A comparative study of two different approaches in wind energy simulations has been made where the aim was to investigate the performance of two commercially available tools. The study includes the linear model by WAsP and the computational fluid dynamic model of WindSim (also featuring an additional forest module). The case studied is a small wind farm located in the inland of Sweden featuring a fairly complex and forested terrain. The results showed similar estimations from both tools and in some cases an advantage for WindSim. The site terrain is however deemed not complex enough to manifest the potential benefits of using the CFD model. It can be concluded that estimating the energy output in this kind of terrain is done satisfyingly with both tools. WindSim does however show a significant improvement in consistency when estimating the energy output from different measurement heights when using the forest module compared to only using the standardized roughness length.

The aim of this thesis is to assess how the configuration of linear and non-linearwind resource grids impacts the optimization.Three different software tools are used for this study: WAsP (linear model) includedin WindPRO, and WindSim (a non-linear model) - a CFD tool, and WindPRO forthe optimization. With the same configuration for wind resources, WAsP andWindSim will run to calculate the wind resource grids, .rsf or .wrg format, whichwill be compared in the post processing tab of WindPRO (from CFD interface).Using different optimization algorithms, the results from two software will becompared. The test site is flat terrain in the sea with no complexity (0,0002roughness and no orography or obstacle), and the chosen turbine here is Enercon40.3 (55m hub height, with the rated power at 14 m/s), and the wind is coming fromone direction, in our case North, which means sector 0.After comparison of the resource files from linear and non-linear wind resourcegrids, the optimization and comparison is ran for the two wind resource grids (linearand non-linear). The results of the optimization are also compared with optimizationresults of Eftun Yilmaz’s thesis (Eftun Yilmaz, 2013). We can see from the resultsthat WindSim gives almost 40% bigger values for the production. The results arecomparable with findings of Eftun Yilmaz thesis.

Modelling the wake effect generated by wind turbines is an essential part for calcu- lating a wind farm’s expected energy production. Operating wind turbines disturb the flow of the wind, which results in decreased production of downwind turbines. The N. O. Jensen model is an industry standard wake model that assumes a linear expansion of the downstream wake. The only adjustable parameter in the model is the wake decay constant (WDC), which has traditionally been derived semi em- pirically from terrain surface roughness. However, the WDC defines the expansion rate of the generated wake, and therefore can be linked to the ambient turbulence intensity (TI): high ambient turbulence leads to a faster decay of the generated wake, and therefore to lower wake losses, and vice-versa. Since the influence of the roughness on the ambient turbulence intensity is expected to be less significant at higher heights, these roughness-based WDC values are rather uncertain for the hub heights employed nowadays. The following study presents the results of a comparison between observed and mod- elled wake losses based on different WDC values. To investigate how a change in height affects the wake modelling, two wake scenarios occurring between two tur- bine sets with different hub heights are selected from an operational wind farm. By modelling the wakes using roughness as well as turbulence intensity-based WDCs, conclusions can be drawn on how the predictive capability of the N.O. Jensen model depends on the selection of a suitable WDC value. Finally it is concluded that the goodness of fit between modelled and observed wake losses shows a clear dependency on the wind speed/power production inter- val. At higher wind speeds, the TI-based WDC resulted in a better accuracy of the modelled wake losses as compared to the roughness-based WDC, while for lower wind speeds the N. O. Jensen model performed most accurately when using WDC = 0.075. However, for the investigated cases the overall accuracy of the modelled wake appears to be higher when choosing WDC = 0.075 instead of a TI-based WDC.

The New European Wind Atlas (NEWA) was developed with an aim to provide high accuracy wind climate data for the region of EU and Turkey. Wind industry always seek for solid performance in wind resource assessment, and it is highly affected by the quality of modelled data. The aim of this study is to validate the newly developed wind atlas for two onshore sites in Sweden. Wind resource assessment is conducted using NEWA mesoscale data as wind condition of the sites. AEP estimation is performed using two different simulation tools, and the results of estimation are compared to the actual SCADA data for the validation of NEWA. During the process of simulation, downscaling is executed for the mesoscale data to reflect micro terrain effects. The result of the current study showed that NEWA mesoscale data represents wind climate very well for the onshore site with simple terrain. On the other hand, NEWA provided overestimated wind speeds for the relatively complex onshore site with forested areas. The overestimation of wind speed led to predict AEP significantly higher than the measurements. The result of downscaling showed only little difference to the original data, which can be explained by the sites’ low orographic complexity. This study contributes to a deeper understanding of NEWA and provides insights into its validity for onshore sites. It is beyond the scope of this study to investigate whole region covered by NEWA. A further study focusing on sites with higher orographic complexity or with cold climate is recommended to achieve further understanding of NEWA.

