EDF Renewables

The coronavirus 2019 (COVID 19, or SARS-CoV-2) pandemic that started in December 2019 has caused an unprecedented impact in most countries globally and continues to threaten human lives worldwide. The COVID-19 and strict lockdown measures have had adverse effects on human health and national economies. These lockdown measures have played a critical role in improving air quality, water quality, and the ozone layer and reducing greenhouse gas emissions. Using Soil Moisture Active Passive (SMAP) Level 4 carbon (SMAP LC4) satellite products, this study investigated the impacts of COVID-19 lockdown measures on annual carbon emissions globally, focusing on 47 greatly affected countries and their 105 cities by December 2020. It is shown that while the lockdown measures significantly reduced carbon emissions globally, several countries and cities observed this reduction as temporary because strict lockdown measures were not imposed for extended periods in 2020. Overall, the total carbon emissions of select 184 countries reduced by 438 Mt in 2020 than in 2019. Since the global economic activities are slowly expected to return to the non-COVID-19 state, the reduction in carbon emissions during the pandemic will not be sustainable in the long run. For sustainability, concerned authorities have to put significant efforts to change transportation, climate, and environmental policies globally that fuel carbon emissions. Overall, the presented results provide directions to the stakeholders and policymakers to develop and implement measures to control carbon emissions for a sustainable environment.

Abstract. With the planned construction of vast offshore wind farms along the US East Coast, identifying and understanding key coastal processes, such as sea breezes, has become a critical need for the sustainability and development of US offshore wind energy. In this study, a new two-step identification method is proposed to detect and characterize three types of sea breezes (pure, corkscrew and backdoor) over the US northeastern coast from a year-long WRF (Weather Research and Forecasting) simulation. The results suggest that the proposed detection method can identify the three different types of sea breezes in the model simulation. Key sea breeze features, such as the calm zone associated with pure sea breezes and coastal jets associated with corkscrew sea breezes, are evident in the sea breeze composite imagery. In addition, the simulated sea breeze events indicate a seasonal transition from pure to corkscrew sea breeze between March and August as the land–sea thermal contrast increases. Furthermore, the location and extension of the sea breeze front are different for each type of sea breeze, suggesting that the coastal impact of sea breeze varies with sea breeze type. From the wind energy perspective, the power production associated with a 10 MW offshore wind turbine would be approximately 3 to 4 times larger during a corkscrew sea breeze event than the other two types of sea breezes. This highlights the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.

Abstract The impacts of wind energy on bat populations is a growing concern because wind turbine blades can strike and kill bats, and wind turbine development is increasing. We tested the effectiveness of 2 management actions at 2 wind‐energy facilities for reducing bat fatalities: curtailing turbine operation when wind speeds were <5.0 m/second and combining curtailment with an acoustic bat deterrent developed by NRG Systems. We measured the effectiveness of the management actions using differences in counts of bat carcasses quantified by daily and twice‐per‐week standardized carcass searches of cleared plots below turbines, and field trials that estimated searcher efficiency and carcass persistence. We studied turbines located at 2 adjacent wind‐energy facilities in northeast Illinois, USA, during fall migration (1 Aug–15 Oct) in 2018. We estimated the effectiveness of each management action using a generalized linear mixed‐effects model with several covariates. Curtailment alone reduced overall bat mortality by 42.5% but did not reduce silver‐haired bat ( Lasionycteris noctivagans ) mortality. Overall bat fatality rates were 66.9% lower at curtailed turbines with acoustic deterrents compared to turbines that operated at manufacturer cut‐in speed. Curtailment and the deterrent reduced bat mortality to varying degrees between species, ranging from 58.1% for eastern red bats ( Lasiurus borealis ) to 94.4 for big brown bats ( Eptesicus fuscus ). Hoary ( Lasiurus cinereus ) and silver‐haired bat mortality was reduced by 71.4% and 71.6%, respectively. Our study lacked a deterrent‐only treatment group because of the expense of acoustic deterrents. We estimated the additional reduction in mortality with concurrent deployment of the acoustic deterrent and curtailment under the assumption that curtailment and the acoustic deterrent would have reduced mortality by the same percentage at adjacent wind‐energy facilities. Acoustic deterrents resulted in 31.6%, 17.4%, and 66.7% additional reductions of bat mortality compared to curtailment alone for eastern red bat, hoary bat, and silver‐haired bat, respectively. The effectiveness of acoustic deterrents for reducing bat mortality at turbines with rotor‐swept area diameters >110 m is unknown because high frequency sound attenuates quickly, which reduces coverage of rotor‐swept areas. Management actions should consider species differences in the ability of curtailment and deterrents to reduce bat mortality and increase energy production.

