Pinned Repositories
hydro-power-database
JRC Hydro-power plants database
a-recipe-for-weather-regimes
A recipe in Python to calculate weather regimes
c3s-xskillscore
C3S_evaluator
A simple way to evaluate Copernicus Climate Change (C3S) seasonal forecasts monthly data
ECAD-data-browser
A R Shiny application to explore ECA&D metadata
hydro-sam
a set of R scripts that can be used to scrape and/or process hydropower generation data
metenergy_data
modelling-electricity-generation-with-era5
This repository contains a set of R scripts and the data illustrating the possibility to model hourly electricity generation from renewable sources using the latest climate reanalysis from the Copernicus Climate Change Service (C3S).
panas
Reduce the friction when working with maps & time-series
pypsa-entsoe
Open modelling of European power systems in Python: a proof-of-concept
matteodefelice's Repositories
matteodefelice/pypsa-entsoe
Open modelling of European power systems in Python: a proof-of-concept
matteodefelice/C3S_evaluator
A simple way to evaluate Copernicus Climate Change (C3S) seasonal forecasts monthly data
matteodefelice/a-recipe-for-weather-regimes
A recipe in Python to calculate weather regimes
matteodefelice/hydro-sam
a set of R scripts that can be used to scrape and/or process hydropower generation data
matteodefelice/modelling-electricity-generation-with-era5
This repository contains a set of R scripts and the data illustrating the possibility to model hourly electricity generation from renewable sources using the latest climate reanalysis from the Copernicus Climate Change Service (C3S).
matteodefelice/panas
Reduce the friction when working with maps & time-series
matteodefelice/c3s-xskillscore
matteodefelice/ECAD-data-browser
A R Shiny application to explore ECA&D metadata
matteodefelice/eneaR
Climate lab support functions
matteodefelice/era5-country-averages
Lorem Ipsum
matteodefelice/bboxfinder.com
Helper page for finding bbox values from a map to help with interaction with tools like gdal, leaflet, openlayers, etc.
matteodefelice/era5-correlation-video
Source code for the video https://www.youtube.com/watch?v=xpBijQev-4s
matteodefelice/grib-timeseries-extract
Extract time-series from ERA5-land using political administrative boundaries
matteodefelice/simple-energy-model
matteodefelice/metenergy_data
matteodefelice/Atlas
Code for reproducibility of the products of the AR6 WGI Interactive Atlas
matteodefelice/climate-life-events
Climate history and possible futures showing your important life events
matteodefelice/climate_indices
Climate indices for drought monitoring
matteodefelice/covid
Data analysis on COVID-19
matteodefelice/dash-energy
Testing Dash app on Azure
matteodefelice/Dispa-SET
The Dispa-SET unit-commitment and optimal dispatch model, developed at the JRC
matteodefelice/docker-dash
Docker configuration to develop and deploy a Plotly Dash application
matteodefelice/ERA5-Land-globe-animation
Animation of several ERA5-Land variables on a rotating globe
matteodefelice/Estimating-The-Forward-Electricity-Curve-In-Brazil
Estimating The Forward Electricity Curve In Brazil With A Model Of Two Agents Using Contracts By Difference And ECP_G Function Authors: Felipe Van de Sande Araujo, Cristina Spineti Luz, Leonardo Lima Gomes, Luís Eduardo Teixeira Brandão Abstract: The development of simple and effective mechanisms to estimate the value of the forward curve of power could enable market participants to better price hedging or speculative positions. This could in turn provide transparency in future price definition to all market participants and lead to more safety and liquidity in the market for electricity futures and power derivatives. This work presents a model for two market participants, a buyer and a seller of a contract for difference on the future spot price of electricity in southwest Brazil. It is shown that this model is representative of all market participants that have exposure to the future price of power. Each participant’s utility function is modeled using a Generalized Extended CVaR Preference (ECP_G) and the market equilibrium is obtained through the minimization of the quadratic difference between the certainty equivalent of both agents. The results are compared with prediction of the future spot price of power made by market specialists and found to yield reasonable results when using out of sample data.
matteodefelice/mapama_download
Code (R and Python) to retrieve data from the Redes de Seguimiento del Estado e Información Hidrológica
matteodefelice/ninja_automator
Acquire data with honour and wisdom — using the way of the ninja.
matteodefelice/renewable_test_PSMs
Test power system models for time series & renewable energy analysis
matteodefelice/xskillscore
Metrics for verifying forecasts
matteodefelice/yapos
A simple power system model at daily resolution written in Python
matteodefelice/yaposer
Companion R package for the power systemo model YAPOS