Wind Speed Correlation Research Articles

Evaluation of the WRF-UCM mesoscale model and ECMWF global operational forecasts over the Paris region in the prospect of tracer atmospheric transport modeling

The quantification of CO2 emissions from cities using atmospheric measurements requires accurate knowledge of the atmospheric transport. Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to lower horizontal winds and enhanced convection over urban areas. The question remains whether these processes should be included in atmospheric transport models that are used for city scale CO2 inversion, and whether they need to be tailored on a city basis. In this study, we use the WRF model over Paris to address the following research question: does WRF runs at a 3 km resolution, including urban effects and the assimilation of local weather data, perform better than ECMWF forecasts that give fields at 16 km resolution? The analysis of model performances focuses on three variables: air temperature, wind and the planetary boundary layer (PBL) height. The results show that the use of objective analysis and nudging tools are required to obtain good agreements between WRF simulated fields with observations. Surface temperature is well reproduced by both WRF and ECMWF forecasts, with correlation coefficients with hourly observations larger than 0.92 and MBEs within 1°C over one month. Wind speed correlations with hourly observations are similar for WRF (range 0.76~0.85 across stations) and ECMWF (0.79~0.84), but the associated RMSEs and MBEs are better for ECMWF. Conversely, WRF outperforms ECMWF forecasts for its description of wind direction, horizontal and vertical gradients. Sensitivity tests with different WRF physics schemes show that the wind speed and the PBL height are strongly influenced by PBL schemes. The marginal advantage of WRF over ECMWF for the desired application is sufficient to motive additional testing with prescribed CO2 flux maps for comparing modeled CO2 concentrations with available observations in an urban environment.

Renewable Energy Accommodation Capability Evaluation of Power System with Wind Power and Photovoltaic Integration

Abstract This paper proposes a novel approach to evaluate the renewable energy accommodation capacity (REAC) of power system with wind power and photovoltaic (PV) integration. Based on Copula theory, the wind power output model is proposed considering uncertainty and the spatial correlation of wind speed in different wind farms. The PV output model is presented based on solar irradiance and weather influence factor. The solar irradiance depicts the periodicity and correlation of PV output while the new concept of weather influence factor is introduced to reflect the uncertainty of PV output. And the REAC evaluation model considering reliability of power system with wind power and PV integration is proposed with Monte-Carlo method. The proposed method comprehensively considers the characteristics of renewable energy and evaluates power system REAC more accurately. Furthermore, the influences of multiple factors on REAC of power system are analyzed. Results of case studies based on IEEE Case 39 test system and actual historical renewable energy data of northern China illustrate the feasibility of proposed approach.

Comparison of Seasonal Cycles of Phytoplankton Chlorophyll, Aerosols, Winds and Sea-Surface Temperature off Somalia

In climate research, an important task is to characterise the relationships between Essential Climate Variables (ECVs). Here, satellite-derived data sets have been used to examine the seasonal cycle of phytoplankton (chlorophyll concentration) in the waters off Somalia, and its relationship to aerosols, winds and Sea Surface Temperature (SST). Chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), A° ngstro¨m Exponent (AE), Dust Optical Thickness (DOT), Sea Surface Temperature (SST) and sea-surface wind data for a 16-year period were assembled from various sources. The data were used to explore whether there is evidence in the data to show that dust aerosols enhance Chl-a concentration in the study area. The Cross 11 Correlation Function (CCF) showed highest positive correlation (r2=0.3) in the western Arabian Sea when AOT led Chl-a by 1 to 2 time steps (here, 1 time step is 8 days). A 2o□ 2obox off Somalia was selected for further investigations. The correlations of alongshore wind speed, Ekman Mass Transport (EMT) and SST with Chl-a were higher than that of AOT, for a lag of 8 days. When all four variables were considered together in a multiple linear regression, the increase in r2 associated with the AOT is only about 0.02, a consequence of covariance among AOT, SST, EMT and alongshore wind speed. The AOT data show presence of dust aerosols most frequently during the summer monsoon season (June- September). When the analyses were repeated for the dust aerosol events, the correlations were generally lower, but still significant. Again, the inclusion of DOT in the multiple linear regression increased the correlation coefficient by only 2%, indicating minor enhancement in Chl-a concentration. Interestingly, during summer monsoon season, there is a higher probability of finding more instances of positive changes in Chl-a after one time step, regardless of whether there is dust aerosol or not. On the other hand, during the winter monsoon season (November-December) and rest of the year, the probability of Chl-a enhancement is higher when dust aerosol is present than when dust aerosol is present than when it is absent. The phase relationship in the annual climatologies of Chl-a and AOT

Wind turbulence parameters from three dimensional full-scale measurements at 344 m high guyed mast site Gartow 2

The behavior of structures due to gusty wind loads can be analyzed using statistical concepts in the frequency domain or in the time domain. In both cases, the knowledge about the temporal correlation of the turbulent wind speed in the atmospheric boundary layer at spatially adjacent points is required, especially for large structures such as long-span bridges. In a current project at the Institute of Steel Structures, a new extended wind measuring system based on 48 3D ultra-sonic anemometers is being installed along the guys of a 344 m high measurement mast, with first measured data already available. The probes follow a conical distribution. The measured data will be evaluated regarding to their temporal correlations in the time and frequency domain. The estimated turbulence parameters such as integral time scales and standard deviations of all three turbulence components from more than 2500 10-min. time series in the period from June to August 2015 are presented. It can be demonstrated that the estimated auto spectral density functions of all three turbulence components are in a good agreement with the von Kármán and Dryden models. The estimated cross correlation functions are also in a good agreement with the theoretical model functions.

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system production simulation. Existing power system probabilistic production simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic production simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.

Using probabilistic collocation method for neighbouring wind farms modelling and power flow computation of South Australia grid

In this study, the probabilistic collocation method (PCM) is proposed to construct a stochastic correlation model of wind speeds at neighbouring wind farms and solve probabilistic power flow (PPF) of South Australia (SA) grid. Based on the historical sampled wind source data, the model is developed to reduce the number of uncertain parameters of the power system model by considering the spatial correlation of wind speeds between neighbouring wind farms. Furthermore, this model aims to increase the computational efficiency of PCM when dealing with PPF simulation. Finally, the computation efficiency and accuracy of the PCM, compared with traditional Monte Carlo simulation method, are validated by the simulation results of aggregated power flow model of SA case studies.

A Combined Reliability Model of VSC-HVDC Connected Offshore Wind Farms Considering Wind Speed Correlation

This paper proposes a combined reliability model of voltage source converter-based high voltage direct current (VSC-HVDC) connected offshore wind farms (WFs) using the frequency and duration technique. First, a two-dimensional multistate WF model is developed considering wind speed variations and WTGs outage. The wind speed correlation between different WFs is included in the two-dimensional multistate WF model by using an improved k-means clustering method. Then, the entire system with two WFs and a three-terminal VSC-HVDC system is modeled as a multistate generation unit. The proposed model is applied to the Roy Billinton test system for adequacy studies. Both the probability and frequency indices are calculated. The effectiveness and accuracy of the combined model is validated by comparing results with the sequential Monte Carlo simulation method. The effects of the outage of VSC-HVDC system and wind speed correlation on the system reliability were analyzed. Sensitivity analyses were conducted to investigate the impact of repair time of the offshore VSC-HVDC system on system reliability.

Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies

As the scale of wind power bases rises, it becomes significant in power system planning and operation to provide detailed correlation characteristic of wind speed in different geographical hierarchies, that is among wind turbines, within a wind farm and its regional wind turbines, and among different wind farms. A new approach to analyze the correlation characteristics of wind speed in different geographical hierarchies is proposed in this paper. In the proposed approach, either linear or nonlinear correlation of wind speed in each geographical hierarchy is firstly identified. Then joint sectionalized wind speed probability distribution is modeled for linear correlation analysis while a Copula function is adopted in nonlinear correlation analysis. By this approach, temporal-geographical correlations of wind speed in different geographical hierarchies are properly revealed. Results of case studies based on Jiuquan Wind Power Base in China are analyzed in each geographical hierarchy, which illustrates the feasibility of the proposed approach.

