Breaking-down and parameterising wave energy converter costs using the CapEx and similitude methods

Wave energy converters (WECs) can play a significant role in the transition towards a more renewable-based energy mix as stable and unlimited energy resources. Financial analysis of these projects requires WECs cost and WEC capital expenditure (CapEx) information. However, (i) cost information is often limited due to confidentiality and (ii) the wave energy field lacks flexible methods for cost breakdown and parameterisation, whereas they are needed for rapid and optimised WEC configuration and worldwide site pairing. This study takes advantage of the information provided by Wavepiston to compare different costing methods. The work assesses the Froude-Law-similarities-based “Similitude method” for cost-scaling and introduces the more flexible and generic “CapEx method” divided into three steps: (1) distinguishing WEC’s elements from the wave energy farm (WEF)’s; (2) defining the parameters characterising the WECs, WEFs, and site locations; and (3) estimating elements that affect WEC and WEF elements’ cost and translate them into factors using the parameters defined in step (2). After validation from Wavepiston manual estimations, the CapEx method showed that the factors could represent up to 30% of the cost. The Similitude method provided slight cost-overestimations compared to the CapEx method for low WEC up-scaling, increasing exponentially with the scaling

Sustainability of wave energy resources in southern Caspian Sea

This study aims to evaluate the wave energy potential and its spatial and temporal variations in the southern Caspian Sea. For this purpose, SWAN model was used to hindcast wave characteristics for 11 years. The wave energy assessment was conducted in four nearshore stations in order to assess the feasibility of wave energy harvesting and locate the most appropriate station. Assessment of seasonal and monthly variations of the mean and maximum wave powers showed that the central station contains the highest values, especially in November; while the north-eastern station has the lowest values with the highest variation of directional distribution of the wave power. Moreover, the seasonal and monthly variability indices indicate a relatively stable wave condition in all stations. The total and exploitable storages of wave energy were also higher in the central station. Therefore, it was concluded that the central station is the most appropriate location for wave energy harvesting. Furthermore, the inter-annual variations of the mean wave power illustrate no significant long-term change in wave power in the southern Caspian Sea. Therefore, considering the relatively stable condition and comparable exploitable storage of wave energy, this area can be a suitable location for developers

Temporal-spatial variation of wave energy and nearshore hotspots in the Gulf of Oman based on locally generated wind waves

This study aims to assess the wave energy at five coastal stations in the Gulf of Oman using the time series of locally generated wind waves obtained by numerical modeling for 11 years. For this purpose, the spatial, seasonal, monthly, directional, inter-annual of wave energy and power were investigated. The spatial distribution shows that the wave power increases towards the Indian Ocean and the highest mean wave power is located at the eastern station in all seasons. In addition, monthly mean wave power is highest during July and August while the monthly maximum wave power is highest during February at all stations. The ratio of monthly maximum to mean wave power is also the lowest during May to August. Moreover, Monthly Variability Index is the highest in west of the domain where there is no significant wave power potential. In addition, annual wave power as well as total and exploitable wave energies increases from west to east, where the dominant waves propagate from the south, and the exploitable wave energy is approximately 20 times greater than of the central stations

Individual-based modelling of cyanobacteria blooms: Physical and physiological processes

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs

Impacts of atmospheric stilling and climate warming on cyanobacterial blooms: An individual-based modelling approach

Harmful algal blooms of the freshwater cyanobacteria genus Microcystis are a global problem and are expected to intensify with climate change. In studies of climate change impacts on Microcystis blooms, atmospheric stilling has not been considered. Stilling is expected to occur in some regions of the world with climate warming, and it will affect lake stratification regimes. We tested if stilling could affect water column Microcystis distributions using a novel individual-based model (IBM). Using the IBM coupled to a three-dimensional hydrodynamic model, we assessed responses of colonial Microcystis biomass to wind speed decrease and air temperature increase projected under a future climate. The IBM altered Microcystis colony size using relationships with turbulence from the literature, and included light, temperature, and nutrient effects on Microcystis growth using input data from a shallow urban lake. The model results show that dynamic variations in colony size are critical for accurate prediction of cyanobacterial bloom development and decay. Colony size (mean and variability) increased more than six-fold for a 20% decrease in wind speed compared with a 2 °C increase in air temperature. Our results suggest that atmospheric stilling needs to be included in projections of changes in the frequency, distribution and magnitude of blooms of buoyant, colony-forming cyanobacteria under climate change

Uncertainties in wave-driven longshore sediment transport projections presented by a dynamic CMIP6-based ensemble

