Aschach hydro-power plant, Austria (source Wikopedia, Kraftwerk Aschach 1.jpg)

Hydrological data and Hydropower

Using C3S EFAS data to estimate run-of-river generation

Modelling (and thus understanding) the generation of renewable energy sources is very important, especially considering their link with meteorological variables. In this blog post I would like to focus on run-of-river hydropower, a common source of electricity in Europe.

This notebook demonstrates the potential in using free/open data to analyse & model run-of-river generation. I will use three different datasets:

  1. The JRC Open Power Plants Database (JRC-PPDB-OPEN) to know the coordinates of the most important plants in Europe. The dataset is open and it can be downloaded from this link
  2. The time-series of the single units from the ENTSO-E Transparency Platform. The Transparency Platform can be accessed from the website or via SFTP after a registration
  3. River discharge historical data from the European Flood Awareness System (EFAS) available on the Copernicus Data Store. The data is freely available after a registration.

I cannot share the entire workflow because the size of the datasets used is too big: except for the JRC-PPDB-OPEN the other two are more than 50 GBs.

Coordinates and generation time-series

The first step is to extract from the Transparency Platform the time-series of the largest run-of-river plants in Europe, finding then their coordinates using the JRC-PPDB-OPEN.

From the power plants database we get the 20 run-of-river power plants with the highest installed capacity (capacity_p).

units <- read_csv("JRC-PPDB-OPEN.ver0.91/JRC_OPEN_UNITS.csv",
  col_types = cols(
    eic_p = col_character(),
    eic_g = col_character(),
    name_p = col_character(),
    name_g = col_character(),
    capacity_p = col_double(),
    capacity_g = col_double(),
    type_g = col_character(),
    lat = col_double(),
    lon = col_double(),
    country = col_character(),
    NUTS2 = col_character(),
    status_g = col_character(),
    year_commissioned = col_double(),
    year_decommissioned = col_double()
  )
) %>%
  dplyr::filter(type_g == "Hydro Run-of-river and poundage") %>%
  distinct(name_p, .keep_all = TRUE) %>%
  arrange(-capacity_p) %>%
  select(eic_p, eic_g, name_p, country, capacity_p, lat, lon, status_g) %>%
  top_n(20, capacity_p)
kable(units)
eic_p eic_g name_p country capacity_p lat lon status_g
30W-CHE-PDF1—A 30WHIDRPDFE3—H HPP Portile de Fier I Romania 1161.0 44.673 22.532 COMMISSIONED
34WEHP-DJE-1—R 34WEHP-DJE-1G3-L HE DJERDAP I Serbia 1098.9 44.668 22.527 COMMISSIONED
43W-PLAV-HPP—J 43W-PLAV-HG4—U Plavinas HPP Latvia 894.0 56.583 25.239 COMMISSIONED
24WV–HGA——8 NA Gabčíkovo Slovakia 720.0 47.880 17.538 COMMISSIONED
26WIMPIGROSIO-11 26WPREM-GROSIO10 GROSIO Italy 655.0 46.286 10.265 COMMISSIONED
48WSTN00000CRUAC 48W000000CRUA-40 CRUA United Kingdom 440.0 56.394 -5.115 COMMISSIONED
17W100P100P0319C NA GENISSIAT France 420.0 46.053 5.813 COMMISSIONED
34WEHP-BBASH—V 34WEHP-BBASHG4-M HE BAJINA BASTA Serbia 420.0 43.965 19.410 COMMISSIONED
43W-RIG-HPP—-1 NA Riga HPP Latvia 402.0 56.950 24.105 COMMISSIONED
26WIMPIORTICA-13 26WUUUUUORTICA1F TIMPAGRANDE Italy 395.0 39.176 16.781 COMMISSIONED
48WSTN00000FFESK 48W000000FFES-1S FFES United Kingdom 384.0 53.118 -4.103 COMMISSIONED
17W100P100P0324J NA DONZERE MONDRAGON France 348.0 44.304 4.742 COMMISSIONED
14W-BAW-KW—–F 14W-BAW-TU—–V Altenwörth Austria 328.0 48.375 15.855 COMMISSIONED
26WIMPITOADDA-1T 26WUUUALTOADDA15 VENINA Italy 327.0 45.441 12.316 COMMISSIONED
48WSTN00000FOYEQ 48W000000FOYE-22 FOYE United Kingdom 320.0 57.246 -4.490 COMMISSIONED
17W100P100P0323L NA MONTELIMAR France 295.0 44.593 4.726 COMMISSIONED
14W-BGS-KW—–Q 14W-BGS-TU—–5 Greifenstein Austria 293.0 48.355 16.242 COMMISSIONED
17W100P100P0302T 17W100P100P0049F CHASTANG France 293.0 45.151 2.010 COMMISSIONED
14W-BAS-KW—–I 14W-BAS-TU—–Y Aschach Austria 287.4 48.385 14.023 COMMISSIONED
34WEHP-DJE-2—M NA HE DJERDAP II Serbia 270.0 44.302 22.562 COMMISSIONED

Then I have extracted the generation time-series from the Transparency Platform. This dataset can be explored and downloaded directly on the website in the section Actual Generation per Generation Unit. Obviously, if you need all the historical data of multiple plants, the only option is the bulk download via SFTP (in principle, this can be achieved also through the restful API provided but I have never tried).

From the SFTP you will obtain a bunch of CSV files, one for each month of data. From this tabular data the needed plants can be extracted filtering the rows where the EIC code of the unit (GenerationUnitEIC) is in our list (eic_g column). A final postprocess is needed: the data in the Transparency Platform is hourly while the hydrological data we will use in the next step is daily, then the time-series are aggregated at daily level obtaining the data in MWh.

