PyPSA-DE - Hochaufgelöstes, sektorengekoppeltes Modell des deutschen Energiesystems

PyPSA-DE ist ein sektorengekoppeltes Energiesystem-Modell auf Basis der Toolbox PyPSA und des europäischen Modells PyPSA-Eur. Der PyPSA-DE Workflow modelliert das deutsche Energiesystem mit deutschlandspezifischen Datensätzen (MaStR, Netzentwicklungsplan,...) im Verbund mit den direkten Stromnachbarn sowie Spanien und Italien. Der Ausbau und der Betrieb von Kraftwerken, des Strom- und Wasserstoffübertragunsnetzes und die Energieversorgung aller Sektoren werden dann in einem linearen Optimierungsproblem gelöst, mit hoher zeitlicher und räumlicher Auflösung. PyPSA-DE wurde im Rahmen des Kopernikus-Projekts Ariadne entwickelt in dem Szenarien für ein klimaneutrales Deutschland untersucht werden, und spielt eine zentrale Rolle im Ariadne Szenarienreport, als Leitmodell für den Sektor Energiewirtschaft und Infrastruktur und als eines von drei Gesamtsystemmodellen. Die Ergebnisse aus der Modellierung mit PyPSA-DE werden auch im Ariadne-Webinar zu den Kernaussagen des Berichts (Folien) und im Ariadne-Webinar zur Energiewirtschaft (Folien) vorgestellt

PyPSA-DE - High resolution, sector-coupled model of the German Energy System

PyPSA-DE is a sector-coupled energy system model based on the toolbox PyPSA and the European model PyPSA-Eur. It solves a linear optimization problem to simulate the electricty and hydrogen transmission networks, as well as supply, demand and storage in all sectors of the energy system in Germany and its neighboring countries, as well as Italy and Spain, with high spatial and temporal resolution. PyPSA-DE was developed in the context of the Kopernikus-Projekt Ariadne, which studies scenarios of a carbon-neutral German economcy, and plays a decisive role in the Ariadne Szenarienreport, as reference model for the energy and infrastructure sectors. The results of modeling with PyPSA-DE are also presented in the Ariadne-Webinar on the core messages of the report (slides) and in the Ariadne-Webinar on the energy sector (slides).

This repository contains the entire scientific project, including data sources and code. The philosophy behind this repository is that no intermediary results are included, but all results are computed from raw data and code.

Getting ready

You need conda or mamba to run the analysis. Using conda, you can create an environment from within which you can run the analysis:

conda env create -f envs/{os}-pinned.yaml

Where {os} should be replaced with your operating system, e.g. for linux the command would be:

conda env create -f envs/linux-pinned.yaml

Connecting to the Ariadne-Database

For external users: Use config.public.yaml

The default workflow configured for this repository assumes access to the internal Ariadne2 database. The database will soon be publicly available. Until then, users that do not have the required login details can run the analysis based on the data published during the first phase of the Ariadne project.

This is possible by providing an additional config to the snakemake workflow. For every snakemake COMMAND specified in the instructions below, public users should use:

snakemake COMMAND --configfile=config/config.public.yaml

The additional config file specifies the required database, model, and scenario names for Ariadne1. If public users wish to edit the default scenario specifications, they can do so by changing scenarios.public.yaml to scenarios.manual.yaml. More details on using scenarios are given below.

For internal users: Provide login details

The snakemake rule retrieve_ariadne_database logs into the interal Ariadne IIASA Database via the pyam package. The credentials for logging into this database have to be stored locally on your machine with ixmp4. To do this activate the project environment and run

ixmp4 login <username>

You will be prompted to enter your <password>.

Caveat: These credentials are stored on your machine in plain text.

To switch between internal and public use, the command ixmp4 logout may be necessary.

Run the analysis

Before running any analysis with scenarios, the rule build_scenarios must be executed. This will create the file config/scenarios.automated.yaml which includes input data and CO2 targets from the IIASA Ariadne database as well as the specifications from the manual scenario file. [This file is specified in the default config.yaml via they key run:scenarios:manual_file (by default located at config/scenarios.manual.yaml)].

snakemake build_scenarios -f

or in case of using the public database

snakemake build_scenarios --configfile=config/config.public.yaml -f

Note that the hierarchy of scenario files is the following: scenarios.automated.yaml > (any explicitly specified --configfiles) > config.yaml > config.default.yaml Changes in the file scenarios.manual.yaml are only taken into account if the rule build_scenarios is executed.

To run the analysis use

snakemake ariadne_all

This will run all analysis steps to reproduce results. If computational resources on your local machine are limited you may decrease the number of cores by adding, e.g. -c4 to the call to get only 4 cores. For more option please refer to the snakemake documentation.

Repo structure

• config: configuration files
• ariadne-data: Germany specific data from the Ariadne project
• scripts: contains the Python scripts for the workflow, the Germany specific code needed to run this repo is contained in scripts/pypsa-de
• cutouts: very large weather data cutouts supplied by atlite library (does not exist initially)
• data: place for raw data (does not exist initially)
• resources: place for intermediate/processing data for the workflow (does not exist initially)
• results: will contain all results (does not exist initially)
• logs and benchmarks
• The Snakefile contains the PyPSA-DE specific snakemake workflow

Differences to PyPSA-EUR

PyPSA-DE is a softfork of PyPSA-EUR. As such, large parts of the functionality are similar, and the documentation of PyPSA-Eur is a good starting point to get acquainted with the model. On top of that, PyPSA-DE adds several data sources and workflow steps that improve the representation of the German Energy System. Below is a non-conclusive list of the most important changes.

