Foresight Options#

Overnight (greenfield) scenarios#

The default is to calculate a rebuilding of the energy system to meet demand, a so-called overnight or greenfield approach.

In this case, the planning_horizons parameter specifies the reference year for exogenously given transition paths (e.g. the level of steel recycling). It does not affect the year for cost and technology assumptions, which is set separately in the config.

  - 2050

  year: 2030

For running overnight scenarios, use in the config/config.yaml:

foresight: overnight

Perfect foresight scenarios#


Perfect foresight is currently implemented as an experimental test version.

For running perfect foresight scenarios, you can adjust the


foresight: perfect

Myopic foresight scenarios#

The myopic code can be used to investigate progressive changes in a network, for instance, those taking place throughout a transition path. The capacities installed in a certain time step are maintained in the network until their operational lifetime expires.

The myopic approach was initially developed and used in the paper Early decarbonisation of the European Energy system pays off (2020) and later further extended in Speed of technological transformations required in Europe to achieve different climate goals (2022). The current implementation complies with the PyPSA-Eur-Sec standard working flow and is compatible with using the higher resolution electricity transmission model PyPSA-Eur rather than a one-node-per-country model.

The current code applies the myopic approach to generators, storage technologies and links in the power sector. It furthermore applies it to the space and water heating sector (e.g., the share of district heating and reduced space heat demand), industry processes (e.g., steel, direct reduced iron, and aluminum production via primary route), the share of fuel cell and battery electric vehicles in land transport, and the hydrogen share in shipping (see Supply and demand for further information).

The following subjects within the land transport and biomass currently do not evolve with the myopic approach:

  • The percentage of electric vehicles that allow demand-side management and vehicle-to-grid services.

  • The annual biomass potential (default year and scenario for which potential is taken is 2030, as defined in config)


For running myopic foresight transition scenarios, set in config/config.yaml:

foresight: myopic

The following options included in the config/config.yaml file are relevant for the myopic code.

The {planning_horizons} wildcard indicates the year in which the network is optimized. For a myopic optimization, this is equivalent to the investment year. To set the investment years which are sequentially simulated for the myopic investment planning, select for example:

  - 2030
  - 2040
  - 2050

existing capacities

Grouping years indicates the bins limits for grouping the existing capacities of different technologies. Note that separate bins are defined for the power and heating plants due to different data sources.

grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]

grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]

threshold capacity

If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored.

threshold_capacity: 10

conventional carriers

Conventional carriers indicate carriers used in the existing conventional technologies.


- lignite

- coal

- oil

- uranium


The total carbon budget for the entire transition path can be indicated in the sector_opts in config/config.yaml. The carbon budget can be split among the planning_horizons following an exponential or beta decay. E.g. 'cb40ex0' splits a carbon budget equal to 40 Gt \(_{CO_2}\) following an exponential decay whose initial linear growth rate r is zero. They can also follow some user-specified path, if defined here. The paper Speed of technological transformations required in Europe to achieve different climate goals (2022) defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed throughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by

\[e(t) = e_0 (1+ (r+m)t) e^{-mt}\]

where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the scenario.sector_opts E.g. -cb25.7ex0 (1.5C increase) Or cb73.9ex0 (2C increase). See details in Supplemental Note S1 Speed of technological transformations required in Europe to achieve different climate goals (2022).

General myopic code structure#

The myopic code solves the network for the time steps included in planning_horizons in a recursive loop, so that:

  1. The existing capacities (those installed before the base year are added as fixed capacities with p_nom=value, p_nom_extendable=False). E.g. for baseyear=2020, capacities installed before 2020 are added. In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in results/run_name/networks/prenetworks-brownfield.

The base year is the first element in planning_horizons. Step 1 is implemented with the rule add_baseyear for the base year and with the rule add_brownfield for the remaining planning_horizons.

  1. The 2020 network is optimized. The solved network is saved in results/run_name/networks/postnetworks

  2. For the next planning horizon, e.g. 2030, the capacities from a previous time step are added if they are still in operation (i.e., if they fulfil planning horizon <= commissioned year + lifetime). In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in results/run_name/networks/prenetworks-brownfield.

Steps 2 and 3 are solved recursively for all the planning_horizons included in config/config.yaml.

Rule overview#

  • rule add_existing baseyear

    The rule add_existing_baseyear loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operations:

    1. Add the conventional, wind and solar power generators that were installed before the base year.

    2. Add the heating capacities that were installed before the base year.

    The existing conventional generators are retrieved from the powerplants.csv file generated by pypsa-eur which, in turn, is based on the powerplantmatching database.

    Existing wind and solar capacities are retrieved from IRENA annual statistics and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.)

    Existing heating capacities are retrieved from the report Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables).

    The heating capacities are assumed to have a lifetime indicated by the parameter lifetime in the configuration file, e.g 25 years. They are assumed to be decommissioned linearly starting on the base year, e.g., from 2020 to 2045.

    Then, the resulting network is saved in results/run_name/networks/prenetworks-brownfield.

  • rule add_brownfield

    The rule add_brownfield loads the network in results/run_name/networks/prenetworks and performs the following operation:

    1. Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfill planning horizon < commissioned year + lifetime)

    Then, the resulting network is saved in results/run_name/networks/prenetworks_brownfield.