In this study four experiments were conducted to investigate uncertainty in future longshore sediment transport (LST) projections due to: working with continuous time series of CSIRO CMIP6-driven waves (experiment #1) or sliced time series of waves from CSIRO-CMIP6-Ws and CSIRO-CMIP5-Ws (experiment #2); different wave-model-parametrization pairs to generate wave projections (experiment #3); and the inclusion/exclusion of sea level rise (SLR) for wave transformation (experiment #4). For each experiment, a weighted ensemble consisting of offshore wave forcing conditions, a surrogate model for nearshore wave transformation and eight LST models was used. The results of experiment # 1 indicated that the annual LST rates obtained from a continuous time series of waves were influenced by climate variability acting on timescales of 20-30 years. Uncertainty decomposition clearly reveals that for near-future coastal planning, a large part of the uncertainty arises from model selection and natural variability of the system (e.g., on average, 4% scenario, 57% model, and 39% internal variability). For the far future, the total uncertainty consists of 25% scenario, 54% model and 21% internal variability. Experiment #2 indicates that CMIP6 driven wave climatology yield similar outcomes to CMIP5 driven wave climatology in that LST rates decrease along the study area’s coast by less than 10%. The results of experiment #3 indicate that intra- and inter-annual variability of LST rates are influenced by the parameterization schemes of the wave simulations. This can increase the range of uncertainty in the LST projections and at the same time can limit the robustness of the projections. The inclusion of SLR (experiment #4) in wave transformation, under SSP1-2.6 and SSP5-8.5 scenarios, yields only meagre changes in the LST projections, compared to the case no SLR. However, it is noted that future research on SLR influence should include potential changes in nearshore profile shapes

Wave energy and hot spots in Anzali port

Providing energy without unfavorable impacts on the environment is an important issue for many countries. Wave energy is one of the renewable resources with high potential and low impact on the environment, especially in coastal regions. The estimation of the wave characteristics is essential for selection of the appropriate location for wave energy exploitation. In this study, SWAN (Simulating WAves Nearshore) was used for modeling of the wave characteristics and to describe the existence and variability of wave energy in the southern part of the Caspian Sea. The model results were calibrated and verified using in-situ buoy measurements. Wave parameters were simulated and the annual wave energy was estimated in the study area. Then, high-energy spots were determined and the monthly average wave energy and seasonal variations of wave energy in the selected site were investigated. Furthermore, wave energy resource was characterized in terms of sea state parameters i.e. significant wave heights, wave periods and mean directions for selecting the most appropriate wave energy converters in the selected site. It was found that January and February, i.e. winter months, are the most energetic months and the main wave directions with the highest frequencies are northeast and northern-northeast in this site

Climate change impact on wave energy in the Persian Gulf

Excessive usage of fossil fuels and high emission of greenhouse gases have increased the earth’s temperature, and consequently have changed the patterns of natural phenomena such as wind speed, wave height, etc. Renewable energy resources are ideal alternatives to reduce the negative effects of increasing greenhouse gases emission and climate change. However, these energy sources are also sensitive to changing climate. In this study, the effect of climate change on wave energy in the Persian Gulf is investigated. For this purpose, future wind data obtained from CGCM3.1 model were downscaled using a hybrid approach and modification factors were computed based on local wind data (ECMWF) and applied to control and future CGCM3.1 wind data. Downscaled wind data was used to generate the wave characteristics in the future based on A2, B1, and A1B scenarios, while ECMWF wind field was used to generate the wave characteristics in the control period. The results of these two 30-yearly wave modelings using SWAN model showed that the average wave power changes slightly in the future. Assessment of wave power spatial distribution showed that the reduction of the average wave power is more in the middle parts of the Persian Gulf. Investigation of wave power distribution in two coastal stations (Boushehr and Assalouyeh ports) indicated that the annual wave energy will decrease in both stations while the wave power distribution for different intervals of significant wave height and peak period will also change in Assalouyeh according to all scenarios

A distributed wind downscaling technique for wave climate modeling under future scenarios

The aim of this study is to develop a Weibull-based distributed downscaling technique for wind field as forcing for the wave models to investigate the wave climate under future scenarios. For this purpose, the statistical downscaling approach modifies Weibull distribution parameters of the global circulation model wind speeds based on the corresponding features of wind data of ECMWF (European Center for Medium-Range Weather Forecasts). The proposed technique has the advantage of modifying the wind components in each grid point based on the corresponding values in the same grid point of ECMWF wind field. Hence, it is superior to other existing models due to considering the spatial variation. The previous models using inverse distance weighting suffer from heterogeneity and ignoring spatial variation in areas with high gradient of wind speed. Moreover, the Weibull-based technique outperforms the existing statistical downscaling techniques in terms of accuracy. Prior to investigate future distribution of wave characteristics, performance of the selected GCM was evaluated and compared against the corresponding models obtained from the available regional climate models. Future projections of wind fields (RCP4.5, RCP8.5) were downscaled for the period of 2081 to 2100 with the proposed model as driving force for wave modeling in the Persian Gulf. To investigate the impacts of climate change on wave characteristics, results of the wave simulations from a third generation wave model (SWAN) for future scenarios are compared with those of the historical period (1981–2000) in monthly, seasonal, and annual scales. Generally, for RCP8.5, the results indicate a decrease in future significant wave height and peak wave period about 15% and 5%, respectively. However, the change of wave direction is marginal. Moreover, wave models forced with RCP4.5 wind data provide slightly higher average values in terms of wave height and peak wave period compared to those of RCP8.5

Uncertainties in the projected patterns of wave-driven longshore sediment transport along a non-straight coastline

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes