From 99e3557fda89f41b38f01838c5bffe46de1f0a64 Mon Sep 17 00:00:00 2001 From: Tetens Date: Wed, 19 Jul 2023 11:19:33 +0200 Subject: [PATCH 01/27] Added data documentation for gas, methane and H2 grid --- docs/data/gas_grids.rst | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/docs/data/gas_grids.rst b/docs/data/gas_grids.rst index ee113f03d..36b9497ff 100644 --- a/docs/data/gas_grids.rst +++ b/docs/data/gas_grids.rst @@ -1,7 +1,20 @@ -Information about the gas grids and how they were created +The gas grid data stems from the SciGRID_gas project and covers the European Gas +Transmission Grid. All data generated in the SciGRID_gas project is licenced under +*Creative Commons Attribution 4.0 International Public License*. +The specific dataset version is IGGIELGN and can be downloaded at https://zenodo.org/record/4767098. +SciGRID_gas contains extensive data on pipelines, storages, LNGs, productions, consumers and more. +Further information can be obtained in the IGGIELGN documentation. +For eGon-data, SciGRID_gas infrastructure data in Germany has been extracted and used in full resolution +while data of neighboring countries has been aggregated. + Methane grid ~~~~~~~~~~~~ +In the eGon2035 scenario the methane grid is, apart from minor adjustments, equivalent to the gas grid described in the +SciGRID_gas IGGIELGN dataset. Hydrogen grid ~~~~~~~~~~~~~ +In the eGon2035 scenario H2 nodes are present at every methane bus (H2_grid) and at locations where there +are possible H2 cavern storages available (H2_saltcavern). There is no explicit H2 pipeline grid available but H2 can +be transported using the methane grid. \ No newline at end of file From 20af1c09c1a2c98997b176475a4b0820e51e47cf Mon Sep 17 00:00:00 2001 From: Tetens Date: Thu, 20 Jul 2023 09:06:22 +0200 Subject: [PATCH 02/27] Added gas related dataset documentation --- docs/data/gas_grids.rst | 2 +- src/egon/data/datasets/ch4_storages.py | 2 +- .../data/datasets/gas_neighbours/__init__.py | 16 ++++++++++++++++ src/egon/data/datasets/power_etrago/__init__.py | 11 +++++++++++ src/egon/data/datasets/saltcavern/__init__.py | 11 +++++++++++ 5 files changed, 40 insertions(+), 2 deletions(-) diff --git a/docs/data/gas_grids.rst b/docs/data/gas_grids.rst index 36b9497ff..0be162be3 100644 --- a/docs/data/gas_grids.rst +++ b/docs/data/gas_grids.rst @@ -1,4 +1,4 @@ -The gas grid data stems from the SciGRID_gas project and covers the European Gas +The gas grid data stems from the SciGRID_gas project (https://www.gas.scigrid.de/) which covers the European Gas Transmission Grid. All data generated in the SciGRID_gas project is licenced under *Creative Commons Attribution 4.0 International Public License*. The specific dataset version is IGGIELGN and can be downloaded at https://zenodo.org/record/4767098. diff --git a/src/egon/data/datasets/ch4_storages.py b/src/egon/data/datasets/ch4_storages.py index 2b10b2072..a1a2a47af 100755 --- a/src/egon/data/datasets/ch4_storages.py +++ b/src/egon/data/datasets/ch4_storages.py @@ -35,7 +35,7 @@ class CH4Storages(Dataset): *Dependencies* * :py:class:`GasAreaseGon2035 ` - * :py:class:`GasAreaseGon2035 ` + * :py:class:`GasAreaseGon100RE ` * :py:class:`GasNodesAndPipes ` *Resulting tables* diff --git a/src/egon/data/datasets/gas_neighbours/__init__.py b/src/egon/data/datasets/gas_neighbours/__init__.py index 7732553b1..303b5a3bb 100755 --- a/src/egon/data/datasets/gas_neighbours/__init__.py +++ b/src/egon/data/datasets/gas_neighbours/__init__.py @@ -14,6 +14,22 @@ class GasNeighbours(Dataset): + """ + Inserts generation, demand, grid, OCGTs and gas neighbors into database. + + *Dependencies* + * :py:class:`GasNodesAndPipes ` + * :py:class:`PypsaEurSec ` + * :py:class:`ElectricalNeighbours ` + * :py:class:`HydrogenBusEtrago ` + * :py:class:`GasAreaseGon100RE ` + + *Resulting tables* + * :py:class:`grid.egon_etrago_link ` is extended + * :py:class:`grid.egon_etrago_load ` is extended + * :py:class:`grid.egon_etrago_generator ` is extended + + """ def __init__(self, dependencies): super().