Mannu
314127effc
- Pydantic schemas: SolarConfig, WindConfig, BessConfig, GenerationResult - Catalog: synthetic 8760h profiles for RJ/KA/GJ solar and wind; LRU-cached loader - generation/solar.py: 25yr hourly simulation (DC losses, inverter, clipping, soiling, degradation) - generation/wind.py: power-law shear, piecewise power curve, wake/electrical losses, degradation - generation/bess_state.py: SOH-based capacity degradation with augmentation schedule - CLI: remodel --input scenario.json --output gen.parquet (Typer upgraded to 0.25.1 for Click 8.3 compat) - 43 unit tests, 97.4% coverage; mypy strict + ruff clean - S1-T10 parity gate: placeholder fixture + skipped integration tests (awaiting Nagasamudra data) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
118 lines
4.3 KiB
Markdown
118 lines
4.3 KiB
Markdown
# Generation Module
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`remodel_engine.generation` — 25-year hourly simulation for solar, wind, and BESS capacity.
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## Functions
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### `simulate_solar(config: SolarConfig) → pd.DataFrame`
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Runs the full solar model chain for 25 years at hourly resolution.
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**Input**: `SolarConfig` (see `schemas/generation.py`)
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**Output**: DataFrame with 219 000 rows (25 × 8 760), columns:
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| Column | Type | Description |
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|--------|------|-------------|
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| `year` | int16 | 1–25 |
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| `hour` | int16 | 0–8759 (within each year) |
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| `dc_power_mw` | float64 | DC output after wiring + mismatch losses |
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| `ac_power_mw_pre_clip` | float64 | After inverter + AC losses, before clipping |
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| `ac_power_mw` | float64 | Final AC output (clipped, availability, soiling, degradation) |
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| `degradation_factor` | float64 | Year-level multiplier (Y1 = 1−deg_y1, monotonically ↓) |
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**Model chain**:
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```
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irradiance_norm × capacity_dc_mwp × (1 − dc_loss) → dc_power_mw
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dc_power_mw × inverter_η × (1 − ac_loss) → ac_power_mw_pre_clip
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min(ac_power_mw_pre_clip, capacity_ac_mw) → clipped
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clipped × availability × (1 − soiling_monthly) → ac_soiled_base
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ac_soiled_base × degradation_factor → ac_power_mw
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```
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**Degradation**:
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- Year 1: `1 − degradation_y1` (LID + initial)
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- Year n: `(1 − degradation_y1) × (1 − degradation_annual)^(n−1)`
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---
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### `simulate_wind(config: WindConfig) → pd.DataFrame`
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**Input**: `WindConfig`
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**Output**: DataFrame with 219 000 rows, columns:
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| Column | Type | Description |
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|--------|------|-------------|
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| `year` | int16 | 1–25 |
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| `hour` | int16 | 0–8759 |
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| `wind_speed_hub` | float64 | Hub-height wind speed (m/s) |
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| `power_fraction` | float64 | Power curve output (0–1) |
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| `ac_power_mw` | float64 | Final AC output |
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| `degradation_factor` | float64 | 1.0 in Y1, `(1−deg_annual)^(n−1)` for Y_n |
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**Model chain**:
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```
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wind_speed_ref × (hub_h / ref_h)^shear_α → wind_speed_hub
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power_curve(wind_speed_hub) → power_fraction
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power_fraction × capacity_mw → gross_mw
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gross_mw × (1−wake) × (1−electrical) × availability → net_base
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net_base × degradation_factor → ac_power_mw
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```
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**Power curve**: piecewise-linear, cut-in 3 m/s, rated at 12.5 m/s, cut-out 25 m/s. Defined in `catalog/defaults.py::WIND_POWER_CURVE_POINTS`.
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---
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### `simulate_bess_capacity(config: BessConfig, cum_cycles_per_year: list[float]) → pd.Series`
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Models physical battery capacity over 25 years. Does **not** simulate dispatch.
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**Input**:
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- `config`: `BessConfig` with nameplate MWh, design_cycles, eol_soh, augmentation_schedule
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- `cum_cycles_per_year`: cumulative full-equivalent cycles at end of each year (from dispatch module, or a conservative default)
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**Output**: `pd.Series` indexed 1–25, values = usable MWh
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**Degradation**:
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```
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SOH = max(eol_soh, 1 − cum_cycles/design_cycles × (1 − eol_soh))
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usable_mwh = (nameplate + augmentation_so_far) × SOH
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```
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---
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## Catalog profiles
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Bundled 8760-hour CSV files in `catalog/profiles/`:
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| Location | Solar CSV | Wind CSV | Notes |
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|----------|-----------|----------|-------|
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| RJ | `solar/RJ_8760.csv` | `wind/RJ_8760.csv` | Rajasthan ~27°N, desert |
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| KA | `solar/KA_8760.csv` | `wind/KA_8760.csv` | Karnataka ~13°N, coastal |
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| GJ | `solar/GJ_8760.csv` | `wind/GJ_8760.csv` | Gujarat ~23°N, Kutch |
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Solar CSVs: columns `hour, irradiance_norm` (0–1).
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Wind CSVs: columns `hour, wind_speed_ms` (m/s at 100 m reference height).
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Load via `catalog.loader.load_solar_profile(location_id)` / `load_wind_profile(location_id)`.
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Results are LRU-cached per process.
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## Adding a new location profile
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1. Create `catalog/profiles/solar/<ID>_8760.csv` and `catalog/profiles/wind/<ID>_8760.csv`.
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2. Add the ID to `catalog/loader._VALID_LOCATIONS`.
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3. Add a test in `tests/unit/test_catalog.py`.
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## CLI
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```bash
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remodel simulate-gen --input scenario.json --output gen.parquet
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```
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`scenario.json` must have a `"solar"` key (SolarConfig fields) and/or a `"wind"` key (WindConfig fields).
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## Parity gate (S1-T10)
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See `tests/integration/test_parity.py`. Currently skipped pending user-supplied fixture values.
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To unlock: fill `tests/fixtures/nagasamudra_inputs.json._expected` and remove the `pytest.skip`.
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