Deploy clean Space (no binary artifacts)

.gitattributes

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+backend/model/revenue/mc_inputs_30y_monthly_merged.csv filter=lfs diff=lfs merge=lfs -text
+frontend/src/data/GER_NUTS3_TSOs.shp filter=lfs diff=lfs merge=lfs -text
+frontend/src/data/tso.json filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,15 @@
+# venv / local artifacts
+.venv/
+venv/
+**/.venv/
+**/venv/
+__pycache__/
+*.pyc
+
+# model binaries
+wind-power-climate-ml/LGBM_Model/model_artifacts/
+*.pkl
+*.joblib
+
+# optional: trash / outputs
+model/revenue/trash/

Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+ENV PIP_NO_CACHE_DIR=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+# Falls du lightgbm / netcdf etc. nutzt, brauchst du evtl. Systemlibs.
+# Minimal (häufig nötig für lightgbm): gcc/g++ und libgomp
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc g++ libgomp1 \
+  && rm -rf /var/lib/apt/lists/*
+
+# Python deps zuerst (besserer Docker cache)
+COPY wind-power-climate-ml/requirements.txt /app/requirements.txt
+RUN pip install -r /app/requirements.txt 
+RUN pip install -U huggingface_hub
+
+# App-Code
+COPY wind-power-climate-ml /app/wind-power-climate-ml
+
+# Entrypoint
+COPY entrypoint.sh /app/entrypoint.sh
+RUN chmod +x /app/entrypoint.sh
+
+EXPOSE 7860
+CMD ["/app/entrypoint.sh"]

README.md

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+---
+title: windproject
+sdk: docker
+app_port: 7860
+---
+
+Wind Energy Forecast API
+

app/main.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import Response
+from pydantic import BaseModel, Field
+from typing import Optional, Any
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import sys
+import json
+import math
+
+
+# --- Make sure Python can import from backend/model ---
+# project/backend/app/main.py -> project/backend
+BACKEND_ROOT = Path(__file__).resolve().parents[1]
+MODEL_DIR = BACKEND_ROOT / "model"
+sys.path.insert(0, str(MODEL_DIR))
+
+# Now we can import your script
+from profit import power_to_profit, FORECAST_MONTHS
+
+app = FastAPI()
+
+@app.get("/favicon.ico")
+def favicon():
+    return Response(status_code=204)
+
+@app.get("/")
+def root():
+    return {"ok": True, "message": "Backend running. Go to /docs for API docs."}
+
+# Allow the Vite dev server to call the API from the browser during development.
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], # allow all origins for testing
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -------------------------
+# Request schema
+# -------------------------
+class CalcRequest(BaseModel):
+    # Settings from the UI
+    n_turbines: int = Field(ge=1)
+    hub_height_m: int = Field(ge=1)
+    turbine_type_id: int = Field(ge=0, le=2)
+    equity_eur: int = Field(ge=0)
+
+    # Revenue module required keys
+    tso_id: int = Field(ge=0, le=3)
+    eeg_on: bool
+    cod_date: str  # "YYYY-MM-DD"
+    manual_eeg_strike: Optional[float] = None
+
+    # For now: constant monthly MWh (easy to wire first)
+    mwh_constant: float = Field(gt=0)
+
+
+def to_jsonable(obj: Any):
+    """Convert pandas/numpy objects into JSON-serializable Python types.
+    Also replaces NaN/Inf with None (JSON-compliant).
+    """
+    # pandas DataFrame
+    if isinstance(obj, pd.DataFrame):
+        df = obj.copy()
+
+        # Convert datetime columns to strings
+        for col in df.columns:
+            if pd.api.types.is_datetime64_any_dtype(df[col]):
+                df[col] = df[col].astype(str)
+
+        # Replace inf/-inf with NaN, then NaN -> None
+        df = df.replace([np.inf, -np.inf], np.nan)
+        df = df.where(pd.notnull(df), None)
+
+        return df.to_dict(orient="records")
+
+    # pandas Series
+    if isinstance(obj, pd.Series):
+        s = obj.copy()
+
+        # If index is datetime, return list of {date, value}
+        if pd.api.types.is_datetime64_any_dtype(s.index):
+            out = []
+            for idx, val in s.items():
+                if pd.isna(val) or val in (np.inf, -np.inf):
+                    v = None
+                else:
+                    v = float(val)
+                out.append({"date": str(idx), "value": v})
+            return out
+
+        # otherwise just list values
+        out = []
+        for x in s.to_list():
+            if pd.isna(x) or x in (np.inf, -np.inf):
+                out.append(None)
+            else:
+                out.append(float(x))
+        return out
+
+    # pandas Timestamp
+    if isinstance(obj, pd.Timestamp):
+        return str(obj)
+
+    # numpy scalars/arrays
+    if isinstance(obj, (np.integer,)):
+        return int(obj)
+
+    if isinstance(obj, (np.floating,)):
+        val = float(obj)
+        if val != val or val in (float("inf"), float("-inf")):
+            return None
+        return val
+
+    if isinstance(obj, np.ndarray):
+        # Convert elements too (in case the array has NaN)
+        return [to_jsonable(x) for x in obj.tolist()]
+
+    # plain Python float NaN/Inf
+    if isinstance(obj, float):
+        if obj != obj or obj in (float("inf"), float("-inf")):
+            return None
+        return obj
+
+    # dict / list / tuple
+    if isinstance(obj, dict):
+        return {k: to_jsonable(v) for k, v in obj.items()}
+
+    if isinstance(obj, (list, tuple)):
+        return [to_jsonable(x) for x in obj]
+
+    return obj
+
+def scrub_nonfinite(obj: Any):
+    """Recursively replace NaN/Inf/-Inf with None in already JSON-like objects."""
+    # dict
+    if isinstance(obj, dict):
+        return {k: scrub_nonfinite(v) for k, v in obj.items()}
+
+    # list/tuple
+    if isinstance(obj, (list, tuple)):
+        return [scrub_nonfinite(v) for v in obj]