High quality satellite solar irradiation data is used throughout the solar industry to perform energy estimates. The uncertainty of the raw satellite data has been shown to be low. Ground data is often used to correct satellite data but determining the uncertainty of the final dataset could be challenging since the traditional statistical uncertainty and error calculation methods have proven to be unrepresentative. In this paper the limitations of traditional statistical methods are explored along with alternative approaches to calculate a more representative uncertainty value for a long term dataset resulting from ground corrected satellite data.

This repository collects input and simulation datasets from the Offshore Wind Accelerator (OWA) Wake Modelling Challenge, whose objective is to improve confidence in wake models in the prediction of array efficiency. The data is meant to be used together with the open-source model evaluation scripts available in the following github repository: https://github.com/CENER-EPR/OWAbench The results of the challenge are summarized in the following paper: Sanz Rodrigo J, Borbón Guillén F, Fernandes Correia P M, García Hevia B, Schlez W, Schmidt S, Basu S, Li B, Nielsen P, Cathelain M, Dall’Ozzo C, Grignon L, Pullinger D (2020) Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. J. Phys.: Conf. Ser., under review

The study of wind turbine noise and its impact is of growing importance with the increase in the demand for green and clean energy. As it is known that wind turbine noise can be a cause of annoyance in the vicinity of wind farms it is beneficial to predict with certain accuracy the generated noise in the design phase itself. A crucial step is the validation of prediction models against field measurements (in-situ). This article presents a wind turbine noise prediction model that combines Amiet's theory to calculate trailing edge noise and turbulence interaction noise in free field with a wide-angle parabolic equation valid in moving media to account for the long-range acoustic propagation effects. The model considers the wind turbine as an extended noise source and the rotation effects (such as the convective amplification and Doppler effect) are taken into account. The predicted noise levels are compared to those obtained from a measurement campaign where acoustic, meteorological and ground impedance data have been recorded simultaneously. First, the sound source model is validated close to the wind turbines for different wind speeds and directions. Then, noise predictions are compared to SPL measurements at various distances from the sound source, between 350 and 1300meters.

Predicting the noise radiated by a wind farm needs to take into account many parameters such as wind turbine operational conditions, wind and temperature profiles, atmospheric turbulence, ground impedance and topography.In this study, we aim at validating a wind turbine noise prediction model that combines Amiet's theory to calculate trailing edge noise and turbulence interaction noise with a wide-angle parabolic equation valid in moving media to account for long range acoustic propagation effects.The model considers the wind turbine as an extended and rotating noise source.The model predictions are compared to field measurements recorded during ten days around an eight-turbine single row wind farm.As the terrain is flat and the roughness is relatively homogeneous, the meteorological lidar and a mast data are supposed to be rangeindependent.Using representative values for the ground parameters, the model gives the correct interference patterns in the third octave band spectrum.Accurate predictions of the third octave band spectra averaged over 10 minutes are obtained for propagation distances up to 1300 meters, although the influence of background noise becomes more significant as the distance increases.

EDFR has developed a series of methods for quickly drafting a set of utility scale photovoltaic plant layouts and choosing an optimized plant design from that set. The automated drafting methodology utilizes a standard clustering technique and a novel cluster equalizing post-processing algorithm to solve a problem in existing automated drafting software, which is that existing techniques cannot assign dc power in the form of trackers to inverters without human intervention. This step is crucial to create end to end automation of a PV plant layout. Without it, it is impossible to accurately determine the layout of dc wiring and associated electrical equipment. The work nearly eliminates the need for developer drafting of utility scale photovoltaic plant layouts and provides a foundation for reducing levelized cost of energy by allowing EDFR to select the most financially optimal project design without investing large amounts of time creating the feasibility space under which optimization can occur.