Many parameters used for Wind Resource Assessment (WRA) have uncertainty and variability, yet are input into the process as single values. The extent of the uncertainty or variance may not be known, and may or may not be significant enough to affect output. This Thesis focused on the energy calculation element of WRA, to assess the affect that errors (uncertainty) in three key user inputs had on the energy results. A parameter was chosen from each of the main groups influencing the energy calculation: wind speed (atmosphere), surface roughness (site conditions), and power curve (turbine technology). Reasonable variation due to uncertainty for wind speed and power curve were taken from other studies and their application simplified. Roughness change was assessed over the 5 classes (Class 0 (water) to 4 (dense forest/city)). WindPRO software was used to calculate the Annual Energy Production (AEP) and applied to three different wind turbine generators at the same coordinate. A sensitivity analysis was done on the AEP results using a hybrid One-At-a-Time Local Sensitivity Analysis by determining percentage changes from baselines and an overall rate of change for those key input parameters. The results showed that roughness class change effect was not linear. Changing from Class 0 to 1, AEP was on average -8±1%. Class 1 to 2 change was on average ‑12±1%. Class 2 to 3 change was on average -20±2%. Class 3 to 4 change was on average -29±2%. The wind speed change effect was found to be roughly linear. If mean wind speed has an error of ±10%, the AEP could be expected to be out by approximately +18/‑17% with a standard deviation of +4/-3%. The power curve change effect was also roughly linear. A PC±9% error leads to an approximate +6/-7% AEP error with a standard deviation of ±1%. Roughness class change was the most sensitive parameter to AEP with a 14.5 average rate of change, followed by wind speed at 1.8, then power curve with a 0.8 rate. Results compared reasonably well with other relevant studies.

Wind farms are costly projects and prior to the construction, comprehensive wind resource assessment processes are carried out in order to predict the future energy yield with a reliable accuracy. These estimations are made to constitute a basis for the financial assessment of the project. However, predicting the future always accommodates some uncertainties and sometimes these assessments might overestimate the production. Many different factors might account for a discrepancy between the pre-construction wind resource assessment and the operational production data. This thesis investigates an underperforming wind farm in order to ascertain the reasons of a discrepancy case. To investigate the case, the relevant data and information along with the actual production data of three years are shared with the author. Prior to the construction, a wind resource assessment was carried out by an independent wind consultancy company and the work overestimated the annual energy production (AEP) by 19.1% based on the average production value of available three years. An extensive literature review is performed to identify the possible contributing causes of the discrepancy. The data provided is investigated and a new wind resource assessment is carried out. The underestimation of the wind farm losses are studied extensively as a potential reason of the underperformance. For the AEP estimations, WAsP in WindPro interface and WindSim are employed. The use of WindSim led to about 2-2.5% less AEP estimations compared to the results of WAsP. In order to evaluate the influence of long term correlations on the AEP estimations, the climatology datasets are created using the two different reanalysis datasets (MERRA and CFSR-E) as long term references. WindSim results based on the climatology data obtained using the MERRA and CFSR-E datasets as long term references overestimated the results by 10.9% and 8.2% respectively.

This paper sets out to analyze the observed wake effect at Block Island Wind Farm. A comparison is made between several wake simulation methods and the observed data at Block Island using analytic and CFD (Computational Fluid Dynamics) modelling methods.  The observed wake results at Block Island show a similar trend evident in earlier papers- a large power deficit found between the first two Wind Turbine Generators (WTGs) in the row followed by a slight variation in power along the row for the remainder of the WTGs. A noticeable difference is seen between the last two WTGs in the row where an increase in power is found. This increase in power is thought to be due to the alignment of the wind farm. Nevertheless, when the observed data is compared with the modeled results, the observed data seem to underestimate the wake effect due to misalignment issue with the nacelle wind direction measurement. A sensitivity analysis is conducted on the Wake Decay Constant (WDC) and Turbulence Intensity (TI) values. The results show a maximum power variation of ≈30% between a WDC value of 0.07 and 0.04 and ≈18% for TI values between 8% and 14%. The findings show that a value in the higher range of the examined WDC (0.06 and 0.07) and TI (12% and 14%) values represent a better comparison to the observed data. Nevertheless, it is not recommended to alter these parameters to fit the observed data. Furthermore, due to high uncertainty in the data measurements, and hence observed results, a clear conclusion indicating which wake model best represents the wake effect at Block Island cannot be stated.

