Wind Capacity Factor Research Articles

A reversal in global terrestrial stilling and its implications for wind energy production

Wind power, a rapidly growing alternative energy source, has been threatened by reductions in global average surface wind speed, which have been occurring over land since the 1980s, a phenomenon known as global terrestrial stilling. Here, we use wind data from in situ stations worldwide to show that the stilling reversed around 2010 and that global wind speeds over land have recovered. We illustrate that decadal-scale variations of near-surface wind are probably determined by internal decadal ocean–atmosphere oscillations, rather than by vegetation growth and/or urbanization as hypothesized previously. The strengthening has increased potential wind energy by 17 ± 2% for 2010 to 2017, boosting the US wind power capacity factor by ~2.5% and explains half the increase in the US wind capacity factor since 2010. In the longer term, the use of ocean–atmosphere oscillations to anticipate future wind speeds could allow optimization of turbines for expected speeds during their productive life spans.

Building resilient renewable power generation portfolios: The impact of diversification on investors’ risk and return

Over the coming years, variable renewables such as wind and photovoltaics will become increasingly exposed to market risks due to the gradual reduction in policy support. Building diversified portfolios of variable renewables and complementary technologies such as storage (i.e. technological diversification), and in different regions (i.e. geographical diversification) seem to be promising options to mitigate these risks. The extant literature, however, does not provide a comprehensive comparison of these two diversification strategies. Using actual production data from eight wind and photovoltaics plants across Germany from 2015 until 2017 and a storage unit for arbitrage operations, we build a quantitative model to evaluate the impact of these strategies on investors’ risk and return. In our analysis, we compare the results of two scenarios: the first with actual prices and the second which assumes prices reflecting higher shares of variable renewables in the power system. In doing so, our study provides the following important insights for investors: (1) technological diversification largely yields lower risk levels than geographical diversification, (2) maximizing the capacity factor of wind and photovoltaics is an effective way to mitigate risk, and (3) while technological diversification with another variable renewable technology is more effective under current conditions, storage gains importance for mitigating risk in times of high shares of variable renewables.

Addendum: Observation-based solar and wind power capacity factors and power densities (2018 Environ. Res. Lett. 13 104008)

‘Observation-based solar and wind power capacity factors and power densities’ (Miller and Keith 2018 Environ. Res. Lett. 13 104008) contained a methodological error in how we estimated wind plant area, leading to an underestimate of wind power densities. The method and revised results were published as a Corrigendum (Miller and Keith 2019 Environ. Res. Lett. 14 079501). Given the importance of these estimates to energy policy, here in this Addendum, we expand on these corrected results, while also describing the public release of data to allow verification by third-parties. Specifically, here we: (1) illustrate our method by showing in greater detail how it works for the 2 wind power plants from figure 1 of the original study, (2) identify potential selection biases in the sampling of wind power plants used in our study, (3) provide a comparative overview of the various prior published estimates in graphical form, and, (4) conclude with a description of the data we are releasing publicly.

Simulating subhourly variability of wind power output

AbstractAs installed wind power capacity grows, subhourly variability in wind power output becomes increasingly important for determining the system flexibility needs, operating reserve requirements, and cost associated with wind integration. This paper presents a new methodology for simulating subhourly wind power output based on hourly average time series, which are often produced for system planning analyses, for both existing wind plants and expanded, hypothetical portfolios of wind plants. The subhourly model has an AR(p)‐ARCH(q) structure with exogenous input in the heteroskedasticity term. Model coefficients may be fit directly to high‐pass filtered historical data if it exists; for sets of wind plants containing hypothetical plants for which there are no historical data, this paper presents a method to determine model coefficients based on wind plant capacities, capacity factors, and pairwise distances. Unlike predecessors, the model presented in this paper is independent of wind speed data, captures explicitly the high variability associated with intermediate levels of power output, and captures distance‐dependent correlation between the power output of wind plants across subhourly frequencies. The model is parameterized with 1‐minute 2014 plant‐level wind power data from Electric Reliability Council of Texas (ERCOT) and validated out‐of‐sample against analogous 2015 data. The expanded‐capacity model, fit to 2014 data, produces accurate subhourly time series for the 2015 wind fleet (a 49% capacity expansion) based only on the 2015 system's wind plant capacities, capacity factors, and pairwise distances. This supports its use in simulating subhourly fleet aggregate wind power variability for future high‐wind scenarios.

