.. SPDX-FileCopyrightText: Contributors to GreenBubble .. SPDX-License-Identifier: CC-BY-4.0 .. _guide-outputs: Outputs & Results Reference =========================== Every run writes to ``outputs/single_analysis/{run_name}/`` (e.g. ``my_scenario/``). .. _outputs-naming: Output naming structure ----------------------- GreenBubble uses a **hybrid naming scheme** that separates the *folder* from the *file names* inside it: - **Folder** — just ``run_name`` from ``config.yaml``. Short by design to stay within Windows' 260-character path limit. - **File names** inside — a long descriptive prefix that encodes the full configuration detail:: {flags}CO2_{co2}_{tD|tP}_H2_{h2}_MeOH_{meoh}_CH4_{ch4}_{year}_El_{el}_{DET|STC}_{res}_{run_name} Example:: B_H_RE_H2_MEOH_METH_SN_ST_CO2_100_tD_H2_200_MeOH_9_CH4_350_2024_El_0.1_DET_3h_my_scenario Flag abbreviations: ``B`` biogas · ``H`` central heat · ``RE`` renewables · ``H2`` electrolysis · ``MEOH`` methanol · ``METH`` methanation · ``SN`` symbiosis · ``ST`` storage. Rolling-horizon runs append ``_RH`` to the file name prefix. Multiple configurations sharing the same ``run_name`` (different years, modes, or flag sets) coexist in the same folder — the descriptive file names distinguish them. The complete configuration is also captured in ``networks/config_run.yaml`` (see :ref:`outputs-config-run`). .. code-block:: text outputs/single_analysis// ├── networks/ # solved network, run fingerprint (config_run.yaml), topology diagrams ├── csv/ # numeric results (the data behind the plots) ├── plots/ # figures + network diagrams └── duals/ # raw constraint duals (if collect_all_duals: true) Plots and CSVs are produced from the same post-processing pass, so most figures have a CSV twin; they are described together below. --- .. _outputs-networks: ``networks/`` — the solved model -------------------------------- ================================ ==================================================== File Contents ================================ ==================================================== ``_OPT.nc`` The solved PyPSA network (capacities, dispatch, duals). Re-load with ``pypsa.Network(path)`` for custom analysis. ``config_run.yaml`` **Run fingerprint** — the complete merged configuration used for this run (see :ref:`outputs-config-run`). ``network_comp_allocation.pkl`` Mapping of components to technology agents (internal use). ================================ ==================================================== The network **topology diagrams** live in ``plots/``: - ``_PRE.svg`` — the network *before* solving (all candidate components). - ``_OPT.svg`` — the *optimal* network (only built components, sized). - ``*.dot`` — Graphviz source for the SVGs. .. _outputs-config-run: ``config_run.yaml`` — the run fingerprint ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``config_run.yaml`` is the **single authoritative record** of every parameter used in a run. It is the merged result of all three config-file pairs (default + user override) written to disk after the solve completes. **Structure:** .. code-block:: yaml config: # merged config.default.yaml + config.yaml run_name: my_scenario CO2_cost: 100 targets: driver: demand demand_H2: 200000 ... n_flags: biogas: true renewables: true ... optimization: solver: highs solver_profile: highs-fast clustering: temporal: resolution: 3h ... n_config: # merged n_config.default.yaml + n_config.yaml battery: # keyed by component name initial capacity: 0 expansion: true cost factor: 1 max hours: 2.0 biomass boiler: initial capacity: 30 expansion: false remaining_investment_fraction: 0.1 construction_year: 2020 ... n_options: # options: section of n_config (DH, biomass markets, …) DH: enable: true price: 80.0 peak capacity: 80.0 pellets market: enable: true price: 60.0 ... plots_config: # merged plots_config.default.yaml carrier_colors: ... plotting: ... **How to use it:** - **Reproduce a run** — copy ``config:`` back to ``config/config.yaml`` and ``n_config:`` + ``n_options:`` back to ``config/n_config.yaml`` (keep only keys that differ from the defaults). - **Compare runs** — diff two ``config_run.yaml`` files to see exactly what changed between scenarios. - **Audit brownfield settings** — ``n_config`` entries with ``initial capacity > 0`` and ``expansion: false`` are the fixed brownfield assets; ``remaining_investment_fraction`` tells you how much residual CAPEX was charged. - **Check solver** — the ``optimization`` block records which solver and profile were actually used. --- .. _outputs-capacities: Capacities — *what gets built* ------------------------------ - **Plot** ``Opt_capacities_SP_vs_WS.png`` — optimal installed capacity per technology. In stochastic runs it contrasts the single shared design (**SP**, stochastic program) against the per-scenario **WS** (wait-and-see) optima. - **CSV** ``optimal_capacities.csv`` — capacity, fixed cost (€/y), unit and energy capacity per component; ``opt_capacities_SP_vs_WP.csv`` — the SP-vs-WS table behind the