__init__( name="GasNeighbours", diff --git a/src/egon/data/datasets/power_etrago/__init__.py b/src/egon/data/datasets/power_etrago/__init__.py index 862271fa6..e43058619 100755 --- a/src/egon/data/datasets/power_etrago/__init__.py +++ b/src/egon/data/datasets/power_etrago/__init__.py @@ -7,6 +7,17 @@ class OpenCycleGasTurbineEtrago(Dataset): + """ + Insert open cycle gas turbines (OCGT) into the database. + + *Dependencies* + * :py:class:`GasAreaseGon2035 ` + * :py:class:`PowerPlants ` + + *Resulting tables* + * :py:class:`grid.egon_etrago_link ` is extended + + """ def __init__(self, dependencies): super().__init__( name="OpenCycleGasTurbineEtrago", diff --git a/src/egon/data/datasets/saltcavern/__init__.py b/src/egon/data/datasets/saltcavern/__init__.py index ac1f040cc..848495a87 100644 --- a/src/egon/data/datasets/saltcavern/__init__.py +++ b/src/egon/data/datasets/saltcavern/__init__.py @@ -89,6 +89,17 @@ def to_postgres(): class SaltcavernData(Dataset): + """Inserts Saltcavern shapes into database + + *Dependencies* + * :py:class:`DataBundle ` + * :py:class:`Vg250 ` + + *Resulting tables* + * :py:class:`EgonPfHvGasVoronoi ` + + """ + def __init__(self, dependencies): super().__init__( name="SaltcavernData", From 1a5b783c27d383bd972fcb6d454b53b1f9978fba Mon Sep 17 00:00:00 2001 From: Tetens Date: Fri, 21 Jul 2023 10:20:11 +0200 Subject: [PATCH 03/27] Added rtd Data documentation for gas sector --- docs/data/gas_demand.rst | 21 ++++++++++++++++++++- docs/data/gas_grids.rst | 10 +++++----- docs/data/gas_stores.rst | 10 ++++++++-- docs/data/gas_supply.rst | 12 +++++++++++- 4 files changed, 44 insertions(+), 9 deletions(-) diff --git a/docs/data/gas_demand.rst b/docs/data/gas_demand.rst index 072c66394..561130936 100644 --- a/docs/data/gas_demand.rst +++ b/docs/data/gas_demand.rst @@ -1 +1,20 @@ -Information about gas demands and their spatial and temporal aggregation, including hydrogen and methane demands +In the scenario eGon2035 the gas demand data in Germany stems from the eXtremOS project +(https://openaccess.ffe.de/10.34805/ffe-24-21/) where demands are +provided on a NUTS-3 level and hourly resolution for the year 2035. +These include methane and hydrogen demands for industry. +For the neighboring countries there are no hydrogen nodes available. Instead respective +hydrogen demands for industry are indirectly modelled as electricity demands. The total +hydrogen demand is derived from the *Distributed Energy Scenario* of the *TYNDP 2020* and +has been linearly interpolated between the years 2030 and 2040. The demands are temporarily +constant. +The methane demands of neighboring countries accounts for industry demands and +heating demands. The total demand data stems also from the *Distributed Energy Scenario* of the *TYNDP 2020* +interpolated between 2030 and 2040 while ites temporal profile is derived from the *PyPSA-eur-sec* run +because of the high share of rural heating in total methane demand. + +For the scenario eGon100RE the methane and hydrogen demands for industry in Germany +have been calculated in a *PyPSA-eur-sec* run. The spatial and temporal distribution +accords to the hydrogen industry demands of the eXtremOS project for the year 2050. +In eGon100RE no industrial methane demand is assumed. +For the neighboring countries the industrial gas demands (methane and hydrogen) stem from +the *PyPSA-eur-sec* run. \ No newline at end of file diff --git a/docs/data/gas_grids.rst b/docs/data/gas_grids.rst index 0be162be3..101af4ba0 100644 --- a/docs/data/gas_grids.rst +++ b/docs/data/gas_grids.rst @@ -1,17 +1,17 @@ -The gas grid data stems from the SciGRID_gas project (https://www.gas.scigrid.de/) which covers the European Gas -Transmission Grid. All data generated in the SciGRID_gas project is licenced under +The gas grid data stems from the *SciGRID_gas project* (https://www.gas.scigrid.de/) which covers the European Gas +Transmission Grid. All data generated in the *SciGRID_gas* project is licenced under *Creative Commons Attribution 4.0 International Public License*. The specific dataset version is IGGIELGN and can be downloaded at