A multi-site production and microgrid planning model for net-zero energy operations

This paper aims to address two critical questions pertaining to sustainable manufacturing operations. First, how to model and design a multi-facility, production-logistics system under product demand and energy supply uncertainty? Second, is it economically viable to achieve net-zero energy operations through renewable microgrid integration? To answer both questions, we present a joint production scheduling and microgrid sizing model to decarbonize the manufacturing, transportation and warehousing operations by harnessing onsite wind and solar energy. The integrated planning model is solved as a two-stage optimization program: first, scheduling the production to meet the uncertain demand; second, sizing and siting the microgrid systems to meet the electric load of multiple facilities. The preliminary results show that net-zero energy operation is feasible and affordable in locations where the capacity factor of wind and solar generation exceeds 0.25 and 0.45, respectively. Sensitivity analysis shows that battery system combined with solar photovoltaics is cost-effective despite the high capital cost of storage devices. Two managerial insights are also derived. First, integrating onsite renewable power is the ultimate key to realizing net-zero energy industrial operations. Second, grid-connected microgrid generation with net metering results in lower capacity investment than island operation.

An onsite demonstration and validation of LiDAR technology for wind energy resource assessment

ABSTRACTThe investigation of wind resource at higher heights is very crucial in planning wind power project. Normally, this involves the installation of a high and costly meteorological mast with a cup anemometer and wind vanes. This investigation uses the new ground-based remote-sensing technique Light Detection and Ranging (LiDAR) to investigate the wind resource at higher heights. This paper describes the LiDAR technology principle and examines the potential of LiDAR measurement to estimate the wind resource at higher heights by conducting a measurement campaign at Tamil Nadu, India. The wind statistics were determined using the 10 min average time-series wind data monitored by ZephIR LiDAR. These include the Weibull parameters, daily mean wind speed, wind power density, wind energy density, vertical wind speed profile and capacity factor. The investigation reveals that the vertical wind speed profile measured from the LiDAR system has approximate closer values to the standard meteorological measurement.

Wind capacity factor calculator

The wind capacity factor calculator is developed to perform two main tasks: to estimate the annual energy production from the wind resource at any location in South Africa, and to compare the two datasets used in its operation with standard error analysis to determine whether both datasets are suitable for use. This paper focuses on how the software was developed and on error analysis between the CSIR PV/ wind aggregation study data and the latest Wind Atlas for South Africa data. The results will indicate the way forward after determining whether the error found between the two datasets is significant enough to replace the former with latter, going forward.

Generation capacity expansion under demand, capacity factor and environmental policy uncertainties

Abstract This paper presents an extension and application of the two-stage stochastic optimization methodology to determine electricity generation technology choices and capacity additions under sequential and multiple uncertainties. Specifically, the model presented in this paper addresses how uncertainties in electricity demand, capacity factor of wind and solar technologies, and environmental policy combine to affect the capacity additions of electricity generation technologies. This study unpacks the impact of these uncertainties to find that, on the one hand, when policy uncertainty is isolated from uncertainties in demand and capacity factor, neither of the two uncertainties dominates the other; instead they are complementary. Capacity factor uncertainty will dictate the types of technologies to be deployed while demand uncertainty will determine the amount of capacity to be installed or purchased. On the other hand, a combination of the three dimensions of uncertainty highlights intriguing insights that demonstrate the dominance of one uncertainty over the other. Under two different environmental policies, emissions standards and carbon taxes, the optimal prescription of generation technology combinations with respect to capacity additions are varied. However, we find that carbon tax will be more efficient at reducing CO2.

Forecasting of Turkey's electricity generation and CO2 emissions in estimating capacity factor

In this study, Turkey's electricity generation between 2017 and 2021 is forecasted. To achieve this, past data of capacity factor of thermal, hydro, geothermal, wind, and solar power plants from 2006 to 2016 is correlated by using exponentially damped sinusoidal approximation. Estimated installed capacity values of electricity generation from 2017 to 2021 are taken from Turkish Electricity Transmission Company (TEİAŞ). The capacity factor and electricity generation of each power plants are forecasted from 2017 to 2021 for two different scenarios which are based on TEİAŞ's 5‐year installed capacity projection. According to scenario 1 and 2, Turkey's electricity generation is projected in the range of 332.5–362.9 TWh and 322.0–345.2 TWh, respectively. Additionally, projected CO2 emissions due to electricity generation are estimated in the range of 178.7–199.2 million tons and 171.6–187.1 million tons for the Scenario 1 and Scenario 2, respectively. Also, this study is compared with Turkey's vision 2023 electricity generation targets. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 56–65, 2019

Comparing offshore and onshore wind development considering acceptance costs

Cost efficient deployment of wind energy is in focus for reaching ambitious targets for renewable energy and transforming the energy supply to one based on renewables. However, as more wind is being deployed the available sites onshore become less attractive in terms of wind conditions and capacity factor and more resistance from population groups affected in the deployment areas results in a reduction of areas that can be developed. We consider three different methods for estimating acceptance costs, one based on compensation and property purchase costs, one based on property value loss near wind turbines, and one based on willingness to pay calculated from a stated preference study. Utilising these methods, we provide an estimation of Levelised Cost of Energy (LCOE) for an expansion of 12 GW onshore or offshore wind capacity in Denmark. We find that the three methods provide similar estimates for local acceptance, but that a high range of uncertainty exists in the upper bound of acceptance costs. Onshore does not have a clear-cut cost advantage over offshore when considering substantial amounts of wind capacity expansion and using high estimates for nation-wide acceptance costs. Moderate onshore wind expansion considering only local acceptance has a cost advantage.

