL4, IBGE5, MMA6, IBAMA7, DHN8, GISMAPS9 and results from the hydrodynamic simulation from TELEMAC3D.
This information and their scale values are shown in
Theme | Classes | Subclasses | Weight |
---|---|---|---|
Positive | Energy production | 0.3000 | |
Bathymetric data | 0.7000 | ||
Negative | Transmission and | Submarine Cables | 0.1267 |
Transport (n = 6) | Substations | 0.2336 | |
(CR = 0.0589) | Harbors | 0.3038 | |
Electric Transmission | 0.1333 | ||
Roads | 0.0706 | ||
Distance coast-sea | 0.1320 | ||
Sediment data | Silt | 0.0783 | |
(CR = 0.0446) | Sand | 0.2484 | |
Gravel | 0.5263 | ||
Clay | 0.1021 | ||
Mud | 0.0449 | ||
Geological data | Dredge Discharge zones | * | |
(CR = 0.0658) | Paleontological sites | * | |
Archaeological sites | * | ||
Socioeconomic | Shores | 0.0650 | |
(CR = 0.0173) | Economic Fishing zone | 0.1095 | |
Geo-parks | 0.0431 | ||
Population | 0.2634 | ||
Urban area | 0.5189 | ||
Restraining | Conservation Units | * | |
Endangered Species | Turtles | 0.2952 | |
(CR = 0.0672) | Mammals | 0.1422 | |
Benthic | 0.3775 | ||
Birds | 0.0524 | ||
Elasmobranches | 0.0916 | ||
Fish | 0.0411 |
*: Classes with an asterisk were used to create a mask to identify the study track.
formed in a Conservation Unit, paleontological or archaeological sites. In addition, dredge discharge zones were included as a non-suitable spot for the study due to the constant dredging activities in the Rio Grande Harbor, turning these zones inappropriate due to navigation and sediment release. These factors (shown in
The possible installment locations of the turbines are highlighted in
In these regions, the average velocity of the current (
The isobaths were closer in the northern region (
This area was characterized by complex topography, with the 50 m isobaths (the limit of the inner shelf) closer to the shoreline, where there was a high gradient. The gradient was reduced towards the Conceição Lighthouse, where this isobath moves away from the shore. Thus, the circulation pattern was modified when a current flowed from the region (with the 50 m isobaths near the coast) to where the coastal flow was more intense and concentrated in a shallower region near the 20 m isobath. The flow slowed down and spread out as this process occurred. Intensification occurred in the northern region when the current at the 20 m isobath encountered a topographic strangulation, diverting the current to the 30 m isobath and increasing its velocity.
The southern region (
The southern region had a complex bathymetry with significant linear banks, such as the Bank of Albardão, and a large depression (the Albardão Pit at a 75 m depth). The Bank of Albardão acts as a barrier to the coastal current (directed by the 20 m isobaths) causing the flow to diverge. Consequently, the meandering of the current intensified, suggesting that the intensification of power in this region was strongly influenced by the irregular topography.
This region relies on its oceanographic features, while the northern region was highly dominated by a strong vertical gradient which generated squeezing and stretching currents, enhancing the power generation. The south- ern region was forced by a topographic feature (e.g., the Bank of Albardão) that forced the flow to diverge, and, as a consequence, the meandering of the current intensified, increasing the power availability in this region.
The suitable installation areas were determined by the level of power density, the ease of accessibility and the number of environmental conflicts with the site selection methodology. Although the weight of each factor depends on the interference of the stakeholder, the AHP method relies on a very advanced consistency method to avoid misleading decisions.
The AHP was applied on each subclass and gathered them as a new class (their Consistency Ratio is shown on
The positive factors were formed by gathering the energy information from the hydrodynamic model and the bathymetric data (especially the data above 50 m, see
The relative importance of location factors is also changing as the decision process stages proceed [
When reaching the stage of selecting specific locations, site-specific factors such as grain size, endangered species or access to roads and power cables may dominate over another location. In the final stage of evaluating a few selected factors may escalate in favor of another factor, or even, decrease the importance of other factors.
All of the shapes showed the study track of this work, which meant that the suitable area had to be inside of these lines. This mask was made by applying all of the restraining factors, as the incidence of endangered species and Conservation Units inhibited the possibility of using any area. It is feasible to compare the possible and less possible areas according to the blue and red colors in the charts, respectively. The transmission and transport shape (
The sediment data (
to its importance for the installation process. The sand had a weight of 24%. The socioeconomic shape (
We achieved the shapes of the positive and negative factors by integrating the prior results on themes. The positive factors (
The negative factors (
A final AHP was created by using all of the shapes.
In the SBS, two regions were suitable for the installation of current energy converters. The northern region had the highest potential power, where a single converter could generate an average of 10 kW/day and could reach an integrated power conversion of 3.5 MW/year. The southern region had less potential power, generating an average of 3.5 kW/day and integrated values of 1.5 MW/year. Both regions had intense oceanographic features that dominated the current velocities.
The suitable sites for tidal power conversion were marked and evaluated based on three major themes: Negative factors; positive factors; and restraining factors. The TELEMAC3D results were evaluated against all of the socioeconomic, geological and environmental results available. The study aimed for an environmentally friendly result between these variables.
The study showed that the depth constraints, human activities in the coastal zone and the sensitive biological resources limited the amount of suitable locations for a marine current turbine facility. Due to the relatively strong energy conversion in the northern region, this area emerged as a possible place for a turbine. The AHP proved to be a powerful asset for selecting suitable locations.
Despite the results found in this work, the AHP can serve as a useful preliminary analysis tool for decision makers before allocating resources for a more detailed evaluation. Further studies should apply more data into the analysis to achieve more precise results.
The authors are grateful to the Agência Nacional do Petróleo-ANP and Petrobras for the fellowships regarding the Programa de Recursos Humanos (PRH-27) that provided bursaries, the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) for sponsoring this research under contract: 1799123 and to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under contracts: 456292/2013-6 and 305885/2013-8. Further acknowledgments go to the Brazilian Navy for providing detailed bathymetric data for the coastal area; the Brazilian National Water Agency and NOAA for supplying the fluvial discharge and wind data sets, respectively; and to the open TELEMAC-MASCARET (www.opentelemac.org) for providing the academic license of the TELEMAC system to accomplish this research. Although some data were taken from governmental databases, this paper is not necessarily representative of the views of the government.