update dev

README.md

@@ -1,5 +1,6 @@
 # EU-SES - EUropean Sustainable Energy System for Python
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4575214.svg)](https://doi.org/10.5281/zenodo.4575214)
+[![article DOI](https://img.shields.io/badge/article-10.12688/openreseurope.2021.04.19-yellow)](https://doi.org/10.12688/openreseurope.13420.1)
 
 EU-SES is an energy system modelling tool that maximises the use of web-hosted pre-processed data sources. EU-SES offers an automated data-processing approach to build energy system models while maintaining the flexibility of selecting the spatial resolution of the model.
 
@@ -51,9 +52,11 @@ techs_removed = np.setdiff1d(example.ds.coords['tech'].values,techs_considered)
 example.ds = example.ds.drop(techs_removed,dim='tech')
 
 # The areas dataset and the preferred regionalisation method is used to build the regions dataset.
-# The supported regionalisation methods include using national (poli_regions) and administrative boundaries (poli_regions_nuts1). It also possible to define the regions using the max-p regions method (max_p_regions).
+# The supported regionalisation methods include using national (nuts0) and administrative boundaries (nuts1). It also possible to define the regions using the max-p regions method (max_p_regions).
 # In this example the national boundaries method is selected.
-example.create_regions('poli_regions')
+# Before the dataset is loaded
+example.ds.load()
+example.create_regions('nuts0')
 
 # Build a power and heat optimisation calliope model with a limitation on CO2 emission.  
 # The specifications of the technologies within the model are in the calliope_model folder.
@@ -94,4 +97,16 @@ This work was done by ESR 5 of the [ENSYSTRA](https://ensystra.eu/) project and
 
 
 # License
-The code is licensed under Creative Commons Attribution Share Alike 4.0 International.
+   Copyright 2021 Christian Fleischer
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

calliope_model/model_config/techs_elec_heat.yaml

@@ -309,14 +309,13 @@ techs:
             parent: transmission
             carrier: power
         constraints:
-            energy_cap_min: 2000
-            energy_cap_max: 8000
+            energy_cap_equals: 2000
             lifetime: 40
             energy_eff: 1
-        costs:
-            monetary:
-                energy_cap_per_distance: 40000 # value in per 100km , 400 €/MWkm https://doi.org/10.1016/j.energy.2017.06.004
-                interest_rate: 0.07
+        # costs:
+        #     monetary:
+        #         energy_cap_per_distance: 40000 # value in per 100km , 400 €/MWkm https://doi.org/10.1016/j.energy.2017.06.004
+        #         interest_rate: 0.07
 
     dc_transmission:
         essentials:

environment.yml

@@ -3,97 +3,22 @@ channels:
   - conda-forge
   - defaults
 dependencies:
+  - pip
+  - calliope=0.6.7
   - gdal=2.4.1
-  - hdf4=4.2.13
-  - hdf5=1.10.4
-  - json-c=0.13.1
-  - numpy=1.19.1
-  - openjpeg=2.3.1
-  - openssl=1.1.1h
-  - pip=20.2.2
-  - xlrd=1.2.0
-  - fiona=1.8.6
-  - rasterio==1.0.25
+  - mapclassify=2.4.0
+  - coincbc=2.10.3
+  - seaborn=0.11.2
   - pip:
-    - affine==2.3.0
-    - appdirs==1.4.4
-    - attrs==20.3.0
-    - backcall==0.2.0
-    - beautifulsoup4==4.9.1
-    - bottleneck==1.3.2
-    - calliope==0.6.5
-    - cftime==1.2.1
-    - chardet==3.0.4
-    - click==7.1.2
-    - click-plugins==1.1.1
-    - cligj==0.5.0
-    - cycler==0.10.0
+    - spopt==0.1.0
     - datapackage==1.15.1
-    - decorator==4.4.2
-    - descartes==1.1.0
-    - geographiclib==1.50
-    - geopandas==0.8.1
+    - numpy==1.19.1
+    - openpyxl==3.0.6
+    - rasterio==1.1.5
+    - rasterstats==0.15.0
     - geopy==2.0.0
-    - idna==2.10
-    - importlib-metadata==1.7.0
-    - ipykernel==5.3.4
-    - ipython==7.17.0
-    - ipython-genutils==0.2.0
-    - jedi==0.17.2
-    - jinja2==3.0.0a1
-    - joblib==0.16.0
-    - jsonschema==3.2.0
-    - jupyter-client==6.1.6
-    - jupyter-core==4.6.3
-    - kiwisolver==1.2.0
-    - libpysal==4.3.0
-    - markupsafe==2.0.0a1
-    - matplotlib==3.3.1
-    - munch==2.5.0
-    - natsort==7.0.1
-    - nbformat==5.0.7
-    - netcdf4==1.5.4
-    - networkx==2.4
-    - nose==1.3.7
-    - numexpr==2.7.1
-    - ortools==7.8.7959
-    - pandas==0.25.0
-    - parso==0.7.1
-    - pexpect==4.8.0
-    - pickleshare==0.7.5
-    - pillow==7.2.0
-    - plotly==3.10.0
-    - ply==3.11
-    - prompt-toolkit==3.0.6
-    - protobuf==3.13.0
-    - ptyprocess==0.6.0
-    - pygments==2.6.1
-    - pyomo==5.6.9
-    - pyparsing==2.4.7
-    - pyproj==2.6.1.post1
-    - pyrsistent==0.16.0
-    - python-dateutil==2.8.1
-    - pytz==2020.1
-    - pyutilib==5.8.0
-    - pyzmq==19.0.2
-    - rasterstats==0.14.0
-    - requests==2.24.0
-    - ruamel-yaml==0.16.10
-    - ruamel-yaml-clib==0.2.0
-    - scikit-learn==0.23.2
-    - seaborn==0.11.1
-    - scipy==1.5.2
-    - shapely==1.7.0
-    - simplejson==3.17.2
-    - six==1.15.0
-    - smopy==0.0.7
-    - snuggs==1.4.7
-    - soupsieve==2.0.1
-    - threadpoolctl==2.1.0
-    - tornado==6.0.4
-    - traitlets==5.0.0b1
-    - urllib3==1.25.10
-    - wcwidth==0.2.5
-    - xarray==0.14.1
-    - zipp==3.1.0
-    - https://github.com/ENSYSTRA/spopt/archive/master.zip
+    - matplotlib==3.4.3
+    - h5netcdf==0.11.0
+    - ipython==7.29.0
+    - xlrd==2.0.1
+    - jupyter==1.0.0

euses/classification.py

@@ -6,45 +6,71 @@
 import numpy as np
 import spopt, libpysal
 
-def wind_offshore_to_nuts2(ds):
+def disaggregation(ds, disaggr_var = 'preset'):
+    if disaggr_var == 'preset':
+        disaggr_var = {'power':'population'}
     time_range = ds.time.values
     dsc = ds.copy()
+    cc_nuts0_id = dict(zip(np.unique(dsc['country_code'].values),dsc.coords['nuts_0'].values))
+    cc_nuts0_id['EL'] = 'GR'
+    cc_nuts0_id['EE'] = 'EE00'
 
     ds['wind_offshore_cf'] = (('nuts_2','time'),(np.array([[t*0.0 for t in range(len(time_range))]]*len(ds.coords['nuts_2']))))
+    ds['power'] = (('nuts_2','time'),(np.array([[t*0.0 for t in range(len(time_range))]]*len(ds.coords['nuts_2']))))
+
+    ds_sum_var = dsc[disaggr_var['power']].groupby(dsc['country_code']).sum()
+    for nuts2_id in dsc.coords['nuts_2']:
+        cc_id = dsc['country_code'].loc[nuts2_id].values.item()
+        sum_var = ds_sum_var.loc[cc_id].item()
+        weighting_factor = dsc[disaggr_var['power']].loc[nuts2_id].values.item()/sum_var
+        power_profile = dsc['power'].loc[cc_nuts0_id[cc_id]] * weighting_factor
+        ds['power'].loc[nuts2_id] = power_profile
 