An Observer-Based Controller with a LMI-Based Filter against Wind-Induced Motion for High-Rise Buildings

Active mass damper (AMD) control system is proposed for high-rise buildings to resist a strong wind. However, negative influence of noise in sensors impedes the application of AMD systems in practice. To reduce the adverse influence of noise on AMD systems, a Kalman filter and a linear matrix inequality- (LMI-) based filter are designed. Firstly, a ten-year return period fluctuating wind load is simulated by mixed autoregressive-moving average (MARMA) method, and its reliability is tested by wind speed power spectrum and correlation analysis. Secondly, a designed state observer with different filters uses wind-induced acceleration responses of a high-rise building as the feedback signal that includes noise to calculate control force in this paper. Finally, these methods are applied to a numerical example of a high-rise building and an experiment of a single span four-storey steel frame. Both numerical and experimental results are presented to verify that both Kalman filter and LMI-based filter can effectively suppress noise, but only the latter can guarantee the stability of AMD parameters.

Modelling of wind speed correlation based on entropy weighted fuzzy comprehensive evaluation

Modelling of wind speed correlation based on entropy weighted fuzzy comprehensive evaluation

Culturable bioaerosols along an urban waterfront are primarily associated with coarse particles.

The source, characteristics and transport of viable microbial aerosols in urban centers are topics of significant environmental and public health concern. Recent studies have identified adjacent waterways, and especially polluted waterways, as an important source of microbial aerosols to urban air. The size of these aerosols influences how far they travel, their resistance to environmental stress, and their inhalation potential. In this study, we utilize a cascade impactor and aerosol particle monitor to characterize the size distribution of particles and culturable bacterial and fungal aerosols along the waterfront of a New York City embayment. We seek to address the potential contribution of bacterial aerosols from local sources and to determine how their number, size distribution, and taxonomic identity are affected by wind speed and wind direction (onshore vs. offshore). Total culturable microbial counts were higher under offshore winds (average of 778 CFU/m3 ± 67), with bacteria comprising the majority of colonies (58.5%), as compared to onshore winds (580 CFU/m3 ± 110) where fungi were dominant (87.7%). The majority of cultured bacteria and fungi sampled during both offshore winds (88%) and onshore winds (72%) were associated with coarse aerosols (>2.1 µm), indicative of production from local sources. There was a significant correlation (p < 0.05) of wind speed with both total and coarse culturable microbial aerosol concentrations. Taxonomic analysis, based on DNA sequencing, showed that Actinobacteria was the dominant phylum among aerosol isolates. In particular, Streptomyces and Bacillus, both spore forming genera that are often soil-associated, were abundant under both offshore and onshore wind conditions. Comparisons of bacterial communities present in the bioaerosol sequence libraries revealed that particle size played an important role in microbial aerosol taxonomy. Onshore and offshore coarse libraries were found to be most similar. This study demonstrates that the majority of culturable bacterial aerosols along a New York City waterfront were associated with coarse aerosol particles, highlighting the importance of local sources, and that the taxonomy of culturable aerosol bacteria differed by size fraction and wind direction.

Correlation and coherence of mesoscale wind speeds over the sea

A large offshore observational dataset from stations across the North and Baltic Seas is used to investigate the planetary boundary‐layer wind characteristics and their coherence, correlation and power spectra. The data from thirteen sites, with pairs of sites at horizontal distances of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.Results indicate a general good agreement for the coherence calculated based on measurements and the WRF‐derived time series. By normalizing the frequency axes with the distance and mean wind speed, it can be demonstrated that, even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. However the correlation coefficient as a function of distance calculated from WRF is higher than observed in the measurements. For the power spectra, wind speed and wind speed step change distribution, the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution at each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high‐frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.

