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Update myopic and extend carbon budget description
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@ -6,13 +6,13 @@ Myopic transition path
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 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) <https://www.nature.com/articles/s41467-020-20015-4>`__ but 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 <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model. The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ and later further extended in `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__. 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 <https://github.com/PyPSA/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 and the space and water heating sector. 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 :doc:`supply_demand` for further information).
The transport sector and industry are not affected by the myopic code. In essence, the electrification of road and rail transport, the percentage of electric vehicles that allow demand-side management and vehicle-to-grid services, and the transformation in the different industrial subsectors do not evolve with time. They are kept fixed at the values specified in the configuration file. Including the transport sector and industry in the myopic code is planned for the near future. 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
See also other `outstanding issues <https://github.com/PyPSA/pypsa-eur-sec/issues/19#issuecomment-678194802>`_. - The annual biomass potential (default year and scenario for which potential is taken is 2030, defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L109>`_)
Configuration Configuration
================= =================
@ -23,24 +23,38 @@ The following options included in the config.yaml file are relevant for the myo
To activate the myopic option select ``foresight: 'myopic'`` in ``config.yaml``. To activate the myopic option select ``foresight: 'myopic'`` in ``config.yaml``.
To set the investment years which are sequentially simulated for the myopic investment planning, select for example ``planning_horizons : [2020, 2030, 2040, 2050]`` in ``config.yaml``. 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:
planning_horizons:
\- 2020
\- 2030
\- 2040
\- 2050
in ``config.yaml``.
**existing capacities** **existing capacities**
Grouping years indicates the bins limits for grouping the existing capacities of different technologies 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]``
grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
**threshold capacity** **threshold capacity**
if for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored
threshold_capacity: 10 ``threshold_capacity: 10``
@ -49,30 +63,33 @@ threshold_capacity: 10
conventional carriers indicate carriers used in the existing conventional technologies conventional carriers indicate carriers used in the existing conventional technologies
conventional_carriers: ['lignite', 'coal', 'oil', 'uranium'] conventional_carriers:
\- lignite
\- coal
\- oil
\- uranium
Wildcards
==============================
**{planning_horizons} wildcard**
The {planning_horizons} wildcard indicates the timesteps in which the network is optimized, e.g. planning_horizons: [2020, 2030, 2040, 2050]
Options Options
============= =============
The total carbon budget for the entire transition path can be indicated in the ``scenario.sector_opts`` in ``config.yaml``. The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay.
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 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero
E.g. ``'cb40ex0'`` splits the a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ 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 hroughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
$e(t) = e_0 (1+ (r+m)t) e^(-mt)$ $e(t) = e_0 (1+ (r+m)t) e^(-mt)$
See details in Supplementary Note 1 of the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ 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) <https://doi.org/10.1016/j.joule.2022.04.016>`__
Rules overview
=================
General myopic code structure General myopic code structure
=============================== ===============================
@ -89,32 +106,32 @@ The base year is the first element in ``planning_horizons``. Step 1 is implement
Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``. Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``.
Rule overview
===============================
rule add_existing baseyear - rule add_existing baseyear
==========================
The rule add_existing_baseyear loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operations: 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. 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. 2. Add the heating capacities that were installed before the base year.
The existing conventional generators are retrieved from the `powerplants.csv file <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ generated by pypsa-eur which, in turn, is based on the `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ database. The existing conventional generators are retrieved from the `powerplants.csv file <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ generated by pypsa-eur which, in turn, is based on the `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ database.
Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.) Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ 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) Existing heating capacities are retrieved from the report `Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)
<https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__ <https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__
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. 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``. Then, the resulting network is saved in ``results/run_name/networks/prenetworks-brownfield``.
rule add_brownfield - rule add_brownfield
===================
The rule add_brownfield loads the network in ``results/run_name/networks/prenetworks`` and performs the following operation: 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) 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``. Then, the resulting network is saved in ``results/run_name/networks/prenetworks_brownfield``.