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We performed an 11-month field test to examine soiling and irradiance measurements in a bifacial PV system. We demonstrate two methods, denoted “module-module” and “module-cell”, of determining soiling ratio - the ratio of actual power to expected power under clean conditions - using soiled module power measurements. In both methods we use in-situ module I-V measurement to directly measure power output of a soiled reference module within the plant, yielding the numerator of soiling ratio. In the module-module method, for the denominator of soiling ratio, the expected module power, we use measurements from another reference module that is routinely cleaned. In the module-cell method, for the denominator we use module power estimated from front and rear-side cleaned reference cells. We find that the total irradiance calculated from the combination of front and rear reference cells correlates well with the module short-circuit current and maximum power. Furthermore, we find that the module-module and module-cell soiling ratio measurement methods produce nearly identical results during the field test. These results support using automatically washed or soiling-compensated reference cells instead of full-size modules as a clean reference, simplifying instrumentation and operations and maintenance requirements.

We present a study plan to investigate uncertainties in soiling and irradiance measurement in a bifacial photovoltaic (PV) power plant using in-situ I-V measurements of module power. Soiling ratio is the ratio of actual module power output to expected power under clean conditions. However, precise determination of expected module power output for clean conditions is challenging for bifacial systems because of the need to account not only for front-side but also for rear-side irradiance contributions which have greater sources of variability. We present plans for a recently initiated field test to assess two methods of determining soiling ratio. In both methods we will use in-situ module I-V measurement to directly measure power output of a soiled reference module within the plant, yielding the numerator of soiling ratio. In method one, for the denominator of soiling ratio, the expected module power, we will use power measured from another reference module that is routinely cleaned. In method two, for the denominator we will use module power estimated from front and rear-side cleaned reference cells. Our aim is to determine the precision of soiling ratio measurements in a bifacial system given the challenge of normalizing for rear-side irradiance. For both methods we plan to quantify drift in the soiling ratio baseline corresponding to zero soiling loss and use this as a measure of soiling ratio precision.

<strong class="journal-contentHeaderColor">Abstract.</strong> With the planned construction of vast offshore wind farms along the US East Coast, identifying and understanding key coastal processes, such as sea breezes, has become a critical need for the sustainability and development of US offshore wind energy. In this study, a new two-step identification method is proposed to detect and characterize three types of sea breezes (pure, corkscrew and backdoor) over the US northeastern coast from a year-long WRF (Weather Research and Forecasting) simulation. The results suggest that the proposed detection method can identify the three different types of sea breezes in the model simulation. Key sea breeze features, such as the calm zone associated with pure sea breezes and coastal jets associated with corkscrew sea breezes, are evident in the sea breeze composite imagery. In addition, the simulated sea breeze events indicate a seasonal transition from pure to corkscrew sea breeze between March and August as the land–sea thermal contrast increases. Furthermore, the location and extension of the sea breeze front are different for each type of sea breeze, suggesting that the coastal impact of sea breeze varies with sea breeze type. From the wind energy perspective, the power production associated with a 10 MW offshore wind turbine would be approximately 3 to 4 times larger during a corkscrew sea breeze event than the other two types of sea breezes. This highlights the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.

This repository collects input and simulation datasets from the Offshore Wind Accelerator (OWA) Wake Modelling Challenge, whose objective is to improve confidence in wake models in the prediction of array efficiency. The data is meant to be used together with the open-source model evaluation scripts available in the following github repository: https://github.com/CENER-EPR/OWAbench The results of the challenge are summarized in the following paper: Sanz Rodrigo J, Borbón Guillén F, Fernandes Correia P M, García Hevia B, Schlez W, Schmidt S, Basu S, Li B, Nielsen P, Cathelain M, Dall’Ozzo C, Grignon L, Pullinger D (2020) Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. J. Phys.: Conf. Ser., under review