Modelling spatially and temporally correlated wind speed time series over a large geographical area using VARMA

This study presents a modified vector auto-regressive moving average (VARMA) modelling procedure to model spatially and temporally correlated wind speed time series over wide geographical areas. The standard VARMA is normally used to model stationary time series with Gaussian distribution. However, wind speed is non-stationary (mean and variance varies over time) and non-Gaussian. Hence, a method that can be used to transform wind speed data into a stationary and Gaussian time series is introduced in the modified procedure. To show the applicability of the procedure for different scenarios, six cases are investigated in the North and the Baltic Sea. The results show that the procedure can be used to model spatially and temporally correlated wind speed over a large geographical area. In addition, the resulting model can capture probability distribution and periodic characteristics of the wind speed data. Furthermore, based on the investigated case, it is shown that a vector auto-regressive model of order three is a reasonable model structure which can be used to model spatially and temporally correlated wind speed in the North and the Baltic Sea area provided that the power transformed wind speed data is normalised by its monthly mean value and its variance.

136-82: Air quality risk factors of hospitalization of patients with atrial fibrillation living in the highly urbanized region of Poland

Purpose of the study: There are some data suggesting that air pollutants and meteorological factors may trigger supraventicular arrhythmias including atrial fibrillation. It seems interesting if there is similar influence in population of highly urbanized and industrialized regions. Therefore, the aim of the study was to determine the impact of air quality risk factors on hospitalization of patients with atrial fibrillation living in the Silesia Agglomeration. Method used: The hospitalizations data were obtained from Silesian Cardiovascular Database for patients living in the Silesia Agglomeration between 2006 and 2014. A total number of hospitalizations with the diagnosis of atrial fibrillation was 20919. The values of meteorological factors and the results of the quantitative analysis of air at each day of the study period were obtained from the Regional Inspectorate for Environmental Protection in Katowice. Further analysis included concentrations of sulphur dioxide (SO2), nitric monoxide (NO) and dioxide (NO2), carbon monoxide (CO), ozone (O3), particulate matter with aerodynamic diameter less than 10 µm (PM10) or less than 2.5 µm (PM2.5), values of temperature (T), humidity (H), wind speed (WS) and atmospheric pressure (AP). Mentioned parameters were compared with the number of hospitalization with the diagnosis of atrial fibrillation. The relationship between the parameters was determined with a generalized linear model with logarithmic joining function, and assumed Poisson distribution for the dependent variable. Summary of results: There was significant relationship between the number of hospitalizations of patients with atrial fibrillation and values of NO, NO2, O3, WS as well as season other than summer (Table 1). No influence of SO2, PM10, PM2.5, T, H and AP was noticed. Factors associated with number of hospitalizations of patients with atrial fibrillation Factors associated with number of hospitalizations of patients with atrial fibrillation Conclusion: In the Silesian Aggolmeration there is considerable correlation of nitric oxides, ozone and wind speed with the frequency of hospitalizations of patients suffering from atrial fibrillation. However, there is a need to conduct more detailed research on this subject.

On the correlation of spatial wind speed and solar irradiance variability above the North Sea

Abstract. Mesoscale wind fluctuations on a time scale of tens of minutes to several hours lead to high wind power fluctuations. Enhanced mesoscale wind variability emerges during cold air outbreaks and resulting cellular convection. The study investigates spatial wind and solar variability and their correlation during cellular convection. Cellular convection leads to simultaneous high solar and wind variability, but the highest solar or wind variability occurs due to other meteorological phenomena.

Global examination of the wind-dependence of very low frequency underwater ambient noise.

Ocean surface winds play a key role in underwater ambient noise generation. One particular frequency band of interest is the infrasonic or very low frequency (VLF) band from 1 to 20 Hz. In this spectral band, wind generated ocean surface waves interact non-linearly to produce acoustic waves, which couple into the seafloor to generate microseisms, as explained by the theory developed by Longuet-Higgins. This study examines long term data sets in the VLF portion of the ambient noise spectrum, collected by the hydroacoustic systems of the Comprehensive Nuclear-Test Ban Treaty Organization in the Atlantic, Pacific, and Indian Oceans. Three properties of the noise field were examined: (a) the behavior of the acoustic spectrum slope from 1 to 5 Hz, (b) correlation of noise levels and wind speeds, and (c) the autocorrelation behavior of both the noise field and the wind. Analysis results indicate the spectrum slope is site dependent, and for both correlation methods, a high correlation between wind and the noise field in the 1-5 Hz band.

Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation

Wind parks always produce diverse percentages of their nominal power at the same time, leading to a concern about correlation between wind speeds. The assessments of wind speed correlation have been particularly focused on probabilistic modeling of aleatory uncertainty. However, poor historical data, imprecise parameter estimation and incomplete knowledge of wind speeds lead to another type of uncertainty, possibilistic uncertainty, which requires an explicit analysis. Therefore, a fuzzy copula model is firstly proposed to express the possibilistic uncertainty of wind speed correlation. The advantage of the proposed model is that the copula parameters can be interval numbers, triangular or trapezoidal fuzzy numbers based on the wind speed data and subjective judgment of decision makers. For estimating copula parameters, a complete decision rule and interval estimation method is developed based on cumulative probability and probability distributions of correlated wind speeds. The effectiveness of the proposed model is validated by the application in wind curtailment evaluation while a method is developed to evaluate and quantify wind curtailment in a hybrid power system involving different types of generation. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty and evaluating its effect on wind curtailment. Compared with previous research, the proposed model develops a new universal parameter estimation method and selection rule to provide more interval results, by calculating the membership function of copula parameters and wind curtailment. System planners and operators can apply the fuzzy results to various topics like reserve capacity evaluation or real-time dispatch depending on their level of risk tolerance.

Impact of Wind Speed Correlation on the Operation of Energy Storage Systems

Renewable resources technologies such as wind power currently demonstrate a worldwide popularity thanks to their environmental friendly status and their economic potential. The variability of the wind power output implies the use of practical solutions such as energy storage systems in order to retain the power system stability and reliability. The wind geographical correlation between different wind farms also impacts the system reliability. This paper studies the effects of the wind speed correlation level on the performance of the associated energy storage system (ESS). Wind correlated model using Weibull probability distribution and Nataf transformation is presented. Energy storage system model and energy management algorithm are developed. Both are applied to a modified IEEE-RTS power generation and load model. The case simulation results indicate that the wind speed correlation level between two wind farms impacts the power distribution inside energy storages and that it needs to be considered in order not to overestimate ESS benefits on the system.

Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage

This paper analyses wind speed and electricity demand correlation to determine the ability of wind turbine generators to meet electricity demand in the Australian National Electricity Market (NEM) without the aid of energy storage. With the proposed increases in the number of windfarms to meet the Large-scale Renewable Energy Target (LRET), this correlation study is formative to identifying price and power stability issues and determining what transmission structure is required to best facilitate the absorption of wind power.We calculate correlations between wind speed and electricity demand data for the years 2010 to 2012 using Weather Research & Forecasting Model (WRF 2015) wind speed data and Australian Energy Market Operator (AEMO) electricity demand data. We calculate state level correlations to identify potential bottlenecks in the interconnectors that link each state’s transmission network. The transmission lines within each state tend to be less of a constraint.We find a small temporal increase in correlation between electricity demand and wind speed. This we attribute to an unwitting renewable energy portfolio effect with the increase in solar PV and solar water heating. Strengthening this portfolio effect is the decline in manufacturing that makes household domestic demand relatively larger. Comparing our study with an earlier correlation analysis by Bannister and Wallace (2011) tends to confirm our initial findings.We find the most advantage from the lack of correlation between wind speed between the NEM’s peripheral states including Queensland, South Australia and Tasmania. Additionally, the correlation between electricity demand and wind speed is strongest between these states. Similarly, we find the most advantage from the lack of correlation between electricity demand in each of these states. The self-interest groups within Victoria and New South Wales and the transmission companies geographically contained within each state hinders the development of optimal interconnector capacity to maximise the benefit of wind power in the peripheral states and the NEM generally.

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator (DFIG) wind turbines. At present, to the best of the authors’ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabilistic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table (COPT), considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.