ESIG (Energy Systems Integration Group)’s Post

Weather doesn’t have to be the enemy of reliability. But with increasingly extreme storm systems, grids (often made up of aged infrastructure) across the globe are being strained at unprecedented rates. Find out how leading utilities are actively using weather data to inform real time operations. Plus you get to hear from my super sharp colleague David Stuebe

Next week I'll be presenting Camus Energy's awesome progress on forecasting the electric distribution grid in collaboration with NOAA: National Oceanic & Atmospheric Administration Open Data Dissemination. We will share the details of how we operate the Camus forecast system using a host of open source Python data science tools including https://lnkd.in/edHrzMEd https://lnkd.in/eUtGaFwG https://github.com/fsspec supported by communities like Pangeo, IOOS and Numfocus that make using petabytes of NCEP for machine learning possible at scale.

Mark your calendar - you won't want to miss this webinar! "How NOAA Open Data Dissemination Weather Forecasts are Boosting Grid Operations" More details below 👇

Check out David’s talk about how they are leveraging open source Python tools to build machine learning pipelines for electric grid forecasting. We have been collaborating for the last few years to help make the foundational tools for this process more efficient and accessible, and this talk will explore one of the coolest outcomes.

If you missed the recent webinar, Hot Grid Summer: Unpacking ERCOT's Unusually Warm Summer of 2023, the recording is now available. Learn more and watch the session: https://lnkd.in/eVHazRR5 #cleanenergy #renewableenergy

⚡ Superstatistical Wind Fields from Pointwise Atmospheric Turbulence Measurements | American Physical Society Discover more at "voltsbase.com/energy" #Energy #Voltsbase

[BLOG]: ATMOSPHERIC REANALYSIS, by Elies Campmany 🌏 Atmospheric reanalysis integrates global observational data with model data to create comprehensive, consistent datasets through #DataAssimilation, mirroring methods used in #NumericalWeatherPrediction. Key reanalysis datasets include #CFSR, #MERRA2, and #ERA5, each varying in spatial and temporal resolution. These datasets are important for #WindResource assessment, particularly during greenfield prospecting, because they provide long-term, large-scale data useful for energy assessments and extend short-term observational data. #MesoscaleModeling further enhances the resolution of these datasets via #DynamicDownscaling, significantly improving the accuracy of wind resource assessments. In VORTEX, we employ these techniques to provide more precise and reliable data for #WindEnergy projects, supporting the development and optimization of #RenewableEnergy resources. 👉 Full article: https://hubs.ly/Q02D8zcg0 Stay tuned for more insights! Explore all of our Blogs: https://hubs.ly/Q02D8t9t0 #VortexFdC #RenewableEnergy #WindResource #WindEnergy #WindData

Magnificence of aerodynamics of the wind turbine

Excited to share my first research article on solar wind parameters during significant geomagnetic storms! This study investigates the solar wind parameters during the geomagnetic storms of 26th August 2018 and 28th September 2017, highlighting the variations in solar wind speed, density, and magnetic field components. Understanding these parameters helps improve space weather prediction models. Read the full article here: https://lnkd.in/eqnXBuFV #Research_Article #SolarWind #GeomagneticStorms

My paper describing large-eddy simulation of realistic, nonstationary atmospheric conditions during a wind turbine wake-steering experiment is finally out! I used a microscale data assimilation technique to replicate local measurements in the flow simulation. This work highlights many of the site- and case-specific challenges that may be encountered when trying marry observation and simulation. https://lnkd.in/g4W4_H-D