Capacity Factor Of Wind Turbines Research Articles

Onshore versus offshore capacity factor and reliability for wind energy production in Germany: 2010–2022

AbstractSimilarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods.

Growth in turbine size and technological development of modern commercial large scale wind turbines in Türkiye

In this study, in addition to the recent advances and tendencies in wind turbine technology around the world, the progress of commercial wind turbine technology installed in Turkey was also thoroughly examined. In this respect, several metrics for installed wind turbines including number of turbines, installed power capacity (MW), mean rated capacity (MW), mean rotor diameter (m), mean specific power capacity (W/m2), and mean hub height (m) have been obtained between the years of 2011 and 2019. According to the obtained results, the mean rated capacity of Turkey’s annual installed wind turbines advanced from 1.86 MW in 2011 to 3.52 MW in 2019. However, the mean specific power of yearly installed wind turbines declined from 423.7 W/m2 to 314.1 W/m2. Outcomes revealed that the growth in the size and the reduction in the specific power have contributed to the tendency of higher power outputs, and wind turbine capacity factor and power generation capacity have been on the rise in Turkey. In time, wind turbines with greater rotor diameters and hubs started to show up more observable on land. For that purpose, the proposed solution to regulate turbine visibility during site selection is the potential visibility model (PVM), which is meant to be used as an auxiliary variable.

Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations

Urban wind power is an appealing alternative for electricity supply. Comprehensive urban wind resource assessment is a prerequisite for cost-efficient deployment of wind turbines. Based on observations from multiple instruments, including a Doppler lidar (light detection and ranging) system, a microwave radiometer, and a cup anemometer/wind vane set, this study investigates the temporal (interannual, seasonal, and diurnal) variations of wind resources at various heights in a metropolitan city, Hong Kong. The long-term statistical distributions of wind speed, wind shear coefficient, and air density are analyzed, and their variations with height, wind direction, season, and time of day are thoroughly examined. Moreover, the wind power density and capacity factor of prototype wind turbines are estimated. It is observed that the statistical distributions of wind speed and air density are best represented by the Kappa and Normal-Weibull mixture distributions, respectively. There is a significant diurnal variation of wind shear coefficient, from 0.1 in the daytime to 0.4 at night. Moreover, a moderate potential for wind energy harvesting with wind power density of 110 W/m2 is found at the height of 120 m. This is the first known study that jointly uses lidar, microwave radiometer, and anemometer/wind vane for urban wind resource assessment. These instruments enable the investigation of the temporal variations of wind shear coefficient, air density, and wind power density at multiple heights, which were rarely examined in urban wind energy studies before.

Analysis of seasonal variability and complementarity of wind and solar resources in Mexico

Solar and wind time variability may be mitigated by complementing each other, providing firm power to the national grid at local and regional scales. The field commonly utilizes reanalysis data; however, it is crucial to conduct bias analysis and validation to enhance reliability. Mexico’s vast geography and resource potential demand a comprehensive study to validate the accuracy of modeling critical meteorological variables in power estimations. This study explored wind and solar resources’ local and regional complementarity using experimental and ERA5 data. A bias-correction method was used to identify bias effects on power estimations and the reliability of long-term capacity factor analysis. Spearman’s coefficient was used as the complementarity index. The results show that the capacity factor of wind turbines is affected by bias-correction methods compared to that of photovoltaics. Spearman’s coefficient, in general, is robust over bias-correction. The Yucatan Peninsula displays significant potential throughout the year, Spearman −0.6, for combining offshore wind farms with onshore photovoltaics. During summer, the north-central, northeast regions and the Baja California Peninsula offer significant potential for combining wind and solar energy, −0.6 and −0.5 values, respectively. Incorporating renewable energy requires further analysis of local integration and technical limitations to create sustainable long-term solutions.

