diff --git a/config/config.default.yaml b/config/config.default.yaml
index 7b4500ae..48c92123 100644
--- a/config/config.default.yaml
+++ b/config/config.default.yaml
@@ -227,6 +227,12 @@ lines:
   max_extension: .inf
   length_factor: 1.25
   under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
+  dynamic_line_rating:
+    activate: false
+    cutout: europe-2013-era5
+    correction_factor: 0.95
+    max_voltage_difference: false
+    max_line_rating: false
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#links
 links:
diff --git a/config/test/config.electricity.yaml b/config/test/config.electricity.yaml
index 6798e38c..b750bf62 100644
--- a/config/test/config.electricity.yaml
+++ b/config/test/config.electricity.yaml
@@ -60,6 +60,12 @@ renewable:
 clustering:
   exclude_carriers: ["OCGT", "offwind-ac", "coal"]
 
+lines:
+  dynamic_line_rating:
+    activate: true
+    cutout: be-03-2013-era5
+    max_line_rating: 1.3
+
 
 solving:
   solver:
diff --git a/config/test/config.myopic.yaml b/config/test/config.myopic.yaml
index 0bb85ec6..87f75777 100644
--- a/config/test/config.myopic.yaml
+++ b/config/test/config.myopic.yaml
@@ -62,6 +62,11 @@ renewable:
   solar:
     cutout: be-03-2013-era5
 
+lines:
+  dynamic_line_rating:
+    activate: true
+    max_line_rating: 1.3
+
 industry:
   St_primary_fraction:
     2030: 0.6
diff --git a/config/test/config.overnight.yaml b/config/test/config.overnight.yaml
index a2a0f5a4..01cdc728 100644
--- a/config/test/config.overnight.yaml
+++ b/config/test/config.overnight.yaml
@@ -59,6 +59,11 @@ renewable:
   solar:
     cutout: be-03-2013-era5
 