https://zenodo.org/record/4767098. -SciGRID_gas contains extensive data on pipelines, storages, LNGs, productions, consumers and more. +*SciGRID_gas* contains extensive data on pipelines, storages, LNGs, productions, consumers and more. Further information can be obtained in the IGGIELGN documentation. -For eGon-data, SciGRID_gas infrastructure data in Germany has been extracted and used in full resolution +For eGon-data, *SciGRID_gas* infrastructure data in Germany has been extracted and used in full resolution while data of neighboring countries has been aggregated. Methane grid ~~~~~~~~~~~~ In the eGon2035 scenario the methane grid is, apart from minor adjustments, equivalent to the gas grid described in the -SciGRID_gas IGGIELGN dataset. +*SciGRID_gas IGGIELGN* dataset. Hydrogen grid ~~~~~~~~~~~~~ diff --git a/docs/data/gas_stores.rst b/docs/data/gas_stores.rst index 35c10d070..eca676c65 100644 --- a/docs/data/gas_stores.rst +++ b/docs/data/gas_stores.rst @@ -1,7 +1,13 @@ -Description of methods and assumptions to include potential h2 stores in the system - Hydrogen stores --------------- +There are two types of hydrogen stores available: Underground respectively saltcavern stores +and overground stores respectively steel tank stores. The steel tank stores are available at every hydrogen bus +and have no restrictions regarding the possible build-up potential. On the other hand saltcavern stores are capped +by the respective methane underground storage capacity stemming from the *SciGRID_gas IGGIELGN* dataset. Methane stores -------------- +The data of the methane stores stems from the *SciGRID_gas IGGIELGN* dataset picturing the exisiting CH4 cavern stores. +Additionally the CH4 grid has a storage capacity of 130 GWh which is an estimation of the Bundesnetzagentur. For the +scenario eGon100RE this storage capacity is split between H2 and CH4 stores, with the same share as the pipes capacity. +These capacities have been calculated with a *PyPSA-eur-sec* run. \ No newline at end of file diff --git a/docs/data/gas_supply.rst b/docs/data/gas_supply.rst index e5cc586f9..2eb072961 100644 --- a/docs/data/gas_supply.rst +++ b/docs/data/gas_supply.rst @@ -1 +1,11 @@ -Information on gas supply - hydrogen and methane. +The scenario eGon2035 includes the production of fossil methane and biogas. +The potential supply of fossil methane in Germany stems from the *SciGRID_gas* dataset in the iteration +*IGGIELGN* (https://zenodo.org/record/4767098). The potential for biogas production accords +to the *Biogaspartner Einspeiseatlas* (https://www.biogaspartner.de/einspeiseatlas/). The supply cap +for both, fossil methane and biogas, is derived from the *Netzentwicklungsplan (NEP) Gas 2020-2030* +(https://fnb-gas.de/wp-content/uploads/2021/09/fnb_gas_nep_gas_2020_de-1.pdf). +For the neighboring countries gas supply has been linearly interpolated from the *TYNDP 2020* between the years 2030 and 2040. + +For the eGon100RE scenario only biogas can be produced. The source for its potential production in Germany +is also the *Biogaspartner Einspeiseatlas* but its maximum allowed production is derived from a +*PyPSA-eur-sec* run. For the neighboring countries all data stems from the *PyPSA-eur-sec* run. \ No newline at end of file From 86adabead54ea081160886fc75737c8004558eee Mon Sep 17 00:00:00 2001 From: IlkaCu Date: Tue, 8 Aug 2023 11:04:50 +0200 Subject: [PATCH 04/27] Add new subsection in about.rst --- docs/about.rst | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/about.rst b/docs/about.rst index 28f7d3525..561f87bdb 100644 --- a/docs/about.rst +++ b/docs/about.rst @@ -50,12 +50,27 @@ Modeling concept and scenarios =============================== eGon-data provides a data model suitable for calculations and optimizations with the tools eTraGo, eDisGo and eGo and therefore aims to satisfy all requirements regarding the scope and temporal as well as spatial granularity of the resulting data model. + + + +System boundaries and general assumptions +----------------------------------------- + +* Sectors +* Focus on Germany +* Neighbouring countries (which ones and why) +* Spatial resolution / aggregartion levels +* Temporal resolution incl. assumptions on weather year + The following image visualizes the different components considered in scenario ``eGon2035``. .. image:: images/egon-modell-szenario-egon2035.png :width: 800 :alt: Components of the data models +Scenarios +--------- + eGon-data aims to create different scenarios, which differ in terms of RE penetration or the availability of flexibility options. Currently, the following scenarios are available or in progress. * ``eGon2035`` Mid-termin scenario based on assumptions from the German network expansion plan 'scenario C2035', version 2021 and TYNDP From ec8635320b831a8928c4096a58389fe0ee608136 Mon Sep 17 00:00:00 2001 From: IlkaCu Date: Tue, 8 Aug 2023 11:05:50 +0200 Subject: [PATCH 05/27] Extend docs for eHV/HV grid topology --- docs/data/electricity_grids.rst | 32 +++++++++++++++++++++++--------- docs/images/Stromnetz.png | Bin 0 -> 1209722 bytes 2 files changed, 23 insertions(+), 9 deletions(-) create mode 100644 docs/images/Stromnetz.png diff --git a/docs/data/electricity_grids.rst b/docs/data/electricity_grids.rst index 0d288e836..8c94693c8 100644 --- a/docs/data/electricity_grids.rst +++ b/docs/data/electricity_grids.rst @@ -3,18 +3,32 @@ High and extra-high voltage grids ++++++++++++++++++++++++++++++++++ -The model of the German extra-high (eHV) and high voltage (HV) grid is based -on data retrieved from OpenStreetMap (status January 2021) [OSM]_ and additional -parameters for standard transmission lines from [Brakelmann2004]_. To gather all -required information, such as line topology, voltage level, substation locations, -and electrical parameters, to create a calculable power system model, the `osmTGmod -tool `_ was used. The corresponding dataset -:py:class:`Osmtgmod ` executes osmTGmod +The model of the German extra-high (eHV) and high voltage (HV) grid is based +on data retrieved from OpenStreetMap (OSM) (status January 2021) [OSM]_ and additional +parameters for standard transmission lines from [Brakelmann2004]_. To gather all +required information, such as line topology, voltage level, substation locations, +and electrical parameters, to create a calculable power system model, the `*osmTGmod* +tool `_ was used. The corresponding dataset +:py:class:`Osmtgmod ` executes osmTGmod and writes the resulting data to the database. The resulting grid model includes the voltage levels 380, 220 and 110 kV and -all substations interconnecting these grid levels. For further information on the -generation of the grid topology please refer to [Mueller2018]_. +all substations interconnecting the different grid levels. Information about +border crossing lines are as well extracted from *OSM* data by *osmTGmod*. +For further information on the generation of the grid topology please refer to [Mueller2018]_. +The neighbouring countries are included in the model in a significantly lower +spatial resolution with one or two nodes per country. The border crossing lines +extracted by *osmTGmod* are extended to representative nodes of the respective +country in dataset +:py:class:`ElectricalNeighbours `. The +resulting grid topology is shown in the following figure. + +..figure:: images/Stromnetz.png + :scale: 50 % + :name: gridtopology_ehv_hv + :alt: Grid topology extra-high and high voltage grid + + .. _ding0-grids: diff --git a/docs/images/Stromnetz.png b/docs/images/Stromnetz.png new file mode 100644 index 0000000000000000000000000000000000000000..47034f110be86f4ecb6dd5007a0375902c28576d GIT binary patch literal 1209722 zcmZs@byQVb8#TVcPDD^t1Qclmq@+Vd=`LwR0cq*3>s1645~xe&6r+#~pVJ2A<7c>sfQoXFhWsUj;ewGbgW{L=fbRq=c9ff*d18kP|PC zABCR~@ZCFr|DAXyp<#<4tk=;02s*A(lEV*A+ueU?cgNoDsg(`#*!+>rqk~p+Q}{VK z`ty4>hL7wFpCBB!I5=5uakFr6sIcGS=i=k%;2J5@S%+V{`k!Bef3GMmseFx-mHieg z7t5V%_pEH}mmlTl*&Ku4JAwY5w4t-3mCX}dq&K;Z8-7TE{!qcj>WTeh!