.. _economics: Economic Assumptions ===================== This page documents how GreenBubble translates technology investment data into annual capital charges, how the discount rate is applied, and how brownfield initial conditions are parameterised. --- .. _economics-technology-data: Technology-data source ---------------------- Investment costs, fixed O&M rates, efficiencies, and technical lifetimes come from the `technology-data `_ repository (branch ``pypsa-eur_AA``), which is a fork of the `PyPSA-Eur technology-data catalogue `_. The repository provides separate CSV files for each 5-year planning horizon: 2020, 2025, 2030, 2035, 2040, 2045, 2050. All cost projections are in **constant real EUR** of a fixed base year (i.e. they represent technology learning curves, not nominal price inflation — see :ref:`economics-real-costs`). The CSV files are downloaded automatically by the ``retrieve_tech_data`` rule (all years at once) and stored in ``data/technology-data/outputs/``. A git blob SHA is cached alongside each file so that Snakemake detects upstream changes without a manual ``--forcerun``. Project-specific overrides (compressor sizing, GLS-specific equipment) are defined as ``("technology", "parameter")`` tuples in ``scripts/technology_inputs.py`` and merged into the cost table via ``helpers.merge_into_costs()`` before the annuity is computed. --- .. _economics-annuity: Capital cost annualisation -------------------------- The annual capital charge for a technology with investment cost *I* (EUR/MW), fixed O&M rate *f* (% of investment per year), and technical lifetime *L* (years) is: .. math:: \text{capital\_cost} = I \cdot \left[ \text{annuity}(r, L) + \frac{f}{100} \right] where the annuity factor is: .. math:: \text{annuity}(r, L) = \frac{r}{1 - (1+r)^{-L}} *r* is the project discount rate (see :ref:`economics-discount-rate`). This is computed in ``helpers.read_costs()`` and stored in the ``fixed`` column of the cost DataFrame. PyPSA then multiplies ``fixed`` by the optimised capacity (``p_nom_opt``) to obtain the annual capital expenditure in ``n.statistics.capex()``. **Amortization period** By default the annuity denominator equals the technical lifetime *L*. Setting ``amortization_period`` in ``config.yaml`` to a positive number (e.g. ``15``) overrides *L* for all **new** expandable capacity: .. code-block:: yaml amortization_period: 15 # recover new investments over 15 years A shorter amortization period → higher annual charge → harder to invest. ``null`` (default) restores the technical-lifetime behaviour. For existing capacity the effective period is always ``amortization_period`` (warn if it exceeds the asset's remaining technical lifetime — reinvestment may be implied; see :ref:`economics-brownfield`). --- .. _economics-discount-rate: Discount rate ------------- ``discount_rate`` in ``config.yaml`` is a **real** rate — it excludes inflation. All cost data are expressed in constant real EUR of a fixed base year, so comparing costs across planning years (2020 vs 2030) is valid without any price-level adjustment. .. note:: Do **not** use a nominal rate (which includes expected inflation). Mixing real costs with a nominal discount rate would systematically over-penalise future costs. Typical real discount rates for energy projects range from 5 % (public finance) to 10 % (private equity). The default is 7 %. --- .. _economics-year-investment: year_investment --------------- ``year_investment`` selects which year's cost CSV is used for **new** expandable capacity. For example, ``year_investment: 2030`` loads ``data/technology-data/outputs/costs_2030.csv``. This is independent of the energy/weather year (``En_price_year``) used for electricity prices and capacity factors. Available values: 2020, 2025, 2030, 2035, 2040, 2045, 2050. --- .. _economics-brownfield: Brownfield initial conditions ------------------------------ When ``initial capacity > 0`` in ``n_config.yaml``, an existing (``EXI_``) component is added to the network. Three parameters control its annual capital charge: ``construction_year`` The year the asset was built. Used to look up the investment cost at the actual build year: ``I(construction_year)``. Technology costs change between years (learning curves), so an older plant typically cost more than a plant built today. If not set (``null``), defaults to ``year_investment - 10`` (i.e. 10 years before the current planning year), capped at 2020 (the earliest available cost data). ``remaining_investment_fraction`` The fraction of ``I(construction_year)`` that is **financially still outstanding**. This is independent of the technical remaining lifetime: a fully paid-off asset has ``remaining_investment_fraction = 0`` even if it still has many years of useful life ahead. ``0`` (default) = sunk cost; the EXI_ component carries no annual CAPEX. ``1`` = the full original investment is still outstanding. **Annual charge formula** .. math:: \text{EXI\_capital\_cost} = \text{rif} \times I(\text{construction\_year}) \times \text{annuity}(r,\, \text{amortization\_period}) where *rif* = ``remaining_investment_fraction`` and the effective amortization period comes from ``amortization_period`` in ``config.yaml`` (or the technical lifetime if ``null``). .. note:: If the asset's remaining technical lifetime (:math:`\text{construction\_year} + L - \text{year\_investment}`) is shorter than the amortization period, a ``UserWarning`` is issued: re-investment within the planning horizon may be implied. **Example** Existing electrolysis unit, commissioned in 2020, 60 % of original investment still outstanding, no new capacity to be built on top: .. code-block:: yaml # config/n_config.yaml electrolysis: initial capacity: 5 # MW_el expansion: false construction_year: 2020 remaining_investment_fraction: 0.6 With ``year_investment: 2030``, ``amortization_period: null``, ``discount_rate: 0.07``, and a 25-year technical lifetime: - ``remaining_lifetime = 2020 + 25 − 2030 = 15 years`` - ``I(2020)`` is read from ``costs_2020.csv`` - ``annuity(15, 0.07) ≈ 0.110`` (Python call: ``annuity(n, r)``) - Annual charge = ``0.6 × I(2020) × 0.110`` EUR/MW/year **Relationship to amortization_period and remaining lifetime** +-----------------------+---------------------+----------------------------+ | remaining_lifetime | amortization_period | Outcome | +=======================+=====================+============================+ | > amortization_period | set | Recovery accelerated; asset| | | | financially clear before | | | | technical end-of-life. | +-----------------------+---------------------+----------------------------+ | < amortization_period | set | Warning issued; implies | | | | re-investment before full | | | | recovery. | +-----------------------+---------------------+----------------------------+ | = amortization_period | null (default) | Standard case; annuity uses| | | | remaining technical life. | +-----------------------+---------------------+----------------------------+ --- .. _economics-real-costs: Real costs and currency ----------------------- All monetary values in GreenBubble are expressed in **real EUR** of a fixed base year (the base year is inherited from the technology-data repository). The cost trajectories from 2020 to 2050 represent technology learning (e.g. falling solar costs) — not changes in the general price level. Consequence: there is **no need to inflate or deflate** investment costs between ``construction_year`` and ``year_investment``. ``I(2022)`` and ``I(2030)`` are already in the same real EUR, so comparing or dividing them is financially consistent. USD-denominated technologies are converted at the ``USD_to_EUR`` exchange rate set in ``config.yaml``.