     ds_c = ds.where(ds['offshore_wind'] > 0, drop = True)
     for nuts2_id in ds_c.coords['nuts_2'].values:
         nuts0_id = ds['country_code'].loc[nuts2_id].values.item()
         ds['wind_offshore_cf'].loc[nuts2_id] = dsc['wind_offshore_cf'].loc[nuts0_id]
 
 
-def aggregation(ds, groups):
+def aggregation(ds, groups, aggr_var = 'preset'):
+
+    if aggr_var == 'preset':
+        aggr_var = {'wind_cf':'onshore_wind','pv_cf':'area_pv','cop_air':'population'}
 
+    aggr_var['wind_offshore_cf']='offshore_wind'
+    aggr_var['hydro_inflow']='hydro_capacity_all'
     dsc = ds.copy()
 
+    dsc['hydro_capacity_all'] = dsc['hydro_capacity'].sum(dim='hydro_tech')
+    dsc['area_pv'] = dsc['rooftop_pv'] + dsc['utility_pv']
+    dsc['area'] = dsc['geometry']
+    dsc['area'].values = [a.area for a in dsc['geometry'].values]
+
     sums_vars = ['power', 'population', 'heat', 'power_plants', 'onshore_wind','offshore_wind',
                   'rooftop_pv','utility_pv','hydro_capacity', 'hydro_storage']
-    area_weighted_vars = ['wind_cf', 'pv_cf', 'wind_offshore_cf',
-                          'cop_air','hydro_inflow']
+    # area_weighted_vars = ['wind_cf', 'pv_cf', 'wind_offshore_cf',
+    #                       'cop_air','hydro_inflow']
 
     sep=','
     new_coords = dsc.coords['nuts_2'].values
     for nuts in groups:
         if nuts[0] in dsc.coords['nuts_2']:
             ds_is =  dsc.sel(nuts_2=nuts)
-            group_area = sum([i.area for i in ds_is['geometry'].values])
-            offshore_area_sum = ds_is['offshore_wind'].sum()
+            # group_area = sum([i.area for i in ds_is['geometry'].values])
+            # offshore_area_sum = ds_is['offshore_wind'].sum()
 
-            for var in area_weighted_vars:
+            for var, weight_var in  aggr_var.items():
+                sum_weighted_var = ds_is[weight_var].sum()
                 for n in nuts:
-                    if var == 'wind_offshore_cf':
-                        ds_is[var].loc[n] = dsc[var].loc[n] * dsc['offshore_wind'].loc[n] / offshore_area_sum
+                    if sum_weighted_var == 0:
+                        ds_is[var].loc[{'nuts_2':n}] = dsc[var].loc[{'nuts_2':n}]
                     else:
-                        ds_is[var].loc[n] = dsc[var].loc[n] * dsc['geometry'].loc[n].values.item().area / group_area
-                dsc[var].loc[nuts[0]] = ds_is[var].sum(axis=0)
+                        ds_is[var].loc[{'nuts_2':n}] = dsc[var].loc[{'nuts_2':n}] * dsc[weight_var].loc[{'nuts_2':n}].sum() / sum_weighted_var
+
+                dsc[var].loc[{'nuts_2':nuts[0]}] = ds_is[var].sum(dim='nuts_2')
 
             for var in sums_vars:
-                dsc[var].loc[nuts[0]] = ds_is[var].sum(axis=0)
+                dsc[var].loc[{'nuts_2':nuts[0]}] = ds_is[var].sum(dim='nuts_2')
 
             dsc['geometry'].loc[nuts[0]] = np.array(str(unary_union(ds_is['geometry'].loc[nuts].values)))