~g$(J@MUw z27d84`isi;2BwCO?GQr8$baBxN70|DnAn*cA|Do7=@H}_A}Mx9#VKZPz}cyNyy#%L z$E2?Umaz_zSk>Cc?f$|;Zu3jd|D_^Eejt~iV z+b|%yJ-mzIrln&_l1f_eaRT{8%)ntXM_dR*!PB?DPB>FTGs8yqold ze&W@2p)(JQIP?w^`o%eDW)DSXW(4=eajkphrfzP>B3^<~uSBUrJx0%T#Jd;tZ#YO| 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zeXNi5c>3YPa_;E+8-FN_dg&3C%R!NN$Yf6Z*76gYV<)(J6jv@Cxin>djC-5hj_Mj3 zQ5=1i+uGSARPn+xL3>!-eV!t8tm;hKJ=od-e<)L!ZN{ zEq|p{;)KN(ulEUsxBGV~l{^;gO zN%y=?IHml(XHQM6>CmD2M-_iufTfQ`5Qg&dw;bx+wr$%sxn+l)Qt~P*J#T5%mzVE* zGVc4&9nj3Ew(g;=zG)nRlrNuGf=Wprb&#yK+*4s-YWgwGgG5OBnKO>IJ-+`!e)Mxm zXT|#F=B|Wy{-mT(rrP9=I>I4cxb=>trCoXXa(;*3e~)o#|NVlu=?v2jE~;G0hlqB0 rHnDD2>z3CQ>N{ Date: Wed, 16 Aug 2023 09:10:40 +0200 Subject: [PATCH 06/27] Add documentation for electricity demand --- docs/data/electricity_demand.rst | 97 +++++++++++++++++++++++++++++++- docs/data/heat_demand.rst | 3 +- docs/literature.rst | 4 ++ 3 files changed, 102 insertions(+), 2 deletions(-) diff --git a/docs/data/electricity_demand.rst b/docs/data/electricity_demand.rst index d375e954c..9ccb1e03e 100644 --- a/docs/data/electricity_demand.rst +++ b/docs/data/electricity_demand.rst @@ -1 +1,96 @@ -Information about electricity demands and their spatial and temporal aggregation +The electricity demand considered includes demand from the residential, commercial and industrial sector. +The target values for scenario *eGon2035* are taken from the German grid development plan from 2021 [NEP2021]_, +whereas the distribution on NUTS3-levels corresponds to the data from the research project *DemandRegio* [demandregio]_. +The following table lists the electricity demands per sector: + +.. list-table:: Electricity demand per sector + :widths: 25 50 + :header-rows: 1 + + * - Sector + - Annual electricity demand in TWh + * - residential + - 115.1 + * - commercial + - 123.5 + * - industrial + - 259.5 + +A further spatial and temporal distribution of the electricity demand is needed to fullfil all requirements of the +subsequent grid optimization. Therefore different, sector-specific distributions methods were developed and applied. + +Residential electricity demand +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The annual electricity demands of households on NUTS3-level from *DemandRegio* are scaled to meet the national target +values for the respective scenario in dataset :py:class:`DemandRegio `. +A further spatial and temporal distribution of residential electricity demands is performed in +:py:class:`HouseholdElectricityDemand ` as described +in [Buettner2022]_. +The result is a consistent dataset across aggregation levels with an hourly resolution. + +.. figure:: /images/S27-3.png + :name: spatial_distribution_electricity_demand + :width: 400 + + Electricity demand on NUTS 3-level (upper left); Exemplary MVGD (upper right); Study region in Flensburg (20 Census cells, bottom) from [Buettner2022]_ + + +.. figure:: /images/S27-4a.png + :name: aggregation_level_electricity_demand + :width: 400 + + Electricity demand time series on different aggregation levels from [Buettner2022]_ + + + +Commercial electricity demand +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The distribution of electricity demand from the commercial, trade and service (CTS) sector is also based on data from +*DemandRegio*, which provides annual electricity demands on NUTS3-level for Germany. In dataset +:py:class:`CtsElectricityDemand ` the annual electricity +demands are further distributed to census cells (100x100m cells from [Census]_) based on the distribution of heat demands, +which is taken from the Pan-European Thermal Altlas version 5.0.1 [Peta]_. For further information refer to section +ref:`heat_demand`. +The applied methods for a futher spatial and temporal distribution to buildings is described in [Buettner2022]_ and +performed in dataset :py:class:`CtsDemandBuildings ` + +Industrial electricity demand +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To distribute the annual industrial electricity demand OSM landuse data as well as information on industrial sites are +taken into account. +In a first step (:py:class:`CtsElectricityDemand `) +different sources providing information about specific sites and further information on the industry sector in which +the