+lines:
+  dynamic_line_rating:
+    activate: true
+    max_line_rating: 1.3
+
 sector:
   gas_network: true
   H2_retrofit: true
diff --git a/data/costs.csv b/data/costs.csv
deleted file mode 100644
index 8953eb8a..00000000
--- a/data/costs.csv
+++ /dev/null
@@ -1,195 +0,0 @@
-technology,year,parameter,value,unit,source
-solar-rooftop,2030,discount rate,0.04,per unit,standard for decentral
-onwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-solar,2030,lifetime,25,years,IEA2010
-solar-rooftop,2030,lifetime,25,years,IEA2010
-solar-utility,2030,lifetime,25,years,IEA2010
-PHS,2030,lifetime,80,years,IEA2010
-hydro,2030,lifetime,80,years,IEA2010
-ror,2030,lifetime,80,years,IEA2010
-OCGT,2030,lifetime,30,years,IEA2010
-nuclear,2030,lifetime,45,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
-CCGT,2030,lifetime,30,years,IEA2010
-coal,2030,lifetime,40,years,IEA2010
-lignite,2030,lifetime,40,years,IEA2010
-geothermal,2030,lifetime,40,years,IEA2010
-biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
-oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,investment,1040,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind-ac-station,2030,investment,250,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind-ac-connection-submarine,2030,investment,2685,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind-ac-connection-underground,2030,investment,1342,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind-dc-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
-offwind-dc-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
-offwind-dc-connection-underground,2030,investment,1000,EUR/MW/km,Haertel 2017; average + 13% learning reduction
-solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-coal,2030,investment,1300,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
-lignite,2030,investment,1500,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-solar-rooftop,2030,investment,725,EUR/kWel,ETIP PV
-solar-utility,2030,investment,425,EUR/kWel,ETIP PV
-PHS,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-hydro,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-ror,2030,investment,3000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-OCGT,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-nuclear,2030,investment,6000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-CCGT,2030,investment,800,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-oil,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,FOM,2.450549,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-solar,2030,FOM,4.166667,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-solar-rooftop,2030,FOM,2,%/year,ETIP PV
-solar-utility,2030,FOM,3,%/year,ETIP PV
-biomass,2030,FOM,4.526935,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-geothermal,2030,FOM,2.358491,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-coal,2030,FOM,1.923076,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
-lignite,2030,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
-oil,2030,FOM,1.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-PHS,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-hydro,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-ror,2030,FOM,2,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-CCGT,2030,FOM,2.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-OCGT,2030,FOM,3.75,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,VOM,2.3,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-solar,2030,VOM,0.01,EUR/MWhel,RES costs made up to fix curtailment order
-coal,2030,VOM,6,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
-lignite,2030,VOM,7,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
-CCGT,2030,VOM,4,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
-OCGT,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
-nuclear,2030,VOM,8,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
-gas,2030,fuel,21.6,EUR/MWhth,IEA2011b
-uranium,2030,fuel,3,EUR/MWhth,DIW DataDoc http://hdl.handle.net/10419/80348
-oil,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
-nuclear,2030,fuel,3,EUR/MWhth,IEA2011b
-biomass,2030,fuel,7,EUR/MWhth,IEA2011b
-coal,2030,fuel,8.4,EUR/MWhth,IEA2011b
-lignite,2030,fuel,2.9,EUR/MWhth,IEA2011b
-oil,2030,fuel,50,EUR/MWhth,IEA WEM2017 97USD/boe = http://www.iea.org/media/weowebsite/2017/WEM_Documentation_WEO2017.pdf
-PHS,2030,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-hydro,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-ror,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-OCGT,2030,efficiency,0.39,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-CCGT,2030,efficiency,0.5,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-biomass,2030,efficiency,0.468,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-geothermal,2030,efficiency,0.239,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-nuclear,2030,efficiency,0.337,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-gas,2030,CO2 intensity,0.187,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
-coal,2030,efficiency,0.464,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
-lignite,2030,efficiency,0.447,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
-oil,2030,efficiency,0.393,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 CT
-coal,2030,CO2 intensity,0.354,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
-lignite,2030,CO2 intensity,0.334,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
-oil,2030,CO2 intensity,0.248,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
-geothermal,2030,CO2 intensity,0.026,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
-electrolysis,2030,investment,350,EUR/kWel,Palzer Thesis
-electrolysis,2030,FOM,4,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-electrolysis,2030,lifetime,18,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-electrolysis,2030,efficiency,0.8,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-fuel cell,2030,investment,339,EUR/kWel,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-fuel cell,2030,FOM,3,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-fuel cell,2030,lifetime,20,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
-fuel cell,2030,efficiency,0.58,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013 conservative 2020
-hydrogen storage,2030,investment,11.2,USD/kWh,budischak2013
-hydrogen storage,2030,lifetime,20,years,budischak2013
-hydrogen underground storage,2030,investment,0.5,EUR/kWh,maximum from https://www.nrel.gov/docs/fy10osti/46719.pdf
-hydrogen underground storage,2030,lifetime,40,years,http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Materialien/ESYS_Technologiesteckbrief_Energiespeicher.pdf
-H2 pipeline,2030,investment,267,EUR/MW/km,Welder et al https://doi.org/10.1016/j.ijhydene.2018.12.156
