Changelog

Here, you'll find notable changes for each version of PREP-SHOT.

Version 0.1.0 - Jun 24, 2024

  • PREP-SHOT model is released with basic functionality for energy expansion planning.

  • Linear programming optimization model for energy systems with multiple zones.

  • Support for solvers such as Gurobi, CPLEX, MOSEK, and GLPK via Pyomo.

  • Input and output handling with pandas and Xarray.

Version 0.1.1 - Jul 11, 2024

Added

  • Add an example, expansion of Southeast Asia Mainland power system considering hydropower of Lower Mekong River.

  • Update the documentation with a docstring for each function and class.

  • Add the Semantic Versioning Specification.

Fixed

Changed

  • Support for solvers such as GUROBI (Commercial), COPT (Commercial), MOSEK (Commercial), and HiGHS (Open source) via PyOptInterface.

  • Change default solver to HiGHS.

  • Change the code comment style to NumPy.

  • Change the code style to PEP8.

  • Categorize constraint definitions based on type (co2, cost, demand, generation, hydro, investment, nondispatchable, storage, transmission) for better organization.

  • Split rule.py class into serveral smaller, focused classes according to categorized constraint definitions.

  • Simplify model by replacing intermediate constraints with direct expressions.

  • Extract new modules solver.py, output_data.py, and set_up.py from run.py and utils.py.

  • Remove parameters.py into set_up.py.

  • Refactor and improve comments and function names for clarity and conciseness.

Deprecated

  • Removed dependency on Pyomo due to high memory usage and slow performance for large-scale models. For you reference.

Version 0.1.2 - Jul 22, 2024

Added

  • Added mathematical notations to the constraint module.

  • Added a test script for prepshot.utils.

Fixed

  • Fixed the format of the API reference.

  • Fix code blocks of documentation.

  • Updated Contribution.rst to include context on running tests and code style checks.

  • Defined explicit data types for inputs and outputs of functions for better type checking and readability.

  • Added pyoptinterface._src.core_ext to Pylint's extension package allow list to resolve cpp-extension-no-member warning.

Changed

  • Updated model.py to keep necessary decision variables and use expressions for intermediate variables instead of direct determination.

  • Refactored extract_results_non_hydro in output_data.py to extract common features for different variables, simplifying the code.

  • Removed definitions of complex sets and opted for simple sets wherever possible to streamline the code.

  • Refactor: Organize import order of modules according to PEP 8 guidelines: (1) Grouped standard library imports at the top; (2) Followed by third-party library imports; (3) Local application/library imports at the bottom.

Version 1.0 - Jul 21, 2025

First v1.0 release. Aggregates all changes since v0.1.2 (PRs #23-#34). See the GitHub release notes for the full list. Notable highlights:

  • Bug fixes and refinements to constraint definitions.

  • Documentation improvements and added publications.

  • Stabilized PyOptInterface integration.

Version 1.1.0 - May 2, 2026

Note

v1.x is a rapid-evolution series. Breaking input-format changes may occur on minor version bumps. API and input-schema stability will be promised with v2.0.0. See the Stability page.

Added

  • Time-varying capacity bounds: technology_upper_bound, technology_lower_bound, new_technology_upper_bound, and new_technology_lower_bound are now indexed by (zone, tech, year).

  • Re-introduced technology_lower_bound constraint on cap_existing.

  • Carbon market:

    • carbon_offset[y, z] decision variable.

    • carbon_tax cost term in the objective.

    • carbon_offset_price cost term in the objective.

    • Fractional carbon_offset_limit constraint (offset <= rate * raw zonal emissions).

    • carbon_capacity[y, z] now nets out purchased offsets.

  • southeast_asia/ dataset migrated to the new schema.

  • params.json now stamps _schema_version: 1.

Changed (Breaking)

  • Bound input files reshaped from (tech, zone) to (zone, tech, year).

  • Four new required input files: technology_lower_bound, carbon_tax, carbon_offset_price, carbon_offset_limit. The shipped defaults are zero-filled, which keeps the carbon-market feature dormant.

  • params.json files without _schema_version are rejected by the loader.

