Exploring the Interdependence of Vertical Extrapolation Uncertainties in Repowering Wind Farms

풍력발전 단지의 설계시 풍력 자원 평가 과정은 필수적인 과정이다. 풍력 자원 평가를 위해 장기풍황(20년)자료를 이용하여야 하지만 장기간 관측하는 것은 어렵기 때문에 예정지의 1년 이상의 관측데이터로 평가를 실시하였다. 예정지의 단기 풍황탑(Met-Mast; Meteorology Mast) 자료를 주변의 장기관측 자료인 자동기상관측(AWS; Automatic Weather Station)데이터를 이용하여 수학적 보간법으로 예정지의 데이터를 장기 데이터로 변환한 것을 MCP(Measure-Correlative-Predict)기법이라 한다. 본 연구에서는 MCP기법 중 선형 회계방법을 적용하였다. 선택된 MCP 회귀 모델식에 따라 제주 북동부 구좌지역의 AWS데이터를 제주 북동부 한동 지역의 Met-mast 데이터에 적용하여 연간 에너지 생산량을 예측 하였다. 예정지의 단기 풍황을 이용하였을 때와 보정된 장기 풍황을 이용하여 때 연간 에너지 생산량을 비교하였다. 그 결과 연간 약 3.6 %의 예측오차를 보였고, 이는 연간 약 271 MW의 에너지 생산량의 차이를 의미한다. 풍력발전기의 생애주기인 20년을 비교 하였을 때 약 5,420 MW의 차이를 나타내었으며, 이는 약 9개월 정도의 에너지 생산량과 비슷한 수준이다. 결과적으로, 제안 된 선형 회귀 MCP 방법을 이용하는 것이 단기관측 자료를 통한 불확식성을 제거하는 합리적인 방법으로 판단된다. Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km−2. CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms.

Optimization of the Levelized Cost of Energy (LCoE) in wind farms helps ensure profitability and competitiveness of the project. Recent work has explored driving down LCoE by allowing multiple wind turbines in a single wind farm - with different hub heights, rotor diameters, and rated powers. In this work, we performed optimization of the Lillgrund wind farm with continuously varying hub-heights to mitigate wake interference, improve annual energy production (AEP) and reduce LCoE. The optimization converged to a configuration where the turbines were vertically staggered, resulting in an improvement in both AEP and internal rate of return (IRR) - a financial metric related to LCoE. Reducing the number of turbines to a discrete set of 2 or 3 retained nearly all the benefits of staggering but is more aligned with limitations related to manufacturing and logistics.

Differences in wind farm energy production based on the atmospheric stability dissipation rate: Case study of a 30 MW onshore wind farm

This study investigates the effect of wake losses on the Annual Energy Production at the Norwegian offshore wind site Vestavind F . Three different scenarios of wind farm layouts consisting of turbines of the 5 MW, the 10 MW, and the 15 MW class, respectively, are analysed. Eleven years of historical wind data produced by the Norwegian reanalysis wind dataset NORA3 are used as a time-domain input to compute both the wind farm’s power production and the associated production losses due to wake flow interaction. The computations show a substantial year-to-year variation in both wake losses and energy production. The annual wake losses show an inverse relation to the annual average wind speeds and power production. The total wake losses of the 10 MW farm layout show large year-to-year variations from a minimum of 0.53 % to a maximum of 15.60 % of the total production. These significant fluctuations are primarily attributed to substantial differences in annual average wind speeds at the investigated location. Furthermore, the thrust coefficients and rated wind speeds of the turbine types are seen to influence the amount of wake losses. This study highlights the necessity to use meteorological input data of one decade or more to predict future Norwegian wind farms fluctuating energy production and their associated wake losses.

The assessment of the production capacity of wind farms is a crucial step in wind farm design processes, where a poor assessment can cause significant economic losses. Data from Canadian wind farms benefiting from national production incentive programs show that wind farms are typically characterized by an overestimation of the production capacity. In this context, a study has been done to provide insight on the origin of the discrepancies between the energy production estimates and the measured energy generation, and to develop a method to reduce these discrepancies. To this end, the WAsP and MS-Micro models have been studied. Besides the wind speed measurements, topography indices have been developed to identify the influence of the various characteristics of the site on the error in the annual energy production (AEP). In addition, roughness classes have been created, including a reference roughness and a roughness complexity. The indices have also allowed establishing correlations and developing equations to evaluate the error based on the site characteristics and the positions of wind turbines on the measured annual energy production. An average reduction of up to 83% on the AEP errors was obtained when the methodology was applied to five wind farms in Canada.

Kuwait plans to produce 15 % of its electricity from renewable resources by 2030. This paper aims at designing a 300-MW wind farm in six different sites in Kuwait. The study uses the measured wind data at Kuwait International Airport to predict the wind profile (speed and power density) at the selected sites. To validate the work, two different types of wind generation technologies are used in the design. The first type is Full-Converter Wind Turbine Generators (FCWTGs), and the second type is Doubly Fed Induction Generators (DFIGs). The designed wind farms are simulated using the Wind Atlas Analysis and Application Program (WAsP®) to estimate the Annual Energy Production (AEP) for each type of wind farms in each selected site. The location of every wind turbine in each wind farm (both types) for every selected site is accurately determined while considering needed spacing between rows and columns of turbines. The wake effect for downstream wind turbines is considered and estimated in the design. A comparison between the different wind farms in the six sites using the DIFGs and the FCWTGs generators is carried out. The economic feasibility of the designed wind farms is studied using the estimated Annual Energy Production (AEP) in each one of the designed wind farms.

