Merge pull request #68 from rl-institut/features/prepare-ror-timeseries

Preparation of ROR timeseries

.gitignore

@@ -8,4 +8,5 @@ docs/_build
 *.egg
 *.egg-info
 dist
-build
+build
+gurobi.log

Snakefile

@@ -64,11 +64,12 @@ rule prepare_feedin:
     input:
         wind_feedin="raw/time_series/ninja_wind_country_DE_current_merra-2_nuts-2_corrected.csv",
         pv_feedin="raw/time_series/ninja_pv_country_DE_merra-2_nuts-2_corrected.csv",
+        ror_feedin="raw/time_series/DIW_Hydro_availability.csv",
         script="scripts/prepare_feedin.py"
     output:
         "results/_resources/feedin_time_series.csv"
     shell:
-        "python {input.script} {input.wind_feedin} {input.pv_feedin} {output}"
+        "python {input.script} {input.wind_feedin} {input.pv_feedin} {input.ror_feedin} {output}"
 
 rule build_datapackage:
     input:
@@ -159,4 +160,4 @@ rule plot_joined_scalars:
     output:
         directory("results/joined_scenarios/{scenario_list}/joined_plotted/")
     shell:
-        "python scripts/plot_joined_scalars.py {input} {output}"
+        "python scripts/plot_joined_scalars.py {input} {output}"

scripts/prepare_feedin.py

@@ -14,20 +14,20 @@
 
 Description
 -------------
-This script prepares wind and pv feed-in time series for the regions Berlin and Brandenburg. Raw
-data is read from csvs from https://www.renewables.ninja/ and then formatted to fit the time series
-template for oemof-B3.
+This script prepares wind, pv and run-of-the-river (ror) feed-in time series for the regions Berlin
+and Brandenburg. Raw data is read from csvs from https://www.renewables.ninja/ (wind+pv) and
+https://zenodo.org/record/1044463 (ror) and is then formatted to fit the time series template of
+oemof-B3 (`schema/timeseries.csv`).
 
 """
 
-# todo  ROR could be included here
-
 import sys
 import pandas as pd
 import os
 import oemof_b3.tools.data_processing as dp
 
 # global variables
+# specific to wind and pv time series
 RE_NINJA_YEARS = list(
     range(2010, 2020)
 )  # re ninja wind+pv time series are prepared for these years
@@ -37,9 +37,14 @@
 TS_VAR_UNIT = "None"
 TS_SOURCE = "https://www.renewables.ninja/"
 TS_COMMENT = "navigate to country Germany"
+# specific to ror time series
+REGIONS = ["BB", "B"]
+TS_SOURCE_ROR = "https://zenodo.org/record/1044463"
+TS_COMMENT_ROR = "Isolated ror availability time series from DIW data"
+YEAR_ROR = 2017
 
 
-def prepare_time_series(filename_ts, year, type):
+def prepare_wind_and_pv_time_series(filename_ts, year, type):
     r"""
     Prepares and formats time series of `type` 'wind' or 'pv' for region 'B' and 'BB'.
 
@@ -55,7 +60,7 @@ def prepare_time_series(filename_ts, year, type):
     Returns
     -------
     ts_prepared : pd.DataFrame
-        Contains time series in the format of template in `filename_template`
+        Contains time series in the format of time series template of oemof-B3
 
     """
     # load raw time series and copy data frame
@@ -87,29 +92,80 @@ def prepare_time_series(filename_ts, year, type):
     return ts_prepared
 