-H2 pipeline,2030,lifetime,40,years,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
-H2 pipeline,2030,FOM,5,%/year,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
-H2 pipeline,2030,efficiency,0.98,per unit,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
-methanation,2030,investment,1000,EUR/kWH2,Schaber thesis
-methanation,2030,lifetime,25,years,Schaber thesis
-methanation,2030,FOM,3,%/year,Schaber thesis
-methanation,2030,efficiency,0.6,per unit,Palzer; Breyer for DAC
-helmeth,2030,investment,1000,EUR/kW,no source
-helmeth,2030,lifetime,25,years,no source
-helmeth,2030,FOM,3,%/year,no source
-helmeth,2030,efficiency,0.8,per unit,HELMETH press release
-DAC,2030,investment,250,EUR/(tCO2/a),Fasihi/Climeworks
-DAC,2030,lifetime,30,years,Fasihi
-DAC,2030,FOM,4,%/year,Fasihi
-battery inverter,2030,investment,411,USD/kWel,budischak2013
-battery inverter,2030,lifetime,20,years,budischak2013
-battery inverter,2030,efficiency,0.9,per unit charge/discharge,budischak2013; Lund and Kempton (2008) http://dx.doi.org/10.1016/j.enpol.2008.06.007
-battery inverter,2030,FOM,3,%/year,budischak2013
-battery storage,2030,investment,192,USD/kWh,budischak2013
-battery storage,2030,lifetime,15,years,budischak2013
-decentral air-sourced heat pump,2030,investment,1050,EUR/kWth,HP; Palzer thesis
-decentral air-sourced heat pump,2030,lifetime,20,years,HP; Palzer thesis
-decentral air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
-decentral air-sourced heat pump,2030,efficiency,3,per unit,default for costs
-decentral air-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
-decentral ground-sourced heat pump,2030,investment,1400,EUR/kWth,Palzer thesis
-decentral ground-sourced heat pump,2030,lifetime,20,years,Palzer thesis
-decentral ground-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
-decentral ground-sourced heat pump,2030,efficiency,4,per unit,default for costs
-decentral ground-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
-central air-sourced heat pump,2030,investment,700,EUR/kWth,Palzer thesis
-central air-sourced heat pump,2030,lifetime,20,years,Palzer thesis
-central air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
-central air-sourced heat pump,2030,efficiency,3,per unit,default for costs
-retrofitting I,2030,discount rate,0.04,per unit,Palzer thesis
-retrofitting I,2030,lifetime,50,years,Palzer thesis
-retrofitting I,2030,FOM,1,%/year,Palzer thesis
-retrofitting I,2030,investment,50,EUR/m2/fraction reduction,Palzer thesis
-retrofitting II,2030,discount rate,0.04,per unit,Palzer thesis
-retrofitting II,2030,lifetime,50,years,Palzer thesis
-retrofitting II,2030,FOM,1,%/year,Palzer thesis
-retrofitting II,2030,investment,250,EUR/m2/fraction reduction,Palzer thesis
-water tank charger,2030,efficiency,0.9,per unit,HP
-water tank discharger,2030,efficiency,0.9,per unit,HP
-decentral water tank storage,2030,investment,860,EUR/m3,IWES Interaktion
-decentral water tank storage,2030,FOM,1,%/year,HP
-decentral water tank storage,2030,lifetime,20,years,HP
-decentral water tank storage,2030,discount rate,0.04,per unit,Palzer thesis
-central water tank storage,2030,investment,30,EUR/m3,IWES Interaktion
-central water tank storage,2030,FOM,1,%/year,HP
-central water tank storage,2030,lifetime,40,years,HP
-decentral resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
-decentral resistive heater,2030,lifetime,20,years,Schaber thesis
-decentral resistive heater,2030,FOM,2,%/year,Schaber thesis
-decentral resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
-decentral resistive heater,2030,discount rate,0.04,per unit,Palzer thesis
-central resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
-central resistive heater,2030,lifetime,20,years,Schaber thesis
-central resistive heater,2030,FOM,2,%/year,Schaber thesis
-central resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
-decentral gas boiler,2030,investment,175,EUR/kWhth,Palzer thesis
-decentral gas boiler,2030,lifetime,20,years,Palzer thesis
-decentral gas boiler,2030,FOM,2,%/year,Palzer thesis
-decentral gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
-decentral gas boiler,2030,discount rate,0.04,per unit,Palzer thesis
-central gas boiler,2030,investment,63,EUR/kWhth,Palzer thesis
-central gas boiler,2030,lifetime,22,years,Palzer thesis
-central gas boiler,2030,FOM,1,%/year,Palzer thesis
-central gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
-decentral CHP,2030,lifetime,25,years,HP
-decentral CHP,2030,investment,1400,EUR/kWel,HP
-decentral CHP,2030,FOM,3,%/year,HP
-decentral CHP,2030,discount rate,0.04,per unit,Palzer thesis
-central CHP,2030,lifetime,25,years,HP
-central CHP,2030,investment,650,EUR/kWel,HP
-central CHP,2030,FOM,3,%/year,HP
-decentral solar thermal,2030,discount rate,0.04,per unit,Palzer thesis
-decentral solar thermal,2030,FOM,1.3,%/year,HP
-decentral solar thermal,2030,investment,270000,EUR/1000m2,HP
-decentral solar thermal,2030,lifetime,20,years,HP
-central solar thermal,2030,FOM,1.4,%/year,HP
-central solar thermal,2030,investment,140000,EUR/1000m2,HP
-central solar thermal,2030,lifetime,20,years,HP
-HVAC overhead,2030,investment,400,EUR/MW/km,Hagspiel
-HVAC overhead,2030,lifetime,40,years,Hagspiel
-HVAC overhead,2030,FOM,2,%/year,Hagspiel
-HVDC overhead,2030,investment,400,EUR/MW/km,Hagspiel
-HVDC overhead,2030,lifetime,40,years,Hagspiel
-HVDC overhead,2030,FOM,2,%/year,Hagspiel
-HVDC submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
-HVDC submarine,2030,lifetime,40,years,Hagspiel
-HVDC submarine,2030,FOM,2,%/year,Hagspiel
-HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel
-HVDC inverter pair,2030,lifetime,40,years,Hagspiel
-HVDC inverter pair,2030,FOM,2,%/year,Hagspiel
diff --git a/doc/configtables/lines.csv b/doc/configtables/lines.csv
index ad5dd690..ec9ec007 100644
--- a/doc/configtables/lines.csv
+++ b/doc/configtables/lines.csv
@@ -5,3 +5,9 @@ s_nom_max,MW,"float","Global upper limit for the maximum capacity of each extend
 max_extension,MW,"float","Upper limit for the extended capacity of each extendable line."
 length_factor,--,float,"Correction factor to account for the fact that buses are *not* connected by lines through air-line distance."
 under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction."
+dynamic_line_rating,,,
+-- activate,bool,"true or false","Whether to take dynamic line rating into account"
+-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
+-- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data"
+-- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable"
+-- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable"
diff --git a/envs/environment.yaml b/envs/environment.yaml
index 7e119a2f..8409adb0 100644
--- a/envs/environment.yaml
+++ b/envs/environment.yaml
@@ -53,6 +53,7 @@ dependencies:
 - descartes
 - rasterio!=1.2.10
 