An attempt is made to simulate the Centennial wind farm consisting of 83 wind turbines. The farm is located in the Coulee municipality region near Swift Current, Southern Saskatchewan (Canada). The Wind Atlas Analysis and Application Program and other mapping programs were used for generating the annual energy production (AEP). Based on the generated wind atlas and using both the meteorological data for a period of 10 years (2000–2009), and a vector map with height contour and surface roughness lines, the total AEP for the wind farm was calculated. In addition, AEPs of the wind farm were individually calculated for 2007, 2008 and 2009. The results of the simulated model were compared to the actual values for 3 years. The average absolute deviation between the predicted and actual mean AEP values was found to be less than 7%.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

The magnitude of wind speed as well as the amount of energy production increases with the height at which a wind turbine generator (WTG) is installed. This paper investigates on the optimum height at which a WTG should be installed at a location. Annual Levelized Cost of Energy (ALCoE) is chosen as the index to optimize the height. Six Indian wind sites have been chosen for the study. With time series wind speed data collected for the reference height, the wind regimes at different hub heights at each of the sites have been modeled and then used for estimation of annual energy production (AEP) using WAsP. AEP has been modified to account for uncertainties and then ALCoE has been computed. It is found that the cost of tower and foundation increases with the height, and therefore there is an optimum hub height at which ALCoE is the minimum.

Wind farms are generally designed with turbines of all the same hub height. If wind farms were designed with turbines of different hub heights, wake interference between turbines could be reduced, lowering the cost of energy (COE). This paper demonstrates a method to optimize onshore wind farms with two different hub heights using exact, analytic gradients. Gradient‐based optimization with exact gradients scales well with large problems and is preferable in this application over gradient‐free methods. Our model consisted of the following: a version of the FLOw Redirection and Induction in Steady‐State wake model that accommodated three‐dimensional wakes and calculated annual energy production, a wind farm cost model, and a tower structural model, which provided constraints during optimization. Structural constraints were important to keep tower heights realistic and account for additional mass required from taller towers and higher wind speeds. We optimized several wind farms with tower height, diameter, and shell thickness as coupled design variables. Our results indicate that wind farms with small rotors, low wind shear, and closely spaced turbines can benefit from having two different hub heights. A nine‐by‐nine grid wind farm with 70‐meter rotor diameters and a wind shear exponent of 0.08 realized a 4.9% reduction in COE by using two different tower sizes. If the turbine spacing was reduced to 3 diameters, the reduction in COE decreased further to 11.2%. Allowing for more than two different turbine heights is only slightly more beneficial than two heights and is likely not worth the added complexity.

The remarkable developments in renewable energy at the global scale have paved the way for a sustainable future and cleaner environment. As a fundamental element of this renewable energy revolution, wind energy has received tremendous attention worldwide. In order to harness maximal energy from a wind farm, one crucial decision is the selection of a turbine that is most compatible with the geographical and topographical characteristics of the location under consideration. This selection mechanism ideally considers multiple conflicting decision criteria—i.e., an improvement in one criterion negatively impacts one or more other criteria. Therefore, a tradeoff solution is desired in which the selection criteria are simultaneously optimized to the best achievable limits. Considering the above observations, this paper proposes a TOPSIS (the technique for order of preference by similarity to ideal solution)-based wind turbine selection approach. The problem is modeled as a multi-criteria decision problem while considering hub height, wind speed, percentages of zero and rated output, and annual energy production as the decision criteria. A case study is shown for data collected from two potential sites in northern and north-western Saudi Arabia. Fifteen turbines with rated capacities ranging from 1.5 to 3 MW from various manufacturers were evaluated. Results indicate that the TOPSIS approach was effective in identifying the best turbines for each site. Furthermore, the proposed approach helped in identifying the similarities in the behavior of turbines for the two sites. A comparison of the TOPSIS approach with other multi-criteria decision-making techniques proved the robustness of the method.

Floating offshore wind energy will play a key role in the clean energy transition. The number of large-scale wind farm projects is growing in regions like Northern Europe, the East Coast of the U.S., and the Mediterranean Sea. Offshore wind farms face fewer layout constraints, as they can be situated in vast, open sea areas. Turbines are often arranged in simple layouts, such as grid patterns, but this can cause significant annual energy production (AEP) losses due to wake–rotor interaction. Increasing spacing can mitigate this effect, but it may not always be feasible due to marine space limitations or higher costs for cabling and maintenance. This paper introduces a multi-objective wind farm optimization framework using a non sorted genetic algorithm (NSGA II) to minimize costs and maximize AEP. The method is applied to two case studies in the Mediterranean Sea, assuming 15 MW wind turbines. Case A is located off the coast of Civitavecchia, and case B in the Gulf of Squillace. AEP evaluation is performed with the open-source library FLOw Redirection and Induction in Steady-State (floris), while optimization is done using pymoo. In case A, the layout characterized by the lowest levelized cost of energy (LCOE) features 16 turbines, achieving an AEP of 709 GWh, an LCOE of 112.06 €/MWh, and wake losses of 2.6%. Meanwhile, in case B, the layout with the lowest LCOE consists of 19 turbines, achieving an AEP of 1140 GWh, an LCOE of 80.82 €/MWh, and wake losses of 4%.

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP.

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.