Version 1.1.1 - May 2, 2026

Added

  • prepshot.load_data.CURRENT_SCHEMA constant and a check_schema() guard that validates params.json carries a compatible _schema_version before any input is loaded.

  • tests/test_schema_version.py covering the happy path plus rejection of missing, older, and newer schema stamps.

Changed

  • process_data now filters out keys beginning with _ (such as _schema_version) before iterating the parameter list, so metadata stamps cannot accidentally be treated as input files.

Fixed

  • Legacy params.json files (pre-v1.1.0, no _schema_version stamp) now produce a clear migration hint instead of a downstream KeyError / FileNotFoundError.

Version 1.1.2 - May 2, 2026

Added

  • tests/test_regression.py: end-to-end regression test that runs the full python run.py flow on the canonical input/ dataset and locks in the final-iteration objective (1.8793771299e+11) with a 1 % tolerance. Set PREPSHOT_SKIP_SLOW=1 to skip it (about 150 s).

Fixed

  • prepshot/set_up.py now skips underscore-prefixed metadata keys (e.g. _schema_version) when iterating params.json to build the argparse list. Without this fix v1.1.1 raised TypeError: 'int' object is not subscriptable at the start of initialize_environment, breaking python run.py end-to-end despite the schema guard inside process_data working in isolation.

Version 1.2.0 - May 2, 2026

PREP-SHOT is now installable as a Python package.

Added

  • pyproject.toml at the repo root, declaring prepshot as an installable package with pinned-but-flexible dependency floors. Allows pip install -e . for local development and pip install prepshot once published to PyPI.

  • prepshot/cli.py -- main() entry point that looks for config.json and params.json in the current working directory.

  • prepshot/__main__.py -- enables python -m prepshot invocation.

  • Console-script entry point prepshot (declared in [project.scripts]).

Changed

  • run.py is now a thin backward-compatible shim that delegates to prepshot.cli.main. It preserves the legacy file-relative path behavior (config.json / params.json next to run.py), so existing python run.py workflows continue to work unchanged.

Migration notes

  • New: pip install -e . from the repo root, then run prepshot (or python -m prepshot) from any directory containing config.json and params.json.

  • Existing: python run.py still works as before -- no action needed.

Version 1.3.0 - May 2, 2026

Features can now ship with optional input files. New parameters declared "required": false in params.json no longer force every existing input directory to provide a matching Excel file -- a sensible default is used when the file is absent.

Added

  • params.json entries support two new keys:

    • "required" (bool, default true): when false, a missing input file is tolerated.

    • "default" (any, default {}): the value substituted when an optional input file is missing. Scalar defaults are wrapped in a defaultdict so model-side tuple-key lookups (params['foo'][z, y]) keep working unchanged.

  • tests/test_optional_inputs.py covers required-missing-terminates, optional-missing-uses-scalar-default, and optional-missing-no-default (empty dict).

Changed

  • The four carbon-market / technology_lower_bound entries in params.json are now marked "required": false, "default": 0, so input directories that pre-date v1.1.0 (or any future input dir that does not use these features) no longer need to ship the zero-filled Excel files.

  • README.md now documents the editable-install path (pip install -e .) and the prepshot / python -m prepshot console-script entry points introduced in v1.2.0.

  • doc/source/Installation.rst extended with a "Use PREP-SHOT as a Python library" section that shows both the high-level prepshot.cli.main(root_dir=...) entry point and the low-level initialize_environment / create_model / solve_model flow for downstream code that imports PREP-SHOT programmatically.

Removed

  • tests/test_prepshot.py -- removed. It exercised the legacy load_data, read_four_dims, read_three_dims etc. APIs that were replaced in v0.1.1 by the generic read_excel helper. The test file had been a silent import error since then.

Version 1.3.1 - May 2, 2026

Security / dependency hygiene release. Bumps declared dependency floors to clear known CVEs in the previously pinned old releases (the v0.1.x-era pins were 2-4 years old).