respective industrial site operates are combined. Here, the three data sources [Hotmaps]_, [sEEnergies]_ and +[Schmidt2018]_ are aligned and joined. +Based on the resulting list of industrial sites in Germany and information on industrial landuse areas from OSM [OSM]_ +which where extracted and processed in :py:class:`OsmLanduse ` the annual demands +were distributed. +The spatial and temporal distribution is performed in +:py:class:`IndustrialDemandCurves `. +For the spatial distribution of annual electricity demands from *DemandRegio* [demandregio]_ which are available on +NUTS3-level are in a first step evenly split 50/50 between industrial sites and OSM-polygons tagged as industrial areas. +Per NUTS-3 area the respective shares are then distributed linearily based on the area of the corresponding landuse polygons +and evenly to the identified industrial sites. +In a next step the temporal disaggregation of the annual demands is carried out taking information about the industrial +sectors and sector-specific standard load profiles from [demandregio]_ into account. +Based on the resulting time series and their peak loads the corresponding grid level and grid connections point is +identified. + +Electricity demand in neighbouring countries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The neighbouring countries considered in the model are represented in a lower spatial resolution of one or two buses per +country. The national demand timeseries in an hourly resolution of the respective countries is taken from the Ten-Year +Network Development Plan, Version 2020 [TYNDP]_. In case no data for the target year is available the data is is +interpolated linearly. +Refer to the corresponding dataset for detailed information: +:py:class:`ElectricalNeighbours ` + + + + + + diff --git a/docs/data/heat_demand.rst b/docs/data/heat_demand.rst index 6e004ae28..d03dac90c 100644 --- a/docs/data/heat_demand.rst +++ b/docs/data/heat_demand.rst @@ -1,4 +1,5 @@ -Heat demands comprise space heating and dirinking hot water demands from +.. _heat_demand: +Heat demands comprise space heating and drinking hot water demands from residential and comertial trade and service (CTS) buildings. Process heat demands from the industry are, depending on the required temperature level, modelled as electrcity, hydrogen or methane demand. diff --git a/docs/literature.rst b/docs/literature.rst index e44a0dc0f..9f2328038 100644 --- a/docs/literature.rst +++ b/docs/literature.rst @@ -26,6 +26,8 @@ Literature .. [Huelk2017] L. Hülk, L. Wienholt, I. Cußmann, U.P. Müller, C. Matke, E. Kötter, Allocation of annual electricity consumption and power generation capacities across multiple voltage levels in a high spatial resolution, International Journal of Sustainable Energy Planning and Management Vol. 13 2017 79–92. URL https://journals.aau.dk/index.php/sepm/article/view/1833 +.. [MaStR] Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen, Marktstammdatenregister - Datendownload (Nov. 2022). URL https://www.marktstammdatenregister.de/MaStR/Datendownload + .. [MiD2017] Bundesministerium für Digitales und Verkehr, Mobilität in Deutschland 2017 (2017). URL https://daten.clearingstelle-verkehr.de/279/ .. [Mueller2018] U. Mueller, L. Wienholt, D. Kleinhans, I. Cussmann, W.-D. Bunke, G. Pleßmann, J. Wendiggensen 2018 J. Phys.: Conf. Ser. 977 012003, DOI 10.1088/1742-6596/977/1/012003 @@ -43,3 +45,5 @@ Literature .. [Schmidt2018] D. Schmidt, Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model (Nov. 2019). doi:10.5281/zenodo.3613767. URL https://doi.org/10.5281/zenodo.3613767 .. [sEEnergies] T. Fleiter, P. Manz, N. Neuwirth, F. Mildner, K. Persson, U.AND Kermeli, W. Crijns-Graus, C. Rutten, seenergies d5.1 dataset web-app.seenergies arcgis online web-apps hosted by europa-universität flensburg (2020). URL https://tinyurl.com/sEEnergies-D5-1 + +.. [TYNDP] European Network of Transmission System Operators for Electricity, European Network of Transmission System Operators for Gas, Ten-Year Network Development Plans - “TYNDP 2020 Scenarios” (2020) From 