plot. --- .. _outputs-operation: Operation — *how it runs* ------------------------- - **Plot** ``CF_operation_by_scenario.png`` — capacity factor / utilisation of each technology (how hard each asset works). - **Plot** ``Operation_heat_maps_by_scenario.png`` — hour-of-day × day-of-year dispatch heat maps, revealing daily and seasonal operating patterns. - **Plot** ``CF_operation_heat_maps_by_scenario.png`` — the same as heat maps but normalised to capacity factor. - **CSV** the underlying time series are in ``full_component_table.csv`` and the network ``.nc``. --- .. _outputs-inputs: Inputs — *the exogenous drivers* -------------------------------- - **Plot** ``inputs_LDC_by_scenario.png`` — load-duration curves of the exogenous inputs (electricity price, gas price, renewable capacity factors). Sorting each series high→low shows how often prices/resources are favourable. --- .. _outputs-shadow-prices: Internal-market shadow prices ----------------------------- The dual of each carrier's nodal balance is its **shadow price** (€/MWh) — the marginal value of that energy/material inside the plant. - **Plot** ``shd_prices_mean_bar.png`` — the **energy-weighted mean** shadow price per internal bus (the headline "what is H₂/CO₂/heat worth here" number). - **CSV** ``shadow_prices_mean.csv`` — columns ``bus``, ``energy weighted mean (EUR/MWh)`` (the data behind that bar chart). - **Plot** ``shd_prices_ldc.png`` — duration curve of each shadow price (how often it is high or low). - **Plot** ``shd_prices_violin.png`` — violin showing the full snapshot distribution. The **crimson line** is the *snapshot-weighted* mean (the plain arithmetic average over all hours); the **blue diamond** is the *energy-weighted* mean (same value as ``shadow_prices_mean.csv``/ the bar chart, weighted by the energy flowing through the bus). The gap between the two is informative: if the energy-weighted mean sits above the snapshot mean, the carrier is worth most precisely when it flows most. --- .. _outputs-srmc: SRMC & merit order ------------------ - **Plot** ``srmc_by_technology.png`` — the **short-run marginal cost** time series per producing technology vs the product shadow price; where SRMC ≤ price the unit is *in merit* and runs. - **CSV** ``srmc_by_technology.csv`` — per snapshot & link: ``SRMC_EUR_per_MWh``, ``dispatch_MW``, ``π_product_bus`` (product shadow price), ``in_merit`` flag. --- .. _outputs-costs: System cost & levelised cost ---------------------------- - **Plot** ``TSC_by_carrier.png`` — **total system cost** split by carrier / technology (annualised CAPEX + OPEX); ``TSC_by_agents.png`` — the same split by plant *agent*. - **CSV** ``TSC_by_carrier.csv`` / ``TSC_by_agent.csv`` — columns ``scenario``, ``group``, ``capex``, ``opex``, ``total``, ``probability``. - **Plot/CSV** ``lcop_by_technology.png`` / ``lcop_by_technology.csv`` — the **levelised cost of production** per product, broken into CAPEX, OPEX, indirect OPEX, by-product revenue, annual production and annual profit. ``lcop_kkt_by_technology.csv`` is the dual/KKT cross-check (zero-profit condition — see :ref:`guide-economic-analysis`). - **CSV** ``pypsa_statistics.csv`` — PyPSA's standard statistics (optimal/installed capacity, supply, capacity factor, CAPEX, OPEX, revenue, market value). - **CSV** ``cost_assumptions.csv`` — the techno-economic inputs actually used. --- .. _outputs-full-table: The master data table ---------------------- ``full_component_table.csv`` is the **one-stop data source**: one row per component with its plant, carrier, expandability, initial vs optimal capacity, capacity factor, curtailment, specific and total fixed/variable costs, production and revenue. Most figures above are aggregations of this table — start here when a plot raises a question. .. seealso:: :ref:`guide-economic-analysis` (theory) · :ref:`wildcards` (network-name encoding) · :ref:`tutorial-1-greenfield` (worked example) --- .. _outputs-rerun: Repeating or resetting an analysis ----------------------------------- To cleanly re-run an analysis from scratch, delete **both** the output folder and the corresponding intermediate resources folder, then re-run Snakemake: .. code-block:: bash # replace with your run_name (e.g. my_scenario) rm -rf outputs/single_analysis// rm -rf resources// snakemake --cores 4 The ``resources//`` folder holds the pre-built ``.nc`` and the component-allocation ``.pkl`` that Snakemake uses as intermediate inputs. Deleting it forces ``build_network`` and ``solve_network`` to re-run in full. If you only changed the configuration and want to **re-solve without rebuilding** the network, delete only the output folder and the OPT network inside resources (Snakemake tracks the PRE network as an input to solve): .. code-block:: bash rm -rf outputs/single_analysis// snakemake --forcerun solve_network --cores 4 .. tip:: The long file-name prefix (for manual inspection) is printed by ``snakemake -n`` (dry-run) and is also visible as the file-name stem inside ``outputs/single_analysis//networks/``.