Changed

  • pyproject.toml and requirements.txt floors raised to:

    • numpy>=1.26.0,<2.0

    • scipy>=1.11.4

    • pandas>=1.5.3,<2.0

    • xarray>=2023.4.0,<2024

    • openpyxl>=3.1.2

    • xlsxwriter>=3.1.0

  • pandas and numpy are temporarily capped below 2.0 because pandas 2.x's DataFrame.unstack no longer accepts duplicate column-level values (which the wide input format relies on). The cap will be lifted in a future release once read_excel is rewritten to not rely on unstack.

Tested with

  • numpy 1.26.4, scipy 1.13.1, pandas 1.5.3, xarray 2023.12.0, openpyxl 3.1.5, xlsxwriter 3.2.9, pyoptinterface 0.2.5, highsbox 1.7.0. Full test suite (13 tests) passes.

Migration notes

  • Existing environments should run pip install -U -r requirements.txt (or pip install -e . if you use the editable install) to pick up the bumped dependency versions.

Version 1.3.2 - May 2, 2026

Continuous-integration release. The full unit-test suite now runs automatically on every push and pull request via GitHub Actions.

Added

  • .github/workflows/test.yml: matrix-tests on Python 3.9 / 3.10 / 3.11. Runs the fast unit tests (schema, optional inputs, utils) on every push and PR; runs the slow end-to-end regression test only on pushes to main to keep PR feedback fast.

The fast tests give contributors a quick green/red signal on every PR; the regression test on main catches any change that would alter the model's optimal objective on the canonical input/ dataset.

Version 1.4.0 - May 2, 2026

PREP-SHOT now supports long-format ("tidy") CSV inputs as a parallel option to the wide-Excel format. New parameters can be born long-format without disturbing existing wide-format inputs; existing parameters can migrate one at a time when convenient.

Why long-format

Adding a dimension to a long-format file is a non-breaking column add, not a reshape. This eliminates the recurring "the bound files were reshaped from (tech, zone) to (zone, tech, year)" class of breakage, which historically required migrating every existing input directory.

Long-format is the canonical input shape used by OSeMOSYS, GenX, Switch, and PyPSA -- researchers familiar with those tools will find the convention intuitive.

Added

  • prepshot/load_data.py::read_long_csv -- new reader for tidy CSVs. Convention: dimension columns first, value column last. Output dict shape matches what the wide-format reader produces for the same parameter (scalar keys for 1 dim, tuple keys for 2+ dims), so model code is unchanged regardless of which format is on disk.

  • params.json entries support a new "format": "long" key. When set, the loader looks for <file_name>.csv instead of <file_name>.xlsx, and skips the wide-format-specific keys (index_cols, header_rows, unstack_levels, first_col_only).

  • tests/test_long_format.py -- 7 unit tests covering 1-dim, 2-dim, and 3-dim CSV reading; NaN-row dropping; format dispatch in load_excel_data; and the optional-input fallback for long-format.

Changed

  • carbon_tax migrated from wide-Excel to long-CSV as the working demonstration of the new format. input/carbon_tax.csv and southeast_asia/carbon_tax.csv replace the corresponding carbon_tax.xlsx files. The data and model behavior are identical -- the regression test passes unchanged at 1.879e+11 objective.

Migration notes

  • No action needed for existing input directories that are not based on the canonical input/ -- their wide-format files keep working.

  • Users with custom carbon_tax.xlsx files who pull v1.4.0 should convert them to carbon_tax.csv (zone, year, value columns) and drop the old xlsx, since params.json now declares carbon_tax as long-format.

  • Future features should prefer long-format from the start.

Version 1.5.0 - May 2, 2026

Note

Schema bump 1 -> 2. This is a breaking input-format change. Existing input directories must be migrated. See migration notes below.

Every input is now a CSV. The unstack-based wide-Excel code path is no longer used by the loader at runtime, though read_excel remains in prepshot/load_data.py for the migration tool's benefit (a full v1.4.x -> v1.5.0 migration tool is on the v1.6.0 roadmap).

The pandas<2.0 cap from v1.3.1 is still in place in v1.5.0 because read_excel (used by the migration tool) calls unstack. Once the migration tool no longer needs read_excel (v1.6.0), the cap will be lifted.