+
+    # numpy scalar that may still exist
+    if isinstance(obj, np.generic):
+        return scrub_nonfinite(obj.item())
+
+    # float NaN/Inf
+    if isinstance(obj, float):
+        return None if not math.isfinite(obj) else obj
+
+    return obj
+
+def pick_first_col(df: pd.DataFrame, candidates: list[str]) -> str | None:
+    for c in candidates:
+        if c in df.columns:
+            return c
+    return None
+
+
+
+
+@app.post("/api/calc")
+def calc(req: CalcRequest):
+    try:
+        # Build monthly MWh series for 20 years
+        cod = pd.Timestamp(req.cod_date)
+        cod = pd.Timestamp(year=cod.year, month=cod.month, day=1)
+
+        dates = pd.date_range(cod, periods=FORECAST_MONTHS, freq="MS")
+        mwh_series = pd.Series(float(req.mwh_constant), index=dates)
+
+        result = power_to_profit(
+            mwh_monthly_20y=mwh_series,
+            turbine_type_id=req.turbine_type_id,
+            n_turbines=req.n_turbines,
+            hub_height_m=req.hub_height_m,
+            equity_eur=float(req.equity_eur),
+            revenue_kwargs={
+                "tso_id": int(req.tso_id),
+                "eeg_on": 1 if req.eeg_on else 0,
+                "manual_eeg_strike": req.manual_eeg_strike,
+                "cod_date": req.cod_date,
+            },
+        )
+
+        monthly = result["monthly_revenue_df"].copy()
+        yearly = result["yearly_df"].copy()
+
+        # Ensure project_year exists (it should, from your profit.py logic)
+        if "project_year" not in monthly.columns:
+            raise ValueError("monthly_revenue_df is missing 'project_year'")
+
+        # Choose a reasonable MWh column (depends on your revenue module output)
+        mwh_col = pick_first_col(monthly, ["mwh_gross", "mwh", "mwh_delivered"])
+        if not mwh_col:
+            raise ValueError("monthly_revenue_df has none of: mwh_gross, mwh, mwh_delivered")
+
+        # Weighted average helper (for market price)
+        def wavg(group: pd.DataFrame, value_col: str, weight_col: str):
+            w = pd.to_numeric(group[weight_col], errors="coerce").fillna(0.0)
+            v = pd.to_numeric(group[value_col], errors="coerce")
+            denom = float(w.sum())
+            if denom <= 0:
+                return np.nan
+            return float((v * w).sum() / denom)
+
+        # Aggregate monthly -> yearly drivers
+        agg = (
+            monthly.groupby("project_year", as_index=False)
+            .apply(lambda g: pd.Series({
+                "mwh_gross": float(pd.to_numeric(g[mwh_col], errors="coerce").fillna(0.0).sum()),
+                "market_price_eur_per_mwh": wavg(g, "p_market", mwh_col) if "p_market" in g.columns else np.nan,
+                "cf": float(pd.to_numeric(g["cf"], errors="coerce").mean()) if "cf" in g.columns else np.nan,
+                "cr": float(pd.to_numeric(g["cr"], errors="coerce").mean()) if "cr" in g.columns else np.nan,
+            }))
+        )
+
+        # Join with yearly opex/debt/profit
+        out = yearly.merge(agg, on="project_year", how="left")
+
+        # Build the exact columns you asked for
+        table_df = pd.DataFrame({
+            "year": out["project_year"],
+            "mwh_gross": out["mwh_gross"],
+            "market_price_electricity": out["market_price_eur_per_mwh"],
+            "cf": out["cf"],
+            "cr": out["cr"],
+            "opex": out["annual_opex_eur"],
+            "debt_service": out["debt_service_eur"],
+            "profit": out["profit_after_opex_and_debt_eur"],
+        })
+
+        # Convert NaN/Inf -> None for JSON
+        table_df = table_df.replace([np.inf, -np.inf], np.nan).where(pd.notnull(table_df), None)
+
+        payload = {
+            "ok": True,
+            "npv_eur": float(result["npv_eur"]),
+            "irr": None if (float(result["irr"]) != float(result["irr"])) else float(result["irr"]),
+            "discount_rate_used": float(result["discount_rate_used"]),
+            "table": table_df.to_dict(orient="records"),
+        }
+
+        # Ensure strict JSON (no NaN)
+        json.dumps(payload, allow_nan=False)
+
+        return payload
+
+
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))