5727030e2658597b83f9ccbd5ded2c352fac2eac Mon Sep 17 00:00:00 2001 From: IlkaCu Date: Wed, 16 Aug 2023 11:23:29 +0200 Subject: [PATCH 07/27] Add docu for hydro and biomass --- docs/data/electricity_supply.rst | 43 +++++++++++++++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/docs/data/electricity_supply.rst b/docs/data/electricity_supply.rst index 418bd886b..bfb793262 100644 --- a/docs/data/electricity_supply.rst +++ b/docs/data/electricity_supply.rst @@ -19,6 +19,8 @@ The final distribution in the eGon2035 scenario is shown in figure :ref:`generat Onshore wind +++++++++++++ + + Offshore wind ++++++++++++++ @@ -81,6 +83,45 @@ Disaggregation of PV rooftop scenario capacities: using MaStR data as basis. Hydro -++++++ ++++++ + +In the case of hydropower plants, a distinction is made between the carrier run-of-river +and reservoir. +The methods to distribute and allocate are the same for both carriers. +In a first step all suitable power plants (correct carrier, valid geolocation, information +about federal state) are selected and their installed capacity is scaled to meet the target +values for the respective federal state and scenario. +Information about the voltage level the power plants are connected to is obtained. In case +no information is availabe the voltage level is identified using threshold values for the +installed capacity (see :func:`assign_voltage_level `). +In a next step the correct grid connection point is identified based on the voltage level +and geolocation of the power plants (see :func:`assign_bus_id `) +The resulting list of power plants it added to table +:py:class:`EgonPowerPlants `. + +Biomass ++++++++ + +The allocation of biomass-based power plants follows the same method as the one for hydro +power plants and is performed in function :func:`insert_biomass_plants ` + + + +Conventional +++++++++++++ + +**CHP** + + +**non-chp** + + +In function :func:`allocate_conventional_non_chp_power_plants ` +capacities for conventional power plants, which are no chp plants, with carrier *oil* and +*gas* are allocated. + + + + From a2e53f2bd0b6141c6c7dfe4aa8fcc8299b4cce99 Mon Sep 17 00:00:00 2001 From: KathiEsterl Date: Wed, 16 Aug 2023 14:49:09 +0200 Subject: [PATCH 08/27] Add documentation for DSM --- docs/data/DSM.rst | 48 +++++++++++++++++++++++++++- docs/data/electricity_demand.rst | 2 ++ docs/images/DSM_potential.png | Bin 0 -> 315075 bytes docs/images/shifted_dsm-example.png | Bin 0 -> 74560 bytes docs/literature.rst | 4 +++ 5 files changed, 53 insertions(+), 1 deletion(-) create mode 100644 docs/images/DSM_potential.png create mode 100644 docs/images/shifted_dsm-example.png diff --git a/docs/data/DSM.rst b/docs/data/DSM.rst index 79d4a96ef..40fafa5be 100644 --- a/docs/data/DSM.rst +++ b/docs/data/DSM.rst @@ -1 +1,47 @@ -How did we implement DSM? Results etc. +Demand-side management (DSM) potentials are calculated in function :func:`dsm_cts_ind_processing`. +Potentials relevant for the high and extra-high voltage grid are identified in function :func:`dsm_cts_ind`, +potentials within the medium- and low-voltage grids are determined within the function :func:`dsm_cts_ind_individual` +in a higher spatial resolution. All this is part of the dataset :py:class:`DsmPotential `. + +Loads eligible to be shifted are assumed within industrial loads and loads from Commercial, Trade and Service (CTS). +Therefore, load time series from these sectors are used as input data (see :ref:`elec_demand-ref`). +Shiftable shares of loads mainly derive from heating and cooling processes and selected energy-intensive +industrial processes (cement production, wood pulp, paper production, recycling paper). Technical and sociotechnical +constraints are considered using the parametrization elaborated in [Heitkoetter]_. An overview over the +resulting potentials can be seen in figure :ref:`dsm_potential`. The table summarizes the aggregated potential for Germany per scenario. + +.. figure:: /images/DSM_potential.png + :name: dsm_potential + :width: 400 + + Aggregated DSM potential in Germany + +.. list-table:: Aggregated