<strong class="journal-contentHeaderColor">Abstract.</strong> With the planned construction of vast offshore wind farms along the US East Coast, identifying and understanding key coastal processes, such as sea breezes, has become a critical need for the sustainability and development of US offshore wind energy. In this study, a new two-step identification method is proposed to detect and characterize three types of sea breezes (pure, corkscrew and backdoor) over the US northeastern coast from a year-long WRF (Weather Research and Forecasting) simulation. The results suggest that the proposed detection method can identify the three different types of sea breezes in the model simulation. Key sea breeze features, such as the calm zone associated with pure sea breezes and coastal jets associated with corkscrew sea breezes, are evident in the sea breeze composite imagery. In addition, the simulated sea breeze events indicate a seasonal transition from pure to corkscrew sea breeze between March and August as the land–sea thermal contrast increases. Furthermore, the location and extension of the sea breeze front are different for each type of sea breeze, suggesting that the coastal impact of sea breeze varies with sea breeze type. From the wind energy perspective, the power production associated with a 10 MW offshore wind turbine would be approximately 3 to 4 times larger during a corkscrew sea breeze event than the other two types of sea breezes. This highlights the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.

This paper discusses the impact of inverter-based resources (IBRs) in traditional digital protection relays applied in the interconnection transmission line between the IBR and bulk power system. Real events involving a photovoltaic (PV) power plant are used to show the behavior of the fault currents, which is different from power systems with synchronous generators, especially for negative- sequence components. The paper discusses how to properly handle this kind of source by presenting modern protective relays features, time-domain functions, and special settings for traditional protection intelligent electronic devices (IEDs).

Electricity generation assets need to withstand climatological hazards all along their operating period. With the ongoing climate change, high temperature extremes are expected to increase, therefore, climate change needs to be accounted for in the estimations of extreme temperature levels at the design stage.This study showcases a methodology designed to compute maps of daily maximum temperature return levels in summer over the continental USA by 2050 and the end of the century. The methodology first consists in building a variable whose extremes can be considered as stationary in order to then apply the statistical Extreme Value Theory to compute return levels. Previous studies (Parey et al., 2013) had shown that once the trends in mean and standard deviation are removed, the extremes of the reduced variable can be considered as stationary. The reduced variable is thus computed for daily maximum temperatures at each grid point across the continental USA in summer using the ERA5 reanalysis over the 1950-2014 period. Then, once the desired return level is estimated for this variable, temperature levels are obtained by re-introducing the removed information about the mean and the standard deviation of summer temperature at the desired horizon (Parey et al., 2013). To do so, a set of 9 CMIP6 climate models with 3 emission scenarios, SSP1-2.6, SSP2-4.5 and SSP3-7.0, is considered. For each time horizon, 27 extreme summer temperature maps are produced. Then, a criterium is designed to sum up the information and decide whether two different maps give significantly different results. Finally, once the criterium is applied to each pair of maps, either scenario by scenario or all scenarios together, a classification is applied to identify groups of statistically different maps.&amp;#160;&amp;#160;References:Parey S., Hoang TTH, Dacunha-Castelle D.: The importance of mean and variance in predicting changes in temperature extremes, Journal of Geophysical Research: Atmospheres, Vol 118, 1-12, 2013, doi:10.1002/jgrd.50629Parey S., Hoang T.T.H., Dacunha-Castelle D.: Future high temperature extremes and stationarity, Natural Hazards, 2019, https://doi.org/10.1007/s11069-018-3499-

This work aims to perform an assessment of cell cracks impact on PV module’s performances. Cracks can occur during transportation, severe weather conditions such as hailstorms [1], poor installation practices and manufacturing [2]. Even with multiwire interconnection technologies, the impact of such defects can affect PV module’s performances as well as the energy yield and lifetime of a photovoltaic installation [3]. Moreover, cracks will leave the silicon edge unpassivated with a risk of being more sensitive to external stresses, especially on highly efficient technologies like TOPCon. The main objective of this work is studying the cracks impact on new module’s technology performance to quantify its impact on the production yield and forecast power plant performance. A laboratory testing sequence was performed on two different Glass/backsheet PV technologies: PERC and TOPCon. First, a cracking procedure was conducted to generate several cracks patterns on two batches of PV modules similar to the patterns observed on powerplants sites. An electrical characterization was then performed to carry out a first evaluation of power losses after the cracks. Moreover, several accelerated aging sequences were conducted on the cracked and reference modules to quantify the average losses after each accelerated aging test.