app/main2.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import Response
+from pydantic import BaseModel, Field
+from typing import Optional, List
+import pandas as pd
+from pathlib import Path
+import sys
+
+# --- Make sure Python can import from backend/model ---
+# project/backend/app/main.py -> project/backend
+BACKEND_ROOT = Path(__file__).resolve().parents[1]
+MODEL_DIR = BACKEND_ROOT / "model"
+sys.path.insert(0, str(MODEL_DIR))
+
+# Now we can import your script
+from profit import power_to_profit, FORECAST_MONTHS
+
+app = FastAPI()
+
+@app.get("/favicon.ico")
+def favicon():
+    return Response(status_code=204)
+
+@app.get("/")
+def root():
+    return {"ok": True, "message": "Backend running. Go to /docs for API docs."}
+
+
+# Allow the Vite dev server to call the API from the browser during development.
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -------------------------
+# Request/response schemas
+# -------------------------
+class CalcRequest(BaseModel):
+    # Settings from the UI
+    n_turbines: int = Field(ge=1)
+    hub_height_m: int = Field(ge=1)
+    turbine_type_id: int = Field(ge=0, le=2)
+    equity_eur: int = Field(ge=0)
+
+    # Revenue module required keys
+    tso_id: int = Field(ge=1)
+    eeg_on: bool
+    cod_date: str  # "YYYY-MM-DD"
+    manual_eeg_strike: Optional[float] = None
+
+    # For now: constant monthly MWh (easy to wire first)
+    mwh_constant: float = Field(gt=0)
+
+class CalcResponse(BaseModel):
+    ok: bool
+    npv_eur: float
+    irr: float
+    discount_rate_used: float
+    yearly: List[dict]
+
+@app.post("/api/calc", response_model=CalcResponse)
+def calc(req: CalcRequest):
+    try:
+        # Build monthly MWh series for 20 years
+        cod = pd.Timestamp(req.cod_date)
+        cod = pd.Timestamp(year=cod.year, month=cod.month, day=1)
+
+        dates = pd.date_range(cod, periods=FORECAST_MONTHS, freq="MS")
+        mwh_series = pd.Series(float(req.mwh_constant), index=dates)
+
+        result = power_to_profit(
+            mwh_monthly_20y=mwh_series,
+            turbine_type_id=req.turbine_type_id,
+            n_turbines=req.n_turbines,
+            hub_height_m=req.hub_height_m,
+            equity_eur=float(req.equity_eur),
+            revenue_kwargs={
+                "tso_id": int(req.tso_id),
+                "eeg_on": 1 if req.eeg_on else 0,
+                "manual_eeg_strike": req.manual_eeg_strike,
+                "cod_date": req.cod_date,
+            },
+        )
+
+        yearly_df = result["yearly_df"].copy()
+        if "year_start" in yearly_df.columns:
+            yearly_df["year_start"] = yearly_df["year_start"].astype(str)
+
+        irr_val = result["irr"]
+        irr_val = float(irr_val) if irr_val == irr_val else float("nan")  # handle NaN
+
+        return {
+            "ok": True,
+            "npv_eur": float(result["npv_eur"]),
+            "irr": irr_val,
+            "discount_rate_used": float(result["discount_rate_used"]),
+            "yearly": yearly_df.to_dict(orient="records"),
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))

