Collection of scripts to download & process hydropower generation data in Argentina, Bolivia, Brazil, Uruguay
This repository contains a set of R scripts that can be used to scrape and/or process hydropower generation data from the websites of the following system operators:
- CNDC, Comité Nacional de Despacho de Carga (Bolivia): https://www.cndc.bo/home/index.php
- ONS, Operador Nacional do Sistema Elétrico (Brazil): https://www.ons.org.br/
- UTE, Usinas y Trasmisiones Eléctricas (Uruguay): https://www.ute.com.uy/
- CAMMESA, Compañía Administradora del Mercado Mayorista Eléctrico Sociedad Anónima (Argentina): https://cammesaweb.cammesa.com
The data obtained from this code has been used in the report of the Joint Research Centre of the European Commission: "Extreme and long-term drought in the La Plata Basin: event evolution and impact assessment until September 2022" available here.
The code is very simple and not polished, however I think it might be useful to obtain fundamental data from four south American power systems.
A snapshot of the raw & processed data is archived on Zenodo and available here: https://doi.org/10.5281/zenodo.7644349
The entire dataset contains more than 50 000 rows for the four countries. This is a summary for the data based on the latest snapshot (February 2023).
| Country | time range | time resolution | number of variables (area) per country |
|---|---|---|---|
| ARG | 2015 - 2022 | daily | 24 |
| BOL | 2010 - 2022 | daily | 10 |
| BRA | 2017 - 2023 | weekly | 6 |
| URU | 2010 - 2022 | daily | 1 |
The data must be downloaded from the Descargar Informes Históricos section of the website. Two files must be downloaded:
- The latest
BASE INFORME MENSUALthat is named according to the latest month available (currently isBASE_INFORME_MENSUAL_2022-12.zip) - The file
Base Informe Mensual 2018 12containing all the data until 2018
The script ARG-process.R extract the data saving this table in RDS format:
# A tibble: 2,030 × 3
date area GWh
<dttm> <chr> <dbl>
1 2015-01-01 00:00:00 ARG-ADTOHI 29.2
2 2015-01-01 00:00:00 ARG-ALICHI 114.
3 2015-01-01 00:00:00 ARG-ARROHI 37.6
4 2015-01-01 00:00:00 ARG-ARROHINU 2.83
5 2015-01-01 00:00:00 ARG-CACHHI 41.9
6 2015-01-01 00:00:00 ARG-CCOLHI 0
7 2015-01-01 00:00:00 ARG-CCORHI 6.56
8 2015-01-01 00:00:00 ARG-CHOCHI 164.
9 2015-01-01 00:00:00 ARG-CONDHI 22.2
10 2015-01-01 00:00:00 ARG-CPIEHI 22.2
# … with 2,020 more rows
The script BOL-download.R download the files and the script BOL-process.R extract the time-series. The second script will save in RDS format a table in the following format:
# A tibble: 94,794 × 3
date area GWh
<dttm> <chr> <dbl>
1 2010-01-01 00:00:00 BOL-ZONGO 3.80
2 2010-01-02 00:00:00 BOL-ZONGO 3.80
3 2010-01-03 00:00:00 BOL-ZONGO 3.82
4 2010-01-04 00:00:00 BOL-ZONGO 3.80
5 2010-01-05 00:00:00 BOL-ZONGO 3.44
6 2010-01-06 00:00:00 BOL-ZONGO 3.31
7 2010-01-07 00:00:00 BOL-ZONGO 3.31
8 2010-01-08 00:00:00 BOL-ZONGO 3.28
9 2010-01-09 00:00:00 BOL-ZONGO 3.24
10 2010-01-10 00:00:00 BOL-ZONGO 3.26
# … with 94,784 more rows
The script BRA-download.R download the files and the script BRA-process.R extract the time-series. The second script will save in RDS format a table in the following format:
# A tibble: 1,908 × 3
area GWh date
<chr> <dbl> <date>
1 BRA-Norte 2590. 2022-01-01
2 BRA-Nordeste 559. 2022-01-01
3 BRA-Sul 506. 2022-01-01
4 BRA-Sudeste/Centro-Oeste 4026. 2022-01-01
5 BRA-Itaipu 648. 2022-01-01
6 BRA-Sistema Interligado Nacional 8329. 2022-01-01
7 BRA-Norte 1881. 2020-02-01
8 BRA-Nordeste 517. 2020-02-01
9 BRA-Sul 646. 2020-02-01
10 BRA-Sudeste/Centro-Oeste 4964. 2020-02-01
# … with 1,898 more rows
Unfortunately, the raw data must be exported manually from this webpage selecting each time a different time range (max 1 year). The data table is the one under the tab "Energia"> "Histórico Composición Energética", you must select the time range, then click "Aplicar" on the right and "Exportar" on the bottom. The website is sometimes slow in providing the data.
The script URU-process.R extract the time-series from the raw data saving this table in RDS format:
# A tibble: 4,407 × 3
date area GWh
<dttm> <chr> <dbl>
1 2011-07-24 00:00:00 URU 24.3
2 2011-07-23 00:00:00 URU 28.0
3 2011-07-22 00:00:00 URU 29.0
4 2011-07-21 00:00:00 URU 29.4
5 2011-07-20 00:00:00 URU 28.6
6 2011-07-19 00:00:00 URU 28.4
7 2011-07-17 00:00:00 URU 23.9
8 2011-07-16 00:00:00 URU 22.5
9 2011-07-15 00:00:00 URU 23.7
10 2011-07-14 00:00:00 URU 24.6
# … with 4,397 more rows