Techno-economic assessment of wind power generation feasibility in Sabang

Utilization of renewable energy in Aceh is still limited mainly to small-scale electricity through micro-hydro and solar power plants, accounting for less than 1% of the energy composition. Sabang stands out as a region with significant wind energy potential, boasting a speed of 8 m/s and an average power density of 537 W/m2 for the windiest 10% of the region. Therefore, to explore the viability of harnessing wind energy, a pre-feasibility study is needed. This study aimed to assess the techno-economic feasibility of constructing a wind power plant for electricity generation in Sabang by evaluating the region's wind energy. The statistical method of the Weibull Distribution probability density function was employed to evaluate Wind Power Density (WPD), Wind Turbine Capacity Factor, and Energy Output. Additionally, economic feasibility analysis involved calculating Net Present Value (NPV), Benefit Cost Ratio (BCR), Internal Rate of Return (IRR), Discounted Payback Period (DPP), and Levelized Cost of Energy (LCOE). The results showed that Sabang exhibited favorable[UMN1] wind characteristics with a shape parameter (k) of 1.6, average wind speed (Vm) of 7.9 m/s, and scale parameter (c) of 9 m/s. These features classified Sabang as wind power class 6 with a WPD of 735 watts/m2 at a height of 50 m, and the results were categorized as excellent. The study concluded that constructing a wind power plant in Sabang using the Enercon E-70 turbine at a hub height of 100m was technically feasible. The shape parameter (k) was 1.6, the average wind speed (Vm) was 9.3 m/s, scale parameter (c) was 11 m, and the WPD reached 1213 W/m2, leading to a turbine capacity factor of 0.53 and an annual energy production (AEP) of 64,876,560 kW. The economic analysis yielded promising results for wind power projects. The DPP value was estimated at 5.6 years, the BCR at 1.96, the NPV amounted to USD 14,434,994, the IRR reached 17.9%, and the LCOE was assessed at USD 32.50/MWh. Considering all these economic indicators, it was evident that constructing a wind power plant in Sabang was highly feasible and financially viable

What drives the change of capacity factor of wind turbine in the United States?

The capacity factor (CF) is a vital parameter used to quantify the performance and efficiency of a wind turbine. An increase in generation efficiency leads to higher wind power production, improving the economics within the growing global wind market. In this research, we use a data-driven statistical method to explore the contributions of the three main drivers of CF change: turbine aging, changes in wind speed, and technological improvements. We find that for the group of old turbines (operated before 2008) with an unchanging technical condition, wind increases contributed ∼10% to the increasing CF on average from 2010 to 2020. For new turbines (built from 2008 to 2020), technological improvements had a strong positive effect on CF from 2015 to 2020, exceeding the effect of wind increases and offsetting the effects of aging. On average, rising wind speeds increased CF by ∼5% per year, while technological improvements increased it by ∼12%. As the installed capacity of wind turbines grew, technological progress became the dominant driver in CF increase. However, poor site selection potentially compromised the positive effect on CF afforded by technology changes early in the decade.

Synthetic jet application in the wind turbine concentrator design

ABSTRACT Due to population growth and technological advancements, the energy demands of countries are increasing every year. Consequently, research and development (R&D) studies in renewable energy have been on the rise in order to meet the growing energy demands. Wind energy is especially crucial in this regard. Wind turbines are capable of producing energy within the wind speed range of 5 m/s to 25 m/s, with a capacity factor of approximately 30–35%. In areas where wind speeds are insufficient, efforts are being made to improve turbine performance for energy production at low wind speeds. One of these studies involves incorporating the turbine into a concentrator to increase the wind speed and subsequently the capacity factor of the turbine for energy production at low wind speeds. In this study, the concentrator of the wind turbine with the SG 6043 blade section is taken into consideration, and the impact of synthetic jet application in the wind turbine concentrator is investigated. An optimization study is conducted to determine the parameters that will maximize the speed increase in the concentrator. The optimum parameters that lead to the maximum wind speed increase in the concentrator are as follows: the velocity of the synthetic jet is 140 m/s, the frequency of the synthetic jet is 58.89 Hz, the angle of the synthetic jet is 26.16º, the position of the synthetic jet is 0.675 m, and the angle of attack is 24.1º at a free wind speed of 5 m/s. The use of synthetic jet application in the wind turbine concentrator results in a 1.33 times increase in free wind speed. Consequently, the concentrator with synthetic jet application can be used in regions with poor wind speed statistics to generate energy from wind and increase the capacity factor of wind turbines.

Evaluation method of marine spaces for the planning and exploitation of offshore wind farms in isolated territories. A two-island case study

The interest in offshore wind energy is fundamentally based in most cases on the exploitation of the high levels of wind power found in offshore areas. The evaluation of marine spaces for planning and exploitation of this type of energy source is even more important in territories with isolated and/or weak electrical systems and limited available territory. In the study developed in this paper, GIS-based techniques are applied to identify priority zones for the exploitation of offshore wind farms in the islands of Gran Canaria and Tenerife, the two largest of the eight islands that make up the Canary Archipelago (Spain). The zones identified are prioritized and differentiated by the most viable type of wind turbine substructure technology (bottom-fixed or floating), capacity factor (in MWh/MW), and specific cost (€/MWh). The results are expressed in map form, which can form part of future energy planning processes for the islands. The resulting identified offshore priority zones comprise 375.9 and 68.2 km2, with bottom-fixed:floating surface area ratios of 1:6 and 1:21, for Gran Canaria and Tenerife, respectively. The method developed in this paper is vital for detailed energy planning, an important aspect in the optimization process of offshore wind energy integration in isolated and/or weak electrical systems.

Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China

The joint probability density function can quantitatively describe the statistical characteristics and correlation features between wind speed and shear; this forms the theoretical basis for assessing height-dependent wind energy. Here, a nonparametric copula-based joint probability model of wind speed-wind shear is developed to assess height-dependent wind energy in China. Utilizing the transformation method and optimal bandwidth algorithms, a nonparametric copula model for wind speed/wind shear correlation analysis is proposed. Joint probability density models of wind speed and wind shear are then constructed. Various copula and marginal density models (including single parametric, mixture parametric, and kernel density estimation models) are evaluated at the regional scale. The nonparametric copula model exhibits remarkable superiority and is therefore deemed to be more suitable for wind speed/wind shear correlation analysis and joint probability modeling. When assessing height-dependent wind energy, the average distributions of wind turbine power output and capacity factor are obtained across mainland China. Additionally, this model could accurately analyze variations in wind power density with respect to hub height. These results can effectively facilitate accurate micro-site selection, thereby economically benefiting wind farms.

Sizing Optimization and Techno-Economic Analysis of a Hybrid Renewable Energy System Using HOMER Pro Simulation

Hybrid renewable energy system (HRES) is the integration of multiple energy generating systems installed to generate energy from the renewable sources such as Solar Photovoltaic (PV), wind, bio-generator etc. It is very prudent to determine viable combinations, optimum sizing and to have a techno-economic analysis of HRES before its procurement and installation. In this study two optimum system was modelled using HOMER Pro (open-source version) to meet the electrical load demand of an institution located in Chennai (12.59°N and 80.14°E) and to get the minimum Net Present Cost (NPC) of the proposed system. Based on the modeling, for optimum-1 system the capacity factor and contribution percentage of PV, wind turbine and bio-generator was found to be 79.41%, 0.98%, 19.61% and 92.83%, 0.43%, 6.75%, respectively. And for optimum-2 system, it was 85.86%, 2.02%, 12.12% and 93.31%, 0.82%, 5.87%, respectively. Sensitivity analysis was carried out to find out the variations on NPC and Cost of Energy (COE) by adjusting the cost of PV, wind turbine, battery, converter, bio-generator and fuel, from 0.8 to 1.2 times of its present cost. Based on HOMER Pro simulation, the most feasible system obtained was optimum-1 which consists of 81 kW photovoltaics, 1 kW wind turbine, 20 kW biogas generator, 47.3 kW converter and lead acid battery (rated 101 Ah/12 V-150 numbers) with NPC at $ 1,84,687 to generate energy 1,35,978 kWh per year.

Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

This study analysed temporal and spatial changes in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula during the 20th century by analysing data from ERA20 and ERA5. Both WPD and CF were calculated using reanalysis data considering a wind turbine with a hub height of 90 m and incorporating the effect of air density changes. Since ERA5 assimilates more observations, the data from ERA20 was bias-corrected using quantile matching, with ERA5 reanalysis data as the reference. As both variables are based on wind speed (WS), this variable was also corrected and analysed. The results show that the mean values for WPD, CF, and WS during the 20th century were highest in the Atlantic zone and the Gulf of Lyon and lowest around the Balearic Islands. The results of the assessment of decadal trends using the Theil–Sen estimator show that all indicators increased significantly in the waters of the Iberian Peninsula during the study period (1900–2010). Considering the mean slope over this period, the change over the entire period could amount to 174 Wm-2 for WPD, 8.8% for CF, and 1.1 ms−1 for WS. Based on these changes, offshore wind turbines would have increased their returns by approximately 20% over the 11 decades.

On the potential yield of wind turbines on high-rise buildings

Urban wind energy has intrigued some investments and applications over the last decade. However, most applications have been unsuccessful, in many cases leaving behind non-rotating wind turbines in the built environment. Research on urban wind energy has also lacked in providing a reliable description of the local flow features small wind turbines are placed in, and the positioning strategy is mostly based on empiricism. This study uses data from a wind tunnel test on the flow pattern above the roof of high-rise buildings to estimate the potential yield of small wind turbines installed in various configurations. The data are collected at a height of 12 m above the flat roof under various wind directions. The capacity factor of a medium-size horizontal axis wind turbine is calculated using power curve data available from the literature. The local wind resource is calculated using the mean wind speed as measured with hot-wire anemometry. The annual energy production of the various configurations is calculated using the climate data available for Firenze (IT) and the orientation of the building with respect to main winds. A rather large variability in the power output of the roof-farm was found for the chosen configuration.

A new semi-empirical wind turbine capacity factor for maximizing annual electricity and hydrogen production

The capacity factor is an important wind turbine parameter which is ratio of average output electrical power to rated electrical power of the wind turbine. Another main factor, the AEP, the annual energy production, can be determined using wind characteristics and wind turbine performance. Lower rated power may lead to higher capacity factor but will reduce the AEP. Therefore, it is important to consider simultaneously both the capacity factor and the AEP in design or selecting a wind turbine. In this work, a new semi-empirical secondary capacity factor is introduced for determining a rated wind speed at which yearly energy and hydrogen production obtain a maximum value. This capacity factor is expressed as ratio of the AEP for wind turbine to yearly wind energy delivered by mean wind speed at the rotor swept area. The methodology is demonstrated using the empirical efficiency curve of Vestas-80 2 MW turbine and the Weibull probability density function. Simultaneous use of the primary and the secondary capacity factors are discussed for maximizing electrical energy and hence hydrogen production for different wind classes and economic feasibility are scrutinized in several wind stations in Kuwait.

Techno-Economic Analysis of Standalone Solar Photovoltaic-Wind-Biogas Hybrid Renewable Energy System for Community Energy Requirement

Integrated renewable energy system (IRES) is integration of different energy sources to provide uninterrupted and viable solution for electrification especially for areas not connected to main grid due to difficult terrain and economic reasons. IRES has many advantages like non-depleting, non-polluting nature, better load matching and better renewable energy utilization. In the present study, mathematical modelling, size optimization and techno-economic analysis of standalone IRES have been carried out. Hybrid system is modelled to have maximum contribution from wind and solar energy with minimum net present cost (NPC) of system to meet electric load demand of CRC building, IIT Madras, India (13.01°N and 80.24°E). The results show that most feasible system configuration consists of 12 kW Photovoltaics, 3 kW wind turbine and 15 kW biogas generator with NPC and cost of energy equal to $ 117,098 and $ 0.09/kWh respectively. The IRES generates 71,826 kWh of energy to meet AC load of 64,396 kWh per year. The capacity factor and percentage contribution of PV, wind turbine and biogas generator are 17.8%, 6.57%, 39.1% and 26%, 2.4%, 71.6% respectively. The paper also presents sensitivity analysis of hybrid system with variation in capital cost of different components.

Comparison of the capacity factor of stationary wind turbines and weather-routed energy ships in the far-offshore

Offshore wind energy technology has developed rapidly over the last decade. It is expected to significantly contribute to the further increase of renewable energy in the global energy production in the future. However, even with floating wind turbines, only a fraction of the global offshore wind energy potential can be harvested because grid-connection, moorings, installation and maintenance costs increase tremendously as the distance to shore and the water depth increase. Thus, new technologies enabling harvesting the far offshore wind energy resource are required. To tackle this challenge, mobile energy ship concepts have been proposed. In those concepts, electricity is produced by a water turbine attached underneath the hull of a ship propelled by the wind using sails. It includes an on-board energy storage system since energy ships are not grid-connected. Thus, the ships route schedules could be dynamically optimized taking into account weather forecast in order to maximize their capacity factors (CF). The aim of this study is to investigate how high the capacity factors of energy ships could be when using weather-routing and compare them to that of stationary wind turbines that would be deployed in the same areas. To that end, a modified version of the weather-routing software QtVlm was used. Velocity and power production polar plots of an energy ship that was designed at LHEEA were used as input to QtVlm. Results show that capacity factors over 80% can be achieved with energy ships and stationary offshore wind turbines deployed in the North Atlantic Ocean.

Capacity factor estimation of variable-speed wind turbines considering the coupled influence of the QN-curve and the air density

This paper proposes a systematic method that precisely estimates the capacity factor (CF) of the variable-speed wind turbine (WT) by considering the coupled influence of the turbine operation constraints and the air density. To do so, the WT’s operation is defined by introducing the QN-curve (denoting the generator torque versus rotor speed), and the influence of different QN-curves on the power production is analysed while considering the influence of the air density. Then, a practical power-curve model considering the constraint of the QN-curve is derived for the WT. Following that, the formulation and the procedure of CF estimation are presented. Lastly, the presented CF estimation approach is applied into an industrial WT while considering the wind sites with four different altitudes. Finally, the application results show the capabilities of the proposed approach in evaluating the CF under the coupled influence of the QN-curve constraint and the air density. Meanwhile, comparing the proposed approach to four empirical approaches demonstrates that the CF estimation based on empirical models has considerable deviations to the results of the presented model under different site altitudes. Furthermore, among the four empirical models, the CF estimations from the quadratic and the linear models present the least deviations to those by the proposed model at the sites with low altitude and high altitude, respectively.

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

System design and policy suggestion for reducing electricity curtailment in renewable power systems for remote islands

In renewable power systems for remote islands, a significant amount of electricity is curtailed for power balance. This study examines the effect of reducing electricity curtailment on optimal renewable power system design for remote islands. A Mixed-Integer Linear Programming problem for obtaining the optimal design of remote renewable power systems is formulated with an additional constraint which limits the total electricity curtailment. For a parametric study, the problem is solved with various values of electricity curtailment limit, for four cases; two islands - one with a higher and the other with a lower capacity factor of wind turbines - in South Korea, and two renewable power penetrations - 60% and 90% - in the two islands. Variations in the optimal capacities of photovoltaic panels, wind turbines, and batteries for limited total electricity curtailment are examined for every case. Reasons of the variations are explained by an analysis on the temporal operation profiles of the remote renewable power systems. In addition, the appropriate upper limit of total electricity curtailment considering cost increment, normalized by the total electricity demand, is newly suggested as a function of renewable power penetration to guide policy design.

Techno-economic performance assessment of central-grid wind turbines at major geographical locations of Pakistan

This work is focused on techno-economic performance evaluation of grid connected wind turbines of different power ratings (2 MW and 3 MW) at six coastline areas of Pakistan using RETScreen software. Software imports the weather data of selected locations from its database reported by National Aeronautics and Space Administration. Software outcomes indicate the unadjusted energy production, gross energy production and capacity factor of wind turbines. Comparative study shows that the wind turbine of either rating installed in Jiwani, having capacity factor 33.7% (of 2 MW turbine) and 36.8% (of 3 MW wind turbine) produces ~64% more gross energy than at Karachi and ~4% more than that at Ormara location. Financial viability of wind power project installation is evaluated considering economic determinants and life cycle savings. Technical analysis also reveals the amount of electricity exported to grid. Comparative performance of 3 MW and 2 MW wind turbines are evaluated, and 3 MW wind turbine is found more viable and profitable. Jiwani is found the most favorite location for wind power project installation.

An evaluation of offshore wind power production by floatable systems: A case study from SW Portugal

The challenge for floating offshore wind structures is to reduce costs. The industry needs a wind turbine support solution that can be fabricated and deployed from existing shipyards and port facilities, while investors need accurate estimations and forecasts of wind resources and quantified information on the inherent variability in wind power generation. This paper merges hindcast model data with observed in situ data to characterize the wind resource potential off the SW coast of Portugal. The validation procedure adopted allows an estimation of the coefficient used for power-law extrapolation of the wind measurements and a reduction in the uncertainty of the power density calculations. Different types of turbine model are compared and site metocean characteristics are examined as a basis for choosing between existing wind floatable solutions. The calculations using four different wind turbine models indicate a preferable installed capacity of 3–4 MW for a hub height of 90–120 m (i.e., representing the best capacity factor and load hours). There is a consistent difference in power density of about 20% from a location 5 nautical miles (NM) offshore to one 10 NM offshore, which represents an increment of 20%–25% in energy production depending on the particular wind turbine capacity factor.