entrypoint.sh

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+#!/bin/sh
+set -eu
+
+DATA_ROOT="/app/inference-data"
+ERA5_SUBDIR="ERA5_Germany"
+ERA5_PATH="$DATA_ROOT/$ERA5_SUBDIR"
+
+MODEL_ROOT="/app/model-artifacts"
+
+# ------------------------------------------------------------
+# 1) Download ERA5 dataset (only if missing/empty)
+#    Dataset: dspunituebingen/era5
+#    Folder:  ERA5_Germany/**
+# ------------------------------------------------------------
+if [ ! -d "$ERA5_PATH" ] || [ -z "$(ls -A "$ERA5_PATH" 2>/dev/null)" ]; then
+  echo "[BOOT] Downloading dataset dspunituebingen/era5 -> $DATA_ROOT (patterns: $ERA5_SUBDIR/**)"
+  python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+
+token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+snapshot_download(
+    repo_id="dspunituebingen/era5",
+    repo_type="dataset",
+    local_dir="/app/inference-data",
+    allow_patterns=["ERA5_Germany/**"],
+    token=token,
+)
+
+print("[BOOT] ERA5 data ready at /app/inference-data/ERA5_Germany")
+PY
+fi
+
+# ERA5 path used by your FastAPI code via env var
+export ERA5_DIR="$ERA5_PATH"
+
+# ------------------------------------------------------------
+# 2) Download model repo (only if missing/empty)
+#    Model: dspunituebingen/windproject-model
+#    Files are in the repo ROOT (final_model.pkl, feature_imputer.pkl, *.json)
+# ------------------------------------------------------------
+if [ ! -d "$MODEL_ROOT" ] || [ -z "$(ls -A "$MODEL_ROOT" 2>/dev/null)" ]; then
+  echo "[BOOT] Downloading model dspunituebingen/windproject-model -> $MODEL_ROOT"
+  python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+
+token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+snapshot_download(
+    repo_id="dspunituebingen/windproject-model",
+    repo_type="model",
+    local_dir="/app/model-artifacts",
+    allow_patterns=["*.pkl", "*.json", ".gitattributes", "README.md"],
+    token=token,
+)
+
+print("[BOOT] Model artifacts ready at /app/model-artifacts")
+PY
+fi
+
+# Model directory used by your FastAPI code via env var
+# (Your model files are directly in the repo root, so MODEL_DIR == MODEL_ROOT)
+export MODEL_DIR="$MODEL_ROOT"
+
+# ------------------------------------------------------------
+# 3) Turbine curves path (try dataset first, then repo fallbacks)
+# ------------------------------------------------------------
+if [ -z "${TURBINE_CURVES:-}" ] && [ -f "$DATA_ROOT/turbine_power_curves.json" ]; then
+  export TURBINE_CURVES="$DATA_ROOT/turbine_power_curves.json"
+fi
+
+if [ -z "${TURBINE_CURVES:-}" ]; then
+  if [ -f "/app/wind-power-climate-ml/data/turbine_power_curves.json" ]; then
+    export TURBINE_CURVES="/app/wind-power-climate-ml/data/turbine_power_curves.json"
+  elif [ -f "/app/wind-power-climate-ml/Data/turbine_power_curves.json" ]; then
+    export TURBINE_CURVES="/app/wind-power-climate-ml/Data/turbine_power_curves.json"
+  fi
+fi
+
+# ------------------------------------------------------------
+# 4) Debug / sanity checks
+# ------------------------------------------------------------
+echo "[CONFIG] ERA5_DIR=$ERA5_DIR"
+echo "[CONFIG] MODEL_DIR=$MODEL_DIR"
+echo "[CONFIG] TURBINE_CURVES=${TURBINE_CURVES:-<default in code>}"
+
+echo "[CHECK] ERA5_DIR listing:"
+ls -lah "$ERA5_DIR" | head -n 50 || true
+
+echo "[CHECK] MODEL_DIR listing:"
+ls -lah "$MODEL_DIR" | head -n 50 || true
+
+if [ -n "${TURBINE_CURVES:-}" ]; then
+  echo "[CHECK] TURBINE_CURVES exists:"
+  ls -lah "$TURBINE_CURVES" || true
+fi
+
+# ------------------------------------------------------------
+# 5) Start FastAPI (Uvicorn)
+# ------------------------------------------------------------
+cd /app/wind-power-climate-ml
+exec python -m uvicorn website.api:app --app-dir src --host 0.0.0.0 --port 7860 --log-level info