 
+def prepare_ror_time_series(filename_ts, region):
+    r"""
+    Prepares and formats run-of-the-river (ror) time series for region 'B' and 'BB'.
+
+    Parameters
+    ----------
+    filename_ts : str
+        Path including file name to ror time series of DIW Data Documentation 92
+    region : str
+        Region of time series; used for column 'region' in output
+
+    Returns
+    -------
+    ts_prepared : pd.DataFrame
+        Contains time series in the format of time series template of oemof-B3
+
+    """
+    # load raw time series and copy data frame
+    ts_raw = pd.read_csv(filename_ts, index_col=0, skiprows=3, delimiter=";")
+    time_series = ts_raw.copy()
+
+    time_series.index = pd.date_range(
+        f"{YEAR_ROR}-01-01 00:00:00", f"{YEAR_ROR}-12-31 23:00:00", 8760
+    )
+    # bring time series to oemof-B3 format with `stack_timeseries()` and `format_header()`
+    ts_stacked = dp.stack_timeseries(time_series).rename(columns={"var_name": "region"})
+    ts_prepared = dp.format_header(
+        df=ts_stacked, header=dp.HEADER_B3_TS, index_name="id_ts"
+    )
+
+    # add additional information as required by template
+    ts_prepared.loc[:, "region"] = region
+    ts_prepared.loc[:, "var_unit"] = TS_VAR_UNIT
+    ts_prepared.loc[:, "var_name"] = "ror-profile"
+    ts_prepared.loc[:, "source"] = TS_SOURCE_ROR
+    ts_prepared.loc[:, "comment"] = TS_COMMENT_ROR
+    ts_prepared.loc[:, "scenario"] = f"ts_{YEAR_ROR}"
+
+    return ts_prepared
+
+
 if __name__ == "__main__":
     filename_wind = sys.argv[1]
     filename_pv = sys.argv[2]
-    output_file = sys.argv[3]
+    filename_ror = sys.argv[3]
+    output_file = sys.argv[4]
 
     # initialize data frame
     time_series_df = pd.DataFrame()
 
     # prepare time series for each year
     for year in RE_NINJA_YEARS:
         # prepare wind time series
-        wind_ts = prepare_time_series(
+        wind_ts = prepare_wind_and_pv_time_series(
             filename_ts=filename_wind,
             year=year,
             type="wind",
         )
 
         # prepare pv time series
-        pv_ts = prepare_time_series(filename_ts=filename_pv, year=year, type="pv")
+        pv_ts = prepare_wind_and_pv_time_series(
+            filename_ts=filename_pv, year=year, type="pv"
+        )
 
         # add time series to `time_series_df`
         time_series_df = pd.concat([time_series_df, wind_ts, pv_ts], axis=0)
 
+    # prepare ror time series
+    for region in REGIONS:
+        ror_ts = prepare_ror_time_series(filename_ts=filename_ror, region=region)
+
+        # add time series to `time_series_df`
+        time_series_df = pd.concat([time_series_df, ror_ts], axis=0)
+
     # set index
     time_series_df.index = range(0, len(time_series_df))
 

tests/test_prepare_feedin.py

@@ -1,7 +1,7 @@
 import os
 from pandas.util.testing import assert_frame_equal, assert_series_equal
 
-from scripts.prepare_feedin import prepare_time_series
+from scripts.prepare_feedin import prepare_wind_and_pv_time_series
 from oemof_b3.tools.data_processing import load_b3_timeseries, save_df
 
 # Paths
@@ -20,8 +20,8 @@
 )
 
 
-def test_prepare_time_series_wind():
-    df = prepare_time_series(
+def test_prepare_wind_and_pv_time_series_wind():
+    df = prepare_wind_and_pv_time_series(
         filename_ts=filename_wind,
         year=2012,  # in the test files only years 2010, 2012 and 2013 are included
         type="wind",
@@ -46,8 +46,8 @@ def test_prepare_time_series_wind():
     os.remove(temp_filename)
 
 
-def test_prepare_time_series_pv():
-    df = prepare_time_series(
+def test_prepare_wind_and_pv_time_series_pv():
+    df = prepare_wind_and_pv_time_series(
         filename_ts=filename_pv,
         year=2012,  # in the test files only years 2010, 2012 and 2013 are included
         type="pv",