Added

  • tools/migrate_to_long.py -- runnable migration script that converts a directory of wide-format Excel inputs to long-format CSVs using the legacy params.json spec for shape information. Run as:

    python tools/migrate_to_long.py /path/to/your/input_dir

  • Tailored schema-1 -> schema-2 migration hint in check_schema: users feeding a v1.4.x or earlier params.json now get a clear pointer at tools/migrate_to_long.py instead of a generic version mismatch error.

Changed (Breaking)

  • params.json schema bumped to _schema_version: 2. Files with _schema_version: 1 are rejected with a migration hint.

  • All Group-1/2 parameters in input/ and southeast_asia/ have been converted from wide .xlsx to long .csv:

    • Capacity / cost: technology_*, transmission_line_*, capacity_factor, new_technology_*_bound, lifetime, fuel_price, technology_portfolio, technology_type, historical_capacity, initial_energy_storage_level, charge_efficiency, discharge_efficiency, energy_to_power_ratio, ramp_up, ramp_down.

    • Time-series / spatial: demand, inflow, predefined_hydropower, distance, reservoir_storage_upper_bound, reservoir_storage_lower_bound, initial_reservoir_storage_level, final_reservoir_storage_level.

    • Domain: carbon_emission_limit, emission_factor, carbon_offset_price, carbon_offset_limit, discount_factor.

  • params.json entries for migrated parameters now declare just "format": "long" plus drop_na (and required/default if optional). The wide-format-specific keys (index_cols, header_rows, unstack_levels, first_col_only) are dropped.

Annotation columns

Long-format CSVs now support annotation columns -- human-readable metadata that lives alongside the data but is ignored by the model loader. Following the TransitionZero convention:

  • Every long CSV has a unit column (e.g. MWh, USD/tonneCO2, m3/s).

  • The eight per-field reservoir CSVs additionally have a name column with the human-readable station name (e.g. "Grand Coulee") alongside the stcd ID.

The loader filters out columns whose name (case-insensitive) is in the annotation set {unit, units, name, commodity, comment, comments, description, desc, note, notes, label}, or that ends in _name (zone_name, tech_name, etc.). These columns never appear in the dim-key tuples, so model code is unaffected.

Two new unit tests cover annotation handling: test_unit_column_filtered and test_name_and_other_annotation_columns_filtered.

Column renames for clarity

Several cryptic abbreviations have been replaced with self-describing names across the input CSVs and model code:

  • stcd (and station for params that previously used that name) -> station_id everywhere -- inputs, model.station, params['station_id'], local variables.

  • POWER_ID / NEXTPOWER_ID (water_delay_time) -> upstream_station_id / downstream_station_id.

  • Z / Q / V in the piecewise-function lookups -> tailrace_level / discharge / forebay_level / volume (the same axis was being repurposed: Z meant tailrace level in one file and forebay level in the other).

  • coeff -> coefficient, GQ_max -> generation_flow_max, N_min / N_max -> capacity_min / capacity_max for the per-field reservoir CSVs.

Group 3 migration

The four "table-shaped" parameters are now CSVs as well:

  • water_delay_time, reservoir_tailrace_level_discharge_function, reservoir_forebay_level_volume_function -- already 3-column long internally; resaved as CSV and loaded via the new format: "table" dispatch (returns a DataFrame for downstream groupby/column access).

  • reservoir_characteristics -- previously one wide table with ~13 fields per station -- has been split into 8 single-field long CSVs, one per field the model actually uses:

    • reservoir_zone, reservoir_coefficient, reservoir_outflow_min, reservoir_outflow_max, reservoir_generation_flow_max, reservoir_capacity_min, reservoir_capacity_max, reservoir_head.

    Four field renames in the process (coeff -> coefficient, GQ_max -> generation_flow_max, N_min -> capacity_min, N_max -> capacity_max) for clarity. The three descriptive fields (name, short_name, type) that the model never referenced have been dropped.

  • params.json gains a new "format": "table" value alongside "format": "long" for parameters consumed as DataFrames rather than dicts.