model/costs/capex/capex.py

@@ -0,0 +1,84 @@
+import math
+from dataclasses import dataclass
+
+# -----------------------
+# Turbine archetypes
+# -----------------------
+@dataclass(frozen=True)
+class TurbineType:
+    name: str
+    p_mw: float
+    rotor_diameter_m: float
+
+TURBINE_TYPES = {
+    "LOW_WIND":  TurbineType("LOW_WIND", 6.0, 170.0),
+    "BALANCED":  TurbineType("BALANCED", 5.6, 160.0),
+    "HIGH_WIND": TurbineType("HIGH_WIND", 4.2, 130.0),
+}
+
+# -----------------------
+# BoP costs (2025–2028)
+# -----------------------
+BOP_EUR_PER_KW = 551   # foundations, grid, roads, planning, etc.
+
+# -----------------------
+# Engineering functions
+# -----------------------
+def sfl_w_per_m2(p_mw, rotor_diameter_m):
+    area = math.pi * (rotor_diameter_m / 2) ** 2
+    return (p_mw * 1_000_000) / area
+
+def hik_eur_per_kw(p_mw, sfl, hub_height_m):
+    """
+    HIK regression from German industry data:
+    HIK = 1476.19 - 65.62*P - 1.29*SFL + 3.50*NH
+    """
+    return 1476.19 - 65.62 * p_mw - 1.29 * sfl + 3.50 * hub_height_m
+
+# -----------------------
+# Wind park CAPEX
+# -----------------------
+def windpark_capex(n_turbines, turbine_type_key, hub_height_m):
+    t = TURBINE_TYPES[turbine_type_key]
+
+    park_mw = n_turbines * t.p_mw
+    sfl = sfl_w_per_m2(t.p_mw, t.rotor_diameter_m)
+
+    turbine_capex_kw = hik_eur_per_kw(t.p_mw, sfl, hub_height_m)
+    total_capex_kw = turbine_capex_kw + BOP_EUR_PER_KW
+
+    total_capex = park_mw * 1000 * total_capex_kw
+
+    return {
+        "park_mw": park_mw,
+        "sfl": sfl,
+        "turbine_capex_eur_per_kw": turbine_capex_kw,
+        "bop_eur_per_kw": BOP_EUR_PER_KW,
+        "total_capex_eur_per_kw": total_capex_kw,
+        "total_capex_eur": total_capex
+    }
+
+# -----------------------
+# Pretty printer
+# -----------------------
+def print_windpark_result(n_turbines, turbine_type, hub_height_m):
+    res = windpark_capex(n_turbines, turbine_type, hub_height_m)
+    t = TURBINE_TYPES[turbine_type]
+
+    print(
+        f"{turbine_type} ({n_turbines} × {t.p_mw} MW) | "
+        f"NH={hub_height_m} m | "
+        f"SFL={res['sfl']:.0f} W/m² | "
+        f"Turbine={res['turbine_capex_eur_per_kw']:.0f} €/kW | "
+        f"BoP={res['bop_eur_per_kw']:.0f} €/kW | "
+        f"Total={res['total_capex_eur_per_kw']:.0f} €/kW | "
+        f"CAPEX={res['total_capex_eur']/1e6:.1f} M€"
+    )
+
+# -----------------------
+# Example
+# -----------------------
+if __name__ == "__main__":
+    print_windpark_result(12, "BALANCED", 160)
+    print_windpark_result(15, "HIGH_WIND", 120)
+    print_windpark_result(10, "LOW_WIND", 180)