+
 - pip:
   - tsam>=1.1.0
   - git+https://github.com/PyPSA/PyPSA.git@master
diff --git a/rules/build_electricity.smk b/rules/build_electricity.smk
index c7ef86a2..6964a87d 100644
--- a/rules/build_electricity.smk
+++ b/rules/build_electricity.smk
@@ -274,6 +274,30 @@ rule build_hydro_profile:
         "../scripts/build_hydro_profile.py"
 
 
+if config["lines"]["dynamic_line_rating"]["activate"]:
+
+    rule build_line_rating:
+        input:
+            base_network=RESOURCES + "networks/base.nc",
+            cutout="cutouts/"
+            + CDIR
+            + config["lines"]["dynamic_line_rating"]["cutout"]
+            + ".nc",
+        output:
+            output=RESOURCES + "networks/line_rating.nc",
+        log:
+            LOGS + "build_line_rating.log",
+        benchmark:
+            BENCHMARKS + "build_line_rating"
+        threads: ATLITE_NPROCESSES
+        resources:
+            mem_mb=ATLITE_NPROCESSES * 1000,
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/build_line_rating.py"
+
+
 rule add_electricity:
     params:
         length_factor=config["lines"]["length_factor"],
@@ -295,6 +319,9 @@ rule add_electricity:
             if str(fn).startswith("data/")
         },
         base_network=RESOURCES + "networks/base.nc",
+        line_rating=RESOURCES + "networks/line_rating.nc"
+        if config["lines"]["dynamic_line_rating"]["activate"]
+        else RESOURCES + "networks/base.nc",
         tech_costs=COSTS,
         regions=RESOURCES + "regions_onshore.geojson",
         powerplants=RESOURCES + "powerplants.csv",
diff --git a/scripts/add_electricity.py b/scripts/add_electricity.py
index 68d2d5fa..24567477 100755
--- a/scripts/add_electricity.py
+++ b/scripts/add_electricity.py
@@ -2,8 +2,6 @@
 # SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: MIT
-
-# coding: utf-8
 """
 Adds electrical generators and existing hydro storage units to a base network.
 