DSM Potential for Germany + :widths: 20 20 20 + :header-rows: 1 + + * - + - CTS + - Industry + + * - eGon2035 + - 1.2 GW + - 150 MW + + * - eGon100RE + - 900 MW + - 150 MW + +DSM is modelled following the approach of [Kleinhans]_. DSM components are created wherever +respective loads are seen. A minimum and maximum shiftable power per time step depict time-dependent +charging and discharging power of a storage-equivalent buffer. Time-dependent capacities +of the storage-equivalent buffer account for the time frame of management bounding the period within which +the shifting can be conducted. Figure :ref:`dsm_shifted_p-example` shows the resulting potential at one exemplary bus. + +.. figure:: /images/shifted_dsm-example.png + :name: dsm_shifted_p-example + :width: 400 + + Time-dependent DSM potential at one exemplary bus + + diff --git a/docs/data/electricity_demand.rst b/docs/data/electricity_demand.rst index d375e954c..c82c577bd 100644 --- a/docs/data/electricity_demand.rst +++ b/docs/data/electricity_demand.rst @@ -1 +1,3 @@ +.. _elec_demand-ref: + Information about electricity demands and their spatial and temporal aggregation diff --git a/docs/images/DSM_potential.png b/docs/images/DSM_potential.png new file mode 100644 index 0000000000000000000000000000000000000000..1312cfda0faa364ba2af71ba949c048036bd0a3a GIT binary patch literal 315075 zcmagFWmH>1yC{siLvanXKyi0>*Fy2)R@~h^xE6PpV#T3YuwupCEx0?}^xS*SdwzXi z)~uD4*?XRu%;OfNsw{(!LW}|h1%)mrE2$0z1-k=B(0GYuGf*OK+!MTXbX(B;B zzDQ<~kl#eEQeRy)94uTtjGfJ)EbSfa%$ZzFoz2bdU922j&tZFnp`a+C zp{zKRdVNNsl+bnNTfH!$3A$<6S%lwMTh}^#YR;DLt4Fy2XI%$BFb}dLNfHyk=qOm2 zYYFvextmL`G6AU1z1}82S5^{+8~^vHH*VGQgZ|HNr!r%cRaUhA|2Mb^ad-anu)a5g zkmrA%0ntGNt^Wha_&e(-5c+?wLo*FwW3&J7O^gXifKg8W^Q?dIn<9|pf53teO#i>O zoVCA&{|5*p?echK9!Dks8W@0!mEO@Zii2!kFLmU~&&bT|e6cC^iI&#kc8^dGXp6J6 z?(=rV@}87#;D%|Bga7T5Hh3tq5$pFVxc$j;u)+Iab-p`mf?X7FD? 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[FfE2017] Flexibilisierung der Kraft-Wärme-Kopplung; 2017; Forschungsstelle für Energiewirtschaft e.V. (FfE) +.. [Heitkoetter] Wilko Heitkoetter, Bruno U. Schyska, Danielle Schmidt, Wided Medjroubi, Thomas Vogt, Carsten Agert, Assessment of the regionalised demand response potential in Germany using an open source tool and dataset, Advances in Applied Energy (2021), URL https://www.sciencedirect.com/science/article/pii/S2666792420300019 + .. [Helfenbein2021] K. Helfenbein, Analyse des Einflusses netzdienlicher Ladestrategien auf Verteilnetze aufgrund der zunehmenden Netzintegration von Elektrofahrzeugen, Master’s thesis, Hochschule für Technik und Wirtschaft Berlin, URL https://reiner-lemoine-institut.de/analyse-einflussesnetzdienlicher-ladestrategien-verteilnetze-zunehmender-netzintegration-elektrofahrzeugen-helfenbein-2021/ .. [Hotmaps] S. Pezzutto, S. Zambotti, S. Croce, P. Zambelli, G. Garegnani, C. Scaramuzzino, R. P. Pascuas, A. Zubaryeva, F. Haas, D. Exner, A. Mueller, M. 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Bunke, G. Pleßmann, J. Wendiggensen 2018 J. Phys.: Conf. Ser. 977 012003, DOI 10.1088/1742-6596/977/1/012003 From ebc7ae379e651a0c22ad705adf8af782f9146c97 Mon Sep 17 00:00:00 2001 From: KathiEsterl Date: Wed, 16 Aug 2023 14:49:37 +0200 Subject: [PATCH 09/27] Prepare documentation of PV ground mounted --- docs/data/electricity_supply.rst | 6 ++++++ docs/images/PV_freiflaeche.png | Bin 0 -> 4731326 bytes 2 files changed, 6 insertions(+) create mode 100644 docs/images/PV_freiflaeche.png diff --git a/docs/data/electricity_supply.rst b/docs/data/electricity_supply.rst index 418bd886b..cdbce13c0 100644 --- a/docs/data/electricity_supply.rst +++ b/docs/data/electricity_supply.rst @@ -25,6 +25,12 @@ Offshore wind PV ground mounted ++++++++++++++++++ +.. figure:: /images/PV_freiflaeche.png + :name: pv_ground_mounted-example + :width: 400 + + Example: potential areas for ground mounted PVs and locations of existing ground mounted PVs + PV rooftop +++++++++++ diff --git a/docs/images/PV_freiflaeche.png b/docs/images/PV_freiflaeche.png new file mode 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