Model code (9 sites in hydro.py / head_iteration.py / load_data.py) updated to read from the new per-field params: params['reservoir_characteristics']['<field>', s] becomes params['reservoir_<field>'][s]. The end-to-end regression test confirms the model objective is unchanged after the refactor.

Migration notes

The shipped input/ and southeast_asia/ directories are already in v1.5.0 shape. For custom input directories (e.g. scenario forks such as input_s100_baseline_*):

  • tools/migrate_to_long.py covers the dict-shape parameters; run:

    python tools/migrate_to_long.py /path/to/your/input_dir

  • The four Group-3 parameters and reservoir_characteristics's field-split are not yet automated. Until the v1.6.0 expanded migration tool ships, use the shipped input/ directory as a reference for the new file shapes, or open an issue if you need porting help.

Custom params.json files (i.e. anyone who has forked params.json) will need to mirror the canonical v1.5.0 file shipped in this release: metadata stamp "_schema_version": 2, then minimal long-format entries ("format": "long" / "format": "table") without the legacy wide-format keys.

Version 1.6.0 - May 3, 2026

Cleanup release. The wide-Excel reading machinery is removed, the migration tool is rewritten to work under pandas >= 2.0, and the pandas<2.0 / numpy<2.0 caps from v1.3.1 are lifted.

Removed

  • prepshot.load_data.read_excel -- deleted. Nothing in the runtime path used it after v1.5.0; the migration tool now has its own pd.read_excel call.

Changed

  • tools/migrate_to_long.py rewritten to be self-contained:

    • Bundles its own copy of the v1.4.x wide-format spec (since the on-disk params.json is now v1.5.0 long-format and no longer carries that information).

    • Uses a custom flatten_wide_to_dict helper that replicates the v1.4.x unstack-based key ordering by direct cell iteration. Works under pandas >= 2.0 (which rejects unstack on DataFrames with duplicate column-level values).

    • Verified to produce byte-equivalent output to the shipped v1.5.0 input/ CSVs (modulo annotation columns) for all 38 dict-shape parameters.

  • pyproject.toml and requirements.txt floors raised:

    • numpy>=1.26.0 (no upper cap)

    • pandas>=2.0.0 (no upper cap)

    • xarray>=2023.10.0 (no upper cap)

  • Tested with numpy 2.0.2, pandas 2.3.3, xarray 2024.7.0; full test suite passes (22 tests, end-to-end regression objective unchanged at 1.879e+11).

Fixed

  • np.Inf references in prepshot/_model/investment.py and prepshot/_model/co2.py replaced with np.inf (np.Inf was removed in numpy 2.0).

Version 1.7.0 - May 3, 2026

Data-model cleanup ahead of the v1.8.0 PyPSA-style API. The existing-fleet representation is reshaped from an awkward (zone, tech, age) "historical capacity" table to a tidy (tech, zone, commission_year) "existing fleet" table, and the single-purpose technology_type file is renamed to technologies so it can grow into a per-tech registry.

Added

  • input/existing_fleet.csv and southeast_asia/existing_fleet.csv -- one row per existing-capacity block (tech, zone, commission year, capacity). Sparse representation: only non-zero entries are listed.

  • technologies.csv (replaces technology_type.csv) with columns tech and type. Designed to grow into a richer per-tech registry (e.g. description, category, co2_intensity_class) alongside the v1.8.0 API work.

Removed

  • historical_capacity.csv -- replaced by existing_fleet.csv. The age dimension is gone; capacity blocks now record an explicit commission_year, which is unambiguous and survives schedule shifts.

  • technology_portfolio.csv -- file existed in the schema but was never referenced by any model rule (all values were 0 in the shipped data). Dead code; deleted.

  • technology_type.csv -- renamed to technologies.csv (see Added).

Changed

  • prepshot/_model/investment.py::tech_lifetime_rule rewritten:

    • Before: sum historical_capacity[zone, tech, age] for age in [0, lifetime - service_time).

    • After: sum existing_fleet[tech, zone, commission_year] for all commission years where commission_year <= y < commission_year + lifetime.

    The two formulations are mathematically equivalent for the shipped data; the regression test confirms the model objective is unchanged at 1.879e+11.

  • prepshot/load_data.py::extract_sets derives the tech set from technologies (was technology_type).