model/costs/capex/capex_mod.py

@@ -0,0 +1,169 @@
+# capex.py
+#
+# Central callable CAPEX module for power_to_profit.py
+# - Accepts turbine type as an INDEX (0,1,2) coming from profit.py
+# - Accepts number of turbines and hub height
+# - Returns a dict including keys used by power_to_profit.py:
+#     - park_mw
+#     - total_capex_eur
+#
+# Mapping (you can change ordering if you prefer):
+#   0 -> LOW_WIND
+#   1 -> BALANCED
+#   2 -> HIGH_WIND
+
+import math
+from dataclasses import dataclass
+from typing import Dict, Any
+
+
+# -----------------------
+# Turbine archetypes
+# -----------------------
+@dataclass(frozen=True)
+class TurbineType:
+    name: str
+    p_mw: float
+    rotor_diameter_m: float
+
+
+TURBINE_TYPES_BY_KEY: Dict[str, TurbineType] = {
+    "LOW_WIND":  TurbineType("LOW_WIND", 6.0, 170.0),
+    "BALANCED":  TurbineType("BALANCED", 5.6, 160.0),
+    "HIGH_WIND": TurbineType("HIGH_WIND", 4.2, 130.0),
+}
+
+# Index mapping expected by profit.py
+TURBINE_TYPE_INDEX_MAP: Dict[int, str] = {
+    0: "LOW_WIND",
+    1: "BALANCED",
+    2: "HIGH_WIND",
+}
+
+# -----------------------
+# BoP costs (2025–2028)
+# -----------------------
+BOP_EUR_PER_KW = 551.0  # foundations, grid, roads, planning, etc.
+
+
+# -----------------------
+# Engineering functions
+# -----------------------
+def sfl_w_per_m2(p_mw: float, rotor_diameter_m: float) -> float:
+    """Specific rated power density (W/m²) based on rotor swept area."""
+    area = math.pi * (rotor_diameter_m / 2.0) ** 2
+    return (p_mw * 1_000_000.0) / area
+
+
+def hik_eur_per_kw(p_mw: float, sfl: float, hub_height_m: float) -> float:
+    """
+    HIK regression from German industry data:
+    HIK = 1476.19 - 65.62*P - 1.29*SFL + 3.50*NH
+    """
+    return 1476.19 - 65.62 * p_mw - 1.29 * sfl + 3.50 * hub_height_m
+
+
+# -----------------------
+# Public API used by power_to_profit.py
+# -----------------------
+def windpark_capex(
+    *,
+    n_turbines: int,
+    turbine_type_id: int,
+    hub_height_m: float,
+) -> Dict[str, Any]:
+    """
+    Compute wind park CAPEX based on turbine archetype + hub height.
+
+    Parameters
+    ----------
+    n_turbines : int
+        Number of turbines in the park.
+    turbine_type_id : int
+        Turbine type index coming from profit.py:
+          0 -> LOW_WIND
+          1 -> BALANCED
+          2 -> HIGH_WIND
+    hub_height_m : float
+        Hub height in meters.
+
+    Returns
+    -------
+    dict including:
+      - park_mw
+      - total_capex_eur
+      plus breakdown fields.
+    """
+    if not isinstance(n_turbines, int) or n_turbines <= 0:
+        raise ValueError("n_turbines must be a positive integer.")
+
+    try:
+        turbine_type_id = int(turbine_type_id)
+    except Exception as e:
+        raise ValueError("turbine_type_id must be an integer (0,1,2).") from e
+
+    if turbine_type_id not in TURBINE_TYPE_INDEX_MAP:
+        raise ValueError(
+            f"Unknown turbine_type_id={turbine_type_id}. "
+            f"Valid: {sorted(TURBINE_TYPE_INDEX_MAP.keys())} "
+            f"(0=LOW_WIND, 1=BALANCED, 2=HIGH_WIND)"
+        )
+
+    hub_height_m = float(hub_height_m)
+    if hub_height_m <= 0:
+        raise ValueError("hub_height_m must be > 0.")
+
+    turbine_key = TURBINE_TYPE_INDEX_MAP[turbine_type_id]
+    t = TURBINE_TYPES_BY_KEY[turbine_key]
+
+    park_mw = float(n_turbines) * float(t.p_mw)
+    sfl = sfl_w_per_m2(t.p_mw, t.rotor_diameter_m)
+
+    turbine_capex_eur_per_kw = hik_eur_per_kw(t.p_mw, sfl, hub_height_m)
+    total_capex_eur_per_kw = float(turbine_capex_eur_per_kw) + float(BOP_EUR_PER_KW)
+
+    total_capex_eur = park_mw * 1000.0 * total_capex_eur_per_kw
+
+    return {
+        "turbine_type_id": int(turbine_type_id),
+        "turbine_type": t.name,
+        "n_turbines": int(n_turbines),
+        "hub_height_m": float(hub_height_m),
+        "park_mw": float(park_mw),
+        "rotor_diameter_m": float(t.rotor_diameter_m),
+        "sfl": float(sfl),
+        "turbine_capex_eur_per_kw": float(turbine_capex_eur_per_kw),
+        "bop_eur_per_kw": float(BOP_EUR_PER_KW),
+        "total_capex_eur_per_kw": float(total_capex_eur_per_kw),
+        "total_capex_eur": float(total_capex_eur),
+    }
+
+
+# -----------------------
+# Convenience helper (optional)
+# -----------------------
+def print_windpark_result(n_turbines: int, turbine_type_id: int, hub_height_m: float) -> None:
+    res = windpark_capex(
+        n_turbines=n_turbines,
+        turbine_type_id=turbine_type_id,
+        hub_height_m=hub_height_m,
+    )
+    print(
+        f"Type={res['turbine_type']} (id={res['turbine_type_id']}) | "
+        f"{res['n_turbines']} × {TURBINE_TYPES_BY_KEY[res['turbine_type']].p_mw} MW | "
+        f"NH={res['hub_height_m']:.0f} m | "
+        f"SFL={res['sfl']:.0f} W/m² | "
+        f"Turbine={res['turbine_capex_eur_per_kw']:.0f} €/kW | "
+        f"BoP={res['bop_eur_per_kw']:.0f} €/kW | "
+        f"Total={res['total_capex_eur_per_kw']:.0f} €/kW | "
+        f"CAPEX={res['total_capex_eur']/1e6:.1f} M€"
+    )
+
+
+# -----------------------
+# Example
+# -----------------------
+if __name__ == "__main__":
+    print_windpark_result(12, 1, 160)  # BALANCED
+    print_windpark_result(15, 2, 120)  # HIGH_WIND
+    print_windpark_result(10, 0, 180)  # LOW_WIND