• Default resolution of 16 regions in Germany and 13 region for neighboring countries

• 10 pre-defined scenarios (1 Current Policies, 3 Net-Zero Scenarios (Balanced, Focus H2, Focus Electricity), 2 Demand Variations based on the Balanced Scenario, 4 Demand Variations Based on the Current Policies Scenario)

• Specific cost assumption for Germany:

  • Gas, Oil, Coal prices
  • electrolysis and heat-pump costs
  • Infrastructure costs according to the Netzentwicklungsplan 2021 and 2023
  • option for pessimstic, mean and optimistic cost development

• Transport and Industry demands as well as heating stock imported from the sectoral models in the Ariadne consortium (Aladin, REMOD, FORECAST and REMIND)

• More detailed data on CHPs in Germany

• The model has been validated against 2020 electricity data for Germany

• National CO2-Targets according to the Klimaschutzgesetz

• Additional constraints that limit maximum capacity of specific technologies

• Import constraints on Efuels, hydrogen and electricity

• Renewable build out according to the Wind-an-Land, Wind-auf-See and Solarstrategie laws

• A comprehensive reporting module that exports Capacity Expansion, Primary/Secondary/Final Energy, CO2 Emissions per Sector, Trade, Investments, and more.

• Plotting functionality to compare different scenarios

• Electricity Network development until 2030 (and for AC beyond) according to the NEP23

• Offshore development until 2030 according to the Offshore NEP23

• Hydrogen network development until 2028 according to the Wasserstoffkernnetz. PCI / IPCEI projects for later years are included as well.

• costs:horizon - specify if technology costs are expected to follow an optimistic, mean or pessimistic trajectory

New Config Options

• iiasa_database - interaction with IIASA database. Specify a database, and leitmodelle for demand and co2 emissions data in specific sectors
• wasserstoff_kernnetz - configure which parts of the Wasserstoff Kernnetz should be included in the model
• new_decentral_fossil_boiler_ban - specify in which country and which years to ban fossil boilers
• coal_generation_ban - specify in which country and which years to ban electricity generation from coal
• nuclear_generation_ban - specify in which country and which years to ban electricity generation from nuclear
• first_technology_occurrence - specify the year form which on specific technologies are available
• solving:constraints - specify PyPSA-DE specific limits, e.g. on capacity, trade and generation
• co2_budget_DE_source specify the carbon trajectory for Germany: Following the projections of the Umweltbundestamt (UBA) or targeting net zero with the Klimaschutzgesetz(KSG)
• costs:NEP and costs:transmission - specify which year of the Netzentwicklungsplan should be used as basis for the transmission line costs (2021,2023) and if new HVDC links should be built with overhead or underground cables

License

The code in this repo is MIT licensed, see ./LICENSE.md.

Note

Find below the original README.md from PyPSA-Eur

PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy System

PyPSA-Eur is an open model dataset of the European energy system at the transmission network level that covers the full ENTSO-E area. The model is suitable both for operational studies and generation and transmission expansion planning studies. The continental scope and highly resolved spatial scale enables a proper description of the long-range smoothing effects for renewable power generation and their varying resource availability.

The model is described in the documentation and in the paper PyPSA-Eur: An Open Optimisation Model of the European Transmission System, 2018, arXiv:1806.01613. The model building routines are defined through a snakemake workflow. Please see the documentation for installation instructions and other useful information about the snakemake workflow. The model is designed to be imported into the open toolbox PyPSA.

WARNING: PyPSA-Eur is under active development and has several limitations which you should understand before using the model. The github repository issues collect known topics we are working on (please feel free to help or make suggestions). The documentation remains somewhat patchy. You can find showcases of the model's capabilities in the Joule paper The potential role of a hydrogen network in Europe, another paper in Joule with a description of the industry sector, or in a 2021 presentation at EMP-E. We do not recommend to use the full resolution network model for simulations. At high granularity the assignment of loads and generators to the nearest network node may not be a correct assumption, depending on the topology of the underlying distribution grid, and local grid bottlenecks may cause unrealistic load-shedding or generator curtailment. We recommend to cluster the network to a couple of hundred nodes to remove these local inconsistencies. See the discussion in Section 3.4 "Model validation" of the paper.

The dataset consists of:

• A grid model based on a modified GridKit extraction of the ENTSO-E Transmission System Map. The grid model contains 7072 lines (alternating current lines at and above 220kV voltage level and all high voltage direct current lines) and 3803 substations.
• The open power plant database powerplantmatching.
• Electrical demand time series from the OPSD project.
• Renewable time series based on ERA5 and SARAH, assembled using the atlite tool.
• Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the atlite library.

A sector-coupled extension adds demand and supply for the following sectors: transport, space and water heating, biomass, industry and industrial feedstocks, agriculture, forestry and fishing. This completes the energy system and includes all greenhouse gas emitters except waste management and land use.

This diagram gives an overview of the sectors and the links between them:

Each of these sectors is built up on the transmission network nodes from PyPSA-Eur:

For computational reasons the model is usually clustered down to 50-200 nodes.

Already-built versions of the model can be found in the accompanying Zenodo repository.

Contributing and Support

We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.

• To discuss with other PyPSA users, organise projects, share news, and get in touch with the community you can use the Discord server.
• For bugs and feature requests, please use the PyPSA-Eur Github Issues page.

Licence

The code in PyPSA-Eur is released as free software under the MIT License, see doc/licenses.rst. However, different licenses and terms of use may apply to the various input data, see doc/data_sources.rst.