  • prepshot/model.py::define_basic_sets reads tech categories from technologies (was technology_type).

  • prepshot/_model/hydro.py reads the hydro-tech list from technologies (was technology_type).

Version 1.8.0 - May 3, 2026

PyPSA-style data model. The fixed resource_type enum is replaced with a free-form carrier string plus boolean per-tech behavior flags; any tech can now bound its dispatch with time-varying p_max_pu / p_min_pu profiles, removing the need for separate capacity_factor and must_run paths. Hydro plants are first-class techs (carrier hydro) instead of an aggregate Hydro. Input files gain consistent domain prefixes (tech_, reservoir_, transmission_, storage_, policy_, economic_) and adopt max / min instead of mixed upper_bound / lower_bound. Existing capacity and candidates are now structured symmetrically for both plants and transmission lines.

The regression test confirms the model objective is unchanged at 1.880e+11 for the canonical input/ dataset across all intermediate refactors.

Added

  • input/tech_registry.csv (was technologies.csv) -- per-tech registry with columns tech, name, carrier, is_storage. Replaces the rigid resource_type enum.

  • input/tech_max_gen_profile.csv and input/tech_min_gen_profile.csv -- optional time-varying upper / lower bounds on dispatch (PyPSA's p_max_pu / p_min_pu). Subsumes the capacity_factor and must_run paths.

  • input/transmission_candidates.csv -- symmetric counterpart to tech_candidates.csv for new transmission lines, with columns zone1, zone2, year, unit, capacity_min, capacity_max.

  • input/transmission_existing.csv -- now keyed by (zone1, zone2, commission_year) and respects transmission_lifetime, matching the existing-fleet structure for plants.

  • Per-zone discount factor: input/economic_discount_factor.csv is keyed by (zone, year); cost factors propagate through cost.py.

  • Custom carbon-emission-limit regions in input/policy_carbon_emission_limit.csv: each row carries a comma-separated zones field, allowing arbitrary multi-zone caps without per-zone duplication.

Removed

  • prepshot/_model/nondispatchable.py -- subsumed by the unified gen_up_bound_rule / gen_low_bound_rule in prepshot/_model/generation.py.

  • predefined_hydropower.csv and the must_run else-branch in hydro.py -- replaced by tech_min_gen_profile.csv.

  • capacity_factor.csv -- replaced by tech_max_gen_profile.csv.

Changed

  • Naming sweep: all upper_bound / lower_bound files, param keys, and DataFrame columns renamed to max / min:

    • tech_upper_bound.csv -> tech_capacity_max.csv

    • tech_lower_bound.csv -> tech_capacity_min.csv

    • reservoir_storage_upper_bound.csv -> reservoir_storage_max.csv

    • reservoir_storage_lower_bound.csv -> reservoir_storage_min.csv

    • tech_candidates.csv columns lower_bound / upper_bound -> capacity_min / capacity_max

    • transmission_candidates.csv columns: same rename.

  • File prefixes: input files regrouped by domain. hydro_inflow.csv -> reservoir_inflow.csv; charge_efficiency.csv -> storage_charge_efficiency.csv; carbon_tax.csv -> policy_carbon_tax.csv; discount_factor.csv -> economic_discount_factor.csv; etc.

  • Hydro plants are first-class techs. Each plant appears in tech_registry.csv with carrier='hydro'; reservoir parameters are keyed per station (no main_hydro aggregation).

  • Demand units. demand.csv now carries instantaneous power in MW (PyPSA convention). The nodal balance constraint multiplies by dt to convert to MWh per timestep, matching gen / charge which were already in MWh.

  • prepshot/model.py::define_basic_sets derives hydro_tech, storage_tech, and dispatchable_tech from the registry's carrier and is_storage columns rather than a fixed enum.

  • prepshot/_model/generation.py defines a single pair of generation bound rules using p_max_pu / p_min_pu lookups, replacing the per-resource-type branches.

  • prepshot/_model/transmission.py builds existing transmission capacity by summing over (zone1, zone2, commission_year) entries still in service, mirroring tech_lifetime_rule.