model/costs/capex/other_costs.csv

@@ -0,0 +1,7 @@
+category,eur_per_kw,share
+Planning_and_development,163,0.30
+Grid_connection,133,0.24
+Infrastructure_and_foundations,119,0.22
+Compensation,35,0.06
+Other,101,0.18
+Total,551,1.00

model/costs/finance/finance.py

@@ -0,0 +1,67 @@
+import numpy as np
+
+def financing_model(
+    capex_eur,
+    equity_share=0.15,
+    debt_rate=0.045,
+    equity_return=0.08,
+    debt_tenor_years=20
+):
+    """
+    Simple project finance model for a wind park.
+    """
+
+    debt_share = 1 - equity_share
+
+    equity = capex_eur * equity_share
+    debt = capex_eur * debt_share
+
+    # annuity loan formula
+    r = debt_rate
+    n = debt_tenor_years
+    annuity_factor = r * (1 + r)**n / ((1 + r)**n - 1)
+    annual_debt_service = debt * annuity_factor
+
+    # total debt repaid
+    total_debt_repayment = annual_debt_service * n
+    total_interest = total_debt_repayment - debt
+
+    # Weighted Average Cost of Capital
+    wacc = equity_share * equity_return + debt_share * debt_rate
+
+    return {
+        "capex_eur": capex_eur,
+        "equity_share": equity_share,
+        "debt_share": debt_share,
+        "equity_eur": equity,
+        "debt_eur": debt,
+        "annual_debt_service_eur": annual_debt_service,
+        "total_interest_paid_eur": total_interest,
+        "wacc": wacc
+    }
+
+# -------------------------------
+# Realistic German wind park example
+# -------------------------------
+if __name__ == "__main__":
+    capex = 80_000_000          # 80 million € project
+    equity_share = 0.15        # 15% equity (aggressive but bankable)
+    debt_rate = 0.045          # 4.5% KfW-270 loan
+    equity_return = 0.085      # 8.5% equity target
+    tenor = 18                 # 18-year loan
+
+    result = financing_model(
+        capex_eur=capex,
+        equity_share=equity_share,
+        debt_rate=debt_rate,
+        equity_return=equity_return,
+        debt_tenor_years=tenor
+    )
+
+    print("---- German Onshore Wind Financing ----")
+    print(f"CAPEX:            {result['capex_eur']/1e6:.1f} M€")
+    print(f"Equity (15%):     {result['equity_eur']/1e6:.1f} M€")
+    print(f"Debt (85%):       {result['debt_eur']/1e6:.1f} M€")
+    print(f"Annual debt svc: {result['annual_debt_service_eur']/1e6:.2f} M€ / year")
+    print(f"Total interest:  {result['total_interest_paid_eur']/1e6:.1f} M€ over {tenor} years")
+    print(f"WACC:            {result['wacc']*100:.2f} %")

model/costs/finance/finance_mod.py

@@ -0,0 +1,192 @@
+# finance.py
+#
+# Central callable finance module for power_to_profit.py
+# - Inputs come from profit.py:
+#     - capex_eur (from capex_res["total_capex_eur"])
+#     - equity_eur (absolute € amount; we compute equity_share)
+#     - debt_rate, equity_return, debt_tenor_years
+#     - forecast_months (project horizon; default 240)
+# - Outputs include:
+#     - equity_share_derived
+#     - annual_debt_service_eur
+#     - debt_service_yearly_df (project_year schedule aligned to horizon)
+#     - wacc
+#
+# Notes:
+# - We keep the debt service as an ANNUITY yearly payment (like your current model).
+# - Horizon alignment: we build project_year 1..ceil(forecast_months/12) and set payments
+#   to 0 after debt_tenor_years.
+
+import math
+import pandas as pd
+
+
+def financing_model(
+    *,
+    capex_eur: float,
+    equity_eur: float | None = None,
+    # If equity_eur is None, you can still pass equity_share directly
+    equity_share: float | None = None,
+    debt_rate: float = 0.045,
+    equity_return: float = 0.08,
+    debt_tenor_years: int = 20,
+    forecast_months: int = 240,
+) -> dict:
+    """
+    Simple project finance model for a wind park.
+
+    Parameters
+    ----------
+    capex_eur : float
+        Total project CAPEX in €.
+    equity_eur : float | None
+        Absolute equity amount in €. If provided, equity_share is derived as equity_eur/capex_eur.
+    equity_share : float | None
+        Optional direct equity share (0..1). Used only if equity_eur is None.
+    debt_rate : float
+        Annual debt interest rate (e.g., 0.045).
+    equity_return : float
+        Annual equity target return (used for WACC).
+    debt_tenor_years : int
+        Loan tenor in years (annuity payments).
+    forecast_months : int
+        Project horizon in months (default 240 = 20y). Used to create aligned debt schedule.
+
+    Returns
+    -------
+    dict with keys used by power_to_profit.py plus schedule:
+      - annual_debt_service_eur
+      - wacc
+      - equity_share_derived
+      - debt_service_yearly_df (project_year, debt_service_eur)
+      - plus breakdown fields (equity_eur, debt_eur, total_interest_paid_eur, etc.)
+    """
+
+    capex_eur = float(capex_eur)
+    if capex_eur <= 0:
+        raise ValueError("capex_eur must be > 0")