I have extracted the data in a tibble and saved to a CSV file (you can download it from this link ).

entsoe_tp_data <- read_csv("ENTSOE-TP-time-series-top-20-ror-europe.csv",
  col_types = cols(
    date = col_date(format = ""),
    eic_g = col_character(),
    output = col_double()
  )
) %>%
  group_by(eic_g) %>%
  summarise(
    `number of daily samples` = n(),
    `average daily generation (MWh)` = mean(output, na.rm = TRUE)
  )
kable((entsoe_tp_data))
eic_g number of daily samples average daily generation (MWh)
14W-BAS-TU—–Y 1659 4317.5324
14W-BAW-TU—–V 1659 5256.2183
14W-BGS-TU—–5 1659 4574.5451
17W100P100P0049F 1388 386.1680
26WPREM-GROSIO10 1707 4015.5589
26WUUUALTOADDA15 1707 1275.6479
26WUUUUUORTICA1F 1676 1474.2351
30WHIDRPDFE3—H 1696 2266.5841
34WEHP-BBASHG4-M 5 859.5000
34WEHP-DJE-1G3-L 5 2309.1160
43W-PLAV-HG4—U 1709 604.6829
48W000000CRUA-40 1038 247.5113
48W000000FFES-1S 826 196.4877
48W000000FOYE-22 1225 721.9635

Although we have selected the 20 power plants with the highest capacity in the JRC-PPDB-OPEN, in the Transparency Platform we could find the time-series for only 14 power plants (and two of them have only 5 samples).

The rest of this post will focus only on one plant, but the shown procedure can be applied in batch to multiple plants.

Let’s visualise the daily generation time-series for the Aschach hydro-power plant on the Danube in Austria. In the following figure we can see the daily generation (black line) and the rolling average for 30 days (blue line).

selected_plant <- read_csv("ENTSOE-TP-time-series-top-20-ror-europe.csv",
  col_types = cols(
    date = col_date(format = ""),
    eic_g = col_character(),
    output = col_double()
  )
) %>%
  dplyr::filter(eic_g == "14W-BAS-TU-----Y") %>%
  ggplot(aes(x = date, y = output)) +
  geom_line(size = 0.1) +
  geom_line(aes(y = zoo::rollmean(output, k = 30, fill = NA)), color = "blue") +
  theme_light()
print(selected_plant)

png

River discharge from EFAS and its correlation with the generation

As specified in the dataset page, the EFAS historical data provides estimate of river discharge from 1991 with a resolution of 5 km. Using Python and the powerful xarray module we can open the entire EFAS dataset and extract the the time-series for a single power plant. Given that the coordinate of the power plant can be inaccurate, I don’t select the grid point containing the power plant’s coordinate but also the 8 neighbours, then for each time step, selecting the maximum discharge of those 9 grid points.

The Python code is available on this Github gist.

Now, let’s go back to the Aschach hydro-power plant. The Python script saved the time-series of the river discharge in the coordinate 48.385, 14.023 in a CSV file. Now we can join the generation data with the river discharge.

sel_unit <- units %>% dplyr::filter(eic_g == "14W-BAS-TU-----Y")

generation_data <- selected_plant <- read_csv("ENTSOE-TP-time-series-top-20-ror-europe.csv",
  col_types = cols(
    date = col_date(format = ""),
    eic_g = col_character(),
    output = col_double()
  )
) %>%
  dplyr::filter(eic_g == "14W-BAS-TU-----Y")

discharge_data <- read_csv(sprintf("out-%s_2010-2018.csv", sel_unit$eic_p),
  col_types = cols(
    time = col_datetime(format = ""),
    step = col_character(),
    surface = col_double(),
    valid_time = col_datetime(format = ""),
    dis24 = col_double()
  )
) %>%
  select(time, dis24) %>%
  mutate(time = lubridate::floor_date(time, "days") %>% lubridate::as_date())

joined_data <- inner_join(
  generation_data,
  discharge_data,
  by = c("date" = "time")
) %>%
  dplyr::filter(!is.na(output), !is.na(dis24))
kable(head(joined_data))
date eic_g output dis24
2015-01-04 14W-BAS-TU—–Y 244.0 1171.059
2015-01-05 14W-BAS-TU—–Y 6326.7 1272.953
2015-01-06 14W-BAS-TU—–Y 6454.2 1220.108
2015-01-07 14W-BAS-TU—–Y 5527.0 1142.998
2015-01-08 14W-BAS-TU—–Y 4770.6 1075.707
2015-01-09 14W-BAS-TU—–Y 5055.8 1296.062

Now we can visualise the two time-series, applying a normalisation because they have two different unit measures: MWh for the generation and cubic meters per second for the river discharge.

compare_plot <- ggplot(
  data = joined_data %>%
    select(date, generation = output, discharge = dis24) %>%
    gather(variable, value, -date) %>%
    group_by(variable) %>%
    mutate(value = scale(value)),
  aes(x = date, y = value, color = variable)
) +
  geom_line() +
  theme_light()
print(compare_plot)

png The Spearman correlation of the two variables is 0.88 and also the plots shows the level of their correlation. And the correlation is also high for many other power plants, for example for Laufenburg hydropower station is even 0.92.

What are the potential applications?

  1. For power system modeling this means that we are able to estimate the generation of run-of-river power plants with also the possibility to create a synthetic dataset for the entire EFAS historical timespan (1990-2018)
  2. Also, we can extend this correlation to the climate change scenarios to simulate the generation of run-of-river in different scenarios (and for different models)
  3. Weather and seasonal forecasts might be used to provide(operational) forecasts of run-of-river generation (not an easy task though)
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