Observation-based solar and wind power capacity factors and power densities

Power density is the rate of energy generation per unit of land surface area occupied by an energy system. The power density of low-carbon energy sources will play an important role in mediating the environmental consequences of energy system decarbonization as the world transitions away from high power-density fossil fuels. All else equal, lower power densities mean larger land and environmental footprints. The power density of solar and wind power remain surprisingly uncertain: estimates of realizable generation rates per unit area for wind and solar power span 0.3–47 We m−2 and 10–120 We m−2 respectively. We refine this range using US data from 1990–2016. We estimate wind power density from primary data, and solar power density from primary plant-level data and prior datasets on capacity density. The mean power density of 411 onshore wind power plants in 2016 was 0.50 We m−2. Wind plants with the largest areas have the lowest power densities. Wind power capacity factors are increasing, but that increase is associated with a decrease in capacity densities, so power densities are stable or declining. If wind power expands away from the best locations and the areas of wind power plants keep increasing, it seems likely that wind’s power density will decrease as total wind generation increases. The mean 2016 power density of 1150 solar power plants was 5.4 We m−2. Solar capacity factors and (likely) power densities are increasing with time driven, in part, by improved panel efficiencies. Wind power has a 10-fold lower power density than solar, but wind power installations directly occupy much less of the land within their boundaries. The environmental and social consequences of these divergent land occupancy patterns need further study.

A new approach for assessing synergies of solar and wind power: implications for West Africa

West African countries’ energy and climate policies show a pronounced focus on decarbonising power supply through renewable electricity (RE) generation. In particular, most West African states explicitly focus on hybrid mixes of variable renewable power sources—solar, wind and hydropower—in their targets for the electricity sector. Hydropower, the main current RE resource in West Africa, is strongly sensitive to monsoon rainfall variability, which has led to power crises in the past. Therefore, solar and wind power could play a stronger role in the future as countries move to power systems with high shares of RE. Considering the policy focus on diversified RE portfolios, there is a strong need to provide climate services for assessing how these resources could function together in a power mix. In this study, climate data from the state-of-the-art ERA5 reanalysis is used to assess the synergies of solar photovoltaic (PV) and wind power potential in West Africa at hourly resolution. A new metric, the stability coefficient Cstab, is developed to quantify the synergies of solar PV and wind power for achieving a balanced power output and limiting storage needs. Using this metric, it is demonstrated that there is potential for exploiting hybrid solar/wind power in a larger area of West Africa, covering more important centers of population and closer to existing grid structures, than would be suggested by average maps of solar and wind resource availability or capacity factor for the region. The results of this study highlight why multi-scale temporal synergies of power mixes should be considered in RE system planning from the start.

Comparison of long-term wind and photovoltaic power capacity factor datasets with open-license

Investigation of pathways toward decarbonisation of energy supply systems strongly relies on integration of electricity generation from wind and photovoltaics (PV). Energy system model authors are typically not experts in creation of representative weather datasets, which are fundamental for an unbiased representation of volatile power generation within the models. The aim of this work is therefore to benchmark data quality and verify against feed-in records for datasets published from two projects: EMHIRES and Renewables.ninja; feed-in records taken from Transmission System Operators (TSO). Both projects used meteorological reanalysis data from NASA (National Aeronautics and Space Administration) and Meteosat-based datasets from CM-SAF (Satellite Application Facility on Climate Monitoring) to generate long-term hourly PV and wind power capacity factor time series. Although datasets were based on the same raw data sources, they present significant differences due to modelling of energy conversion technologies, correction and validation methods. Comparison of duration curves, full load hours, plots of hourly PV capacity factors as well as correlation analysis between datasets reveal that for PV generation EMHIRES is more similar to TSO’s data, while the Ninja dataset revealed more similarity when comparing wind datasets. Results showed that even based on the same data sources, time series were strongly dependent on methods applied subsequently. Application of the datasets within energy system models therefore could present a form of hidden exogenous bias to results. System modelers, who need weather based open license data to perform energy simulations, may be aware of differences in open license datasets available.