@@ -752,6 +750,30 @@ def estimate_renewable_capacities(n, year, tech_map, expansion_limit, countries)
             )
 
 
+def attach_line_rating(
+    n, rating, s_max_pu, correction_factor, max_voltage_difference, max_line_rating
+):
+    # TODO: Only considers overhead lines
+    n.lines_t.s_max_pu = (rating / n.lines.s_nom[rating.columns]) * correction_factor
+    if max_voltage_difference:
+        x_pu = (
+            n.lines.type.map(n.line_types["x_per_length"])
+            * n.lines.length
+            / (n.lines.v_nom**2)
+        )
+        # need to clip here as cap values might be below 1
+        # -> would mean the line cannot be operated at actual given pessimistic ampacity
+        s_max_pu_cap = (
+            np.deg2rad(max_voltage_difference) / (x_pu * n.lines.s_nom)
+        ).clip(lower=1)
+        n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(
+            lower=1, upper=s_max_pu_cap, axis=1
+        )
+    if max_line_rating:
+        n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(upper=max_line_rating)
+    n.lines_t.s_max_pu *= s_max_pu
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -834,6 +856,23 @@ if __name__ == "__main__":
 
     update_p_nom_max(n)
 
+    line_rating_config = snakemake.config["lines"]["dynamic_line_rating"]
+    if line_rating_config["activate"]:
+        rating = xr.open_dataarray(snakemake.input.line_rating).to_pandas().transpose()
+        s_max_pu = snakemake.config["lines"]["s_max_pu"]
+        correction_factor = line_rating_config["correction_factor"]
+        max_voltage_difference = line_rating_config["max_voltage_difference"]
+        max_line_rating = line_rating_config["max_line_rating"]
+
+        attach_line_rating(
+            n,
+            rating,
+            s_max_pu,
+            correction_factor,
+            max_voltage_difference,
+            max_line_rating,
+        )
+
     sanitize_carriers(n, snakemake.config)
 