Version 1.8.1 - May 3, 2026

Fixed

  • prepshot/_model/investment.py: retirement check now looks up lifetime at the commissioning year, not the current modeled year, so units built at vintage cy retire after cy + lifetime[te, cy] regardless of any later parameter changes. Bug present in two locations:

    • tech_lifetime_rule (existing-fleet retirement, introduced by the v1.7.0 refactor): lifetime[te, y] -> lifetime[te, cy].

    • remaining_capacity_rule (new-build retirement, original PR #47 by Quan YUAN): lt[te, y] -> lt[te, yy].

    The shipped input/tech_lifetime.csv has constant lifetime across years for every tech, so the regression objective is unchanged at 1.880e+11. The bug only manifests when users supply time-varying lifetimes (e.g. modeling tech improvement).

Version 1.9.0 - May 3, 2026

Optional finance module: weighted-average cost of capital (WACC) for new-build investment, plus public-debt accounting and caps. Backported from the dev branch (commit bfd9de6, "add finance module") and adapted to the v1.8.x long-format / per-zone / max-naming conventions. The feature is OFF by default; the regression objective is unchanged at 1.880e+11 for the canonical input/ dataset.

Added

  • prepshot/_model/finance.py with AddFinanceConstraints:

    • public_debt_newtech[y, z, te] -- discounted public-debt obligation incurred by each new-tech investment, exported in the year.nc results.

    • System-wide cap: sum over (z, te) of public_debt_newtech[y, z, te] <= public_debt_max_system[y]. Skipped when missing or +inf.

    • Per-zone cap: sum over te of public_debt_newtech[y, z, te] <= public_debt_max_zone[z, y]. Same skip behavior.

  • prepshot/utils.py::calc_interest_rate -- weighted-average cost of capital from public-debt / private-debt / equity tranches.

  • Seven new optional inputs (all required: false): finance_public_debt_ratio.csv (per-tech), finance_private_debt_ratio.csv (per-tech), finance_cost_of_public_debt.csv (per-tech, per-zone), finance_cost_of_private_debt.csv (per-tech, per-zone), finance_cost_of_private_equity.csv (per-tech, per-zone), finance_public_debt_max_system.csv (per-year), finance_public_debt_max_zone.csv (per-zone, per-year).

Changed

  • prepshot/load_data.py::compute_cost_factors -- when finance inputs are present, inv_factor[tech, year, zone] discounts the construction outlay at the project-level WACC instead of the zonal discount rate. Fixed/variable/transmission factors continue to use the zonal discount rate. With finance OFF (no inputs), WACC == discount_rate and the legacy behavior is preserved.

  • prepshot/model.py::create_model -- AddFinanceConstraints is wired in only when params['public_debt_ratio'] is populated, so users without finance inputs see no extra variables, constraints, or output variables.

  • prepshot/output_data.py::extract_results_non_hydro -- emits public_debt_newtech only when finance is enabled.

Notes

  • The dev-branch commit used wide-Excel inputs and np.Inf; this backport uses long-format CSV, np.inf, and the v1.8.0 max/min naming convention (e.g. public_debt_max_system rather than public_debt_upper_bound_system).

  • Cap rules treat both missing entries and +inf as "no constraint", matching the candidates / carbon-emission-limit conventions elsewhere in the model.

Version 1.9.1 - May 3, 2026

Added

  • New output variable shadow_price_demand -- the dual of the nodal power-balance constraint, exposing the locational marginal price (LMP) at each (hour, month, year, zone). Sign is flipped from the raw dual so positive values mean "more expensive to serve more demand", matching the convention used by PyPSA / Switch / GenX. Discounted to NPV; divide by var_factor[year, zone] to recover undiscounted real-year prices.

  • prepshot/output_data.py::create_data_array gained an extractor parameter so the same helper can lift either primal values (model.get_value, the default) or duals (model.get_constraint_dual) out of a tupledict.

Notes

  • The shadow-price extraction is wrapped in a try/except: if the solver does not return duals (e.g. for a MIP solve, or after an infeasible run), a warning is logged and the variable is omitted from the NetCDF file rather than aborting the run.