Successful wind power projects start with a realistic representation of the surface, more specifically the surface roughness of the site. This thesis investigates the use of airborne laser scans to model the surface roughness around a new wind farm. Estimations are made to find out how forest management and tree growth affects roughness length and displacement height. Data from scans two years apart for a specific site is provided by the Swedish governmental land registration authority. Next, tree height and plant area index methods are applied and analyzed using MATLAB. The results shows a difference of roughness length between 10.34% and 36.21% during an eight year period. WindPRO/WAsP is used to import roughness lengths for four specific cases. Height contour lines and meteorological data is taken from a long term corrected MESO data set. The results indicate a reduction in uncertainty in annual energy production between 0.79% and 2.89% across four different cases. This effect becomes significantly larger (12.76%) when comparing with classical land cover maps. Further on, effects of turbulence intensity are simulated.Finally, the results of a survey, sent to three large forest land owners in Sweden, show there is an interest in adapting forest management plans in favor of wind energy production if benefits can be shared.

Transitioning to a low-carbon energy system includes deploying renewables such as wind power, which has been installed in Sweden since the 1980s. After a 20 to 25-year lifetime, a wind turbine´s end-of-life options come into play, therefore many of the turbines deployed in Sweden prior to 2011 will reach this mark by 2035. To utilize a site´s wind resource in the best possible way, full repowering is considered in an assessment of seven case studies in Swedish municipalities with the highest deployed pre-2011 wind power capacity. Each case study uses various turbine models to evaluate full repowering scenarios. The most profitable scenarios are estimated through the investment over production (I/P) value and the break-even electricity price. The identified municipalities’ comprehensive plans are reviewed in terms of repowering strategies and wind power deployment guidelines. Only three out of seven investigated municipalities consider repowering in comprehensive plans, with Gotland being best prepared in terms of repowering strategies. Strömsund and Eslöv mention repowering in their comprehensive plans with no specific guidelines. Restrictive policies were identified in the municipality of Laholm, where the maximum total height of turbines is 150m, decreasing the potential annual energy production of an analyzed case study by 64%. The municipalities of Falkenberg, Laholm, Piteå, and Åsele do not include repowering in their comprehensive plans. All the simulated repowering scenarios increased the annual energy production of the identified sites by up to 73%, lowered the number of turbines by up to 70%, decreased the wake losses by up to 77%, and decreased the noise level by 10% while increasing the potential shadow flicker by 19%. The results of the study indicate a possible divide between the intention of the municipalities of Eslöv, Strömsund, and Åsele to maximize energy production from wind power at each exploited site on the one hand and the business cases that developers face on the other. The results suggest the turbines which increase energy production the most at already developed sites, are not necessarily the ones with the lowest investment over production (I/P) value or the lowest break-even electricity price.

Climate change is mankind’s biggest challenge and scientists around the globe agree that civilization is pushing towards a breaking point. Renewable energy are alternatives that are capable to remove the need for fossil fuel. Wind power will play a vital role and has the possibility to confront the challenges that face the globe. In order for wind power to reach its full potential constructors need to take into account the distance between each wind power turbine, as it can cause energy loss and generate less electricity into the system. These energy losses decrease the potential of wind power and thus also for renewable as a whole. Energy losses that emerge within the space between wind power plants are named wake losses. Once the wind has passed the plant, a distance equal to seven rotor diameters is needed for the wind to regain its full force. By positioning the plants within the announced distance, the production of each plant decreases since downstream turbines are not able to generate a full effect.       This Bachelor thesis in Energy Engineering aims to analyse these wake losses for the wind power plant Skäppentorp, which is situated in Mönsterås County. The nearby wind power plant Brotorp is affecting Skäppentorps production and the authors of this degree project chose to present the wake losses as a percentage. A third wind power plant named Idhult functioned as a reference. Idhult is of course not affected before the positioning of Brotorp but neither after it, therefore the plant was used to ensure that weak winds were not ascribed to Brotorp but are a result of a weak wind year. The Bachelor thesis covered thus three wind power plants, Skäppentorp which interacts and is affected by Brotorp and Idhult which served as reference. The wake losses were calculated in Microsoft Excel and set against the software windPRO to validate the programmed produced losses for the same plant. Skäppentorp’s surrounding were divided into 12 sectors, where each sector covers an angle of 30 degrees. By doing so a full circle, 360 degrees, surrounding the plant was established. The wind speed and the production before respectively after Brotorp deployment was produced by using a nearby measuring post. Via an average production value for each sector, before and after Brotorp, a percentage wake loss was calculated. This was set against Idhult to sort away better respectively worse wind years. The period before covered the year 2012 until 2015 and the period after covered 2016 until 2018.  The result from Microsoft Excel indicates that sector four and sector nine were subjected to the highest percentage of losses. The results from the software windPRO however indicated the highest loss in sector four. Three sectors obtained the same percentage loss as windPRO while remaining values came out dissimilar. The distinction between some of the sectors may be caused by the positioning of some of the Brotorp turbines, where some are located on the borderline between sectors. This implies that some turbines affect two sectors when calculated with Microsoft Excel, which it does not when simulated with windPRO. The sum of all sections indicated that Brotorp turbines caused a wake loss of 3,8 %. This was compared to the simulation in windPRO which resulted in 5,7 %.