     n.meta = snakemake.config
diff --git a/scripts/build_line_rating.py b/scripts/build_line_rating.py
new file mode 100755
index 00000000..7ece1c9d
--- /dev/null
+++ b/scripts/build_line_rating.py
@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+
+# coding: utf-8
+"""
+Adds dynamic line rating timeseries to the base network.
+
+Relevant Settings
+-----------------
+
+.. code:: yaml
+
+    lines:
+        cutout:
+        line_rating:
+
+
+.. seealso::
+    Documentation of the configuration file ``config.yaml`
+Inputs
+------
+
+- ``data/cutouts``:
+- ``networks/base.nc``: confer :ref:`base`
+
+Outputs
+-------
+
+- ``resources/line_rating.nc``
+
+
+Description
+-----------
+
+The rule :mod:`build_line_rating` calculates the line rating for transmission lines.
+The line rating provides the maximal capacity of a transmission line considering the heat exchange with the environment.
+
+The following heat gains and losses are considered:
+
+- heat gain through resistive losses
+- heat gain through solar radiation
+- heat loss through radiation of the trasnmission line
+- heat loss through forced convection with wind
+- heat loss through natural convection
+
+
+With a heat balance considering the maximum temperature threshold of the transmission line,
+the maximal possible capacity factor "s_max_pu" for each transmission line at each time step is calculated.
+"""
+
+import logging
+import re
+
+import atlite
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import pypsa
+import xarray as xr
+from _helpers import configure_logging
+from shapely.geometry import LineString as Line
+from shapely.geometry import Point
+
+
+def calculate_resistance(T, R_ref, T_ref=293, alpha=0.00403):
+    """
+    Calculates the resistance at other temperatures than the reference
+    temperature.
+
+    Parameters
+    ----------
+    T : Temperature at which resistance is calculated in [°C] or [K]
+    R_ref : Resistance at reference temperature in [Ohm] or [Ohm/Per Length Unit]
+    T_ref : Reference temperature in [°C] or [K]
+    alpha: Temperature coefficient in [1/K]
+        Defaults are:
+            * T_ref : 20 °C
+            * alpha : 0.00403 1/K
+
+    Returns
+    -------
+    Resistance of at given temperature.
+    """
+    R = R_ref * (1 + alpha * (T - T_ref))
+    return R
+
+
+def calculate_line_rating(n, cutout):
+    """
+    Calculates the maximal allowed power flow in each line for each time step
+    considering the maximal temperature.
+
+    Parameters
+    ----------
+    n : pypsa.Network object containing information on grid
+
+    Returns
+    -------
+    xarray DataArray object with maximal power.
+    """
+    relevant_lines = n.lines[(n.lines["underground"] == False)]
+    buses = relevant_lines[["bus0", "bus1"]].values
+    x = n.buses.x
+    y = n.buses.y
+    shapes = [Line([Point(x[b0], y[b0]), Point(x[b1], y[b1])]) for (b0, b1) in buses]
+    shapes = gpd.GeoSeries(shapes, index=relevant_lines.index)
+    if relevant_lines.r_pu.eq(0).all():
+        # Overwrite standard line resistance with line resistance obtained from line type
+        r_per_length = n.line_types["r_per_length"]
+        R = (
+            relevant_lines.join(r_per_length, on=["type"])["r_per_length"] / 1000
+        )  # in meters
+        # If line type with bundles is given retrieve number of conductors per bundle
+        relevant_lines["n_bundle"] = (
+            relevant_lines["type"]
+            .where(relevant_lines["type"].str.contains("bundle"))
+            .dropna()
+            .apply(lambda x: int(re.findall(r"(\d+)-bundle", x)[0]))
+        )
+        # Set default number of bundles per line
+        relevant_lines["n_bundle"].fillna(1, inplace=True)
+        R *= relevant_lines["n_bundle"]
+        R = calculate_resistance(T=353, R_ref=R)
+    Imax = cutout.line_rating(shapes, R, D=0.0218, Ts=353, epsilon=0.8, alpha=0.8)
+    line_factor = relevant_lines.eval("v_nom * n_bundle * num_parallel") / 1e3  # in mW
+    da = xr.DataArray(
+        data=np.sqrt(3) * Imax * line_factor.values.reshape(-1, 1),
+        attrs=dict(
+            description="Maximal possible power in MW for given line considering line rating"
+        ),
+    )
+    return da
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_line_rating",
+            network="elec",
+            simpl="",
+            clusters="5",
+            ll="v1.0",
+            opts="Co2L-4H",
+        )
+    configure_logging(snakemake)
+
+    n = pypsa.Network(snakemake.input.base_network)
+    cutout = atlite.Cutout(snakemake.input.cutout)
+
+    da = calculate_line_rating(n, cutout)
+    da.to_netcdf(snakemake.output[0])