GIS-based approach for the evaluation of offshore wind power potential for Gujarat

In the current global scenario, India is increasing its focus towards the methods to enrich the benefits of non-renewable energy sources as much as possible due to their key advantage of having low carbon footprint. India has already emerged as a key global player in on-shore wind energy and to achieve its annual wind energy production demand of 50 GWh, avenues other than current options have been researched on. Offshore wind energy has experienced remarkable growth worldwide but has not yet been harnessed sufficiently in India, despite addressing many of environmental and economic concerns. The present study focuses on offshore wind resource assessment on Indian exclusive economic zone (EEZ) around Gujarat region. The geographical information system (GIS) methodology has been used to develop maps of wind speed, power density and capacity factor maps. Further, careful consideration has been accorded for expulsion of marine protected areas, shipping transportation lines, fishing zones, and migratory bird movements. The resultant available area has been considered for annual energy production considering data from Siemens Wind Turbine 3.6. The results obtained shows that offshore wind energy can offset twice the annual energy demand of entire country with a potential energy production of more than 2580 TWh.

Evaluation of Wind Energy Potential in a University Campus

Today, zero energy campus concept is a popular topic. Zero energy attempts are important for reducing global warming effect and greenhouse gas emissions. In the current study, wind characteristics and wind energy potential of Yildiz Technical University Davupasa Campus were investigated. Data from wind measurement station (10 m height) in campus area was examined. Due to the gaps in the measured data, one year long data was lumped by using all the measured data (25 months). The lumped data was used in all energy analyses. Mean wind speed was calculated as 3.45 m/s at 10 m and north northeast (30°) was defined as prevailing wind direction. Annual wind energy production and capacity factor for Fuhrlander FL 30 (30 kW-27 m hub height) wind turbine were estimated as 28.87 MWh and 10.9%, respectively. According to these results, an economically feasible wind energy project seems not possible. However, a possible wind energy project in Davutpasa campus will help to raise awareness of people. Therefore, it will help and promote to catch the aimed future wind energy capacity of Turkey.

Advanced method for short‐term wind power prediction with multiple observation points using extreme learning machines

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power–speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.

INFLUENCE OF THE CHANGING LOCAL CLIMATE ON WIND POTENTIALS OF MOUNT KOPAONIK

Obtaining all acceptable locations is one of the main tasks for siting wind turbines. The economic factors are usually very limiting. Very thorough analyses are needed in order to ensure the project finalization. Nevertheless, even after all the steps are made, some problems may occur. One of them is the real status of the winds in the so-called climatology period. This paper focuses on the influences of the changing winds after the preliminary estimations are done. The estimations are obtained using the WAsP simulation software. The results are compared in terms of quality and quantity of the wind data and capacity factor. Finally, an economic analysis is done.

Analysis on Wind Energy Status and Capacity Factor of South Korea by EPSIS Wind Power Generation Data

Analysis on Wind Energy Status and Capacity Factor of South Korea by EPSIS Wind Power Generation Data

Asynchrony of wind and hydropower resources in Australia

Wind and hydropower together constitute nearly 80% of the renewable capacity in Australia and their resources are collocated. We show that wind and hydro generation capacity factors covary negatively at the interannual time scales. Thus, the technology diversity mitigates the variability of renewable power generation at the interannual scales. The asynchrony of wind and hydropower resources is explained by the differential impact of the two modes of the El Ni˜no Southern Oscillation – canonical and Modoki – on the wind and hydro resources. Also, the Modoki El Ni˜no and the Modoki La Ni˜na phases have greater impact. The seasonal impact patterns corroborate these results. As the proportion of wind power increases in Australia’s energy mix, this negative covariation has implications for storage capacity of excess wind generation at short time scales and for generation system adequacy at the longer time scales.

Development of a statistical bivariate wind speed-wind shear model (WSWS) to quantify the height-dependent wind resource

The goal of this study was to develop a statistical bivariate wind speed-wind shear model (WSWS). The development of WSWS is based on near surface wind speed data available from 397 measurement stations distributed over Germany, as well as on ERA-Interim reanalysis wind speed data available in 1000m above ground level (a.g.l.). These data were used (1) to calculate empirical distributions of wind speed in 1000ma.g.l., (2) empirical distributions of the wind shear exponent, and (3) to fit theoretical distributions to the empirical wind speed and wind shear exponent distributions. It was found that the four parameter Johnson SB distribution reproduces the shape of the wind speed in 1000ma.g.l. empirical distributions best. The four parameter Dagum distribution provided good fits to the empirical wind shear distributions. The parameterized wind speed and wind shear marginal distributions were then linked by 16 joint copulas. Goodness-of-fit evaluation of the joint copulas demonstrates that the Gaussian-Gaussian copula reproduces the empirical bivariate wind speed-wind shear distribution most accurately. By using WSWS it is possible to continuously calculate the wind speed probability density function in hub heights between 10ma.g.l. and 200ma.g.l. This allows WSWS to be applied to virtually any power curve for computing the wind energy yield and capacity factor in the analyzed hub height range. A one-time site-specific parametrization of WSWS is sufficient for a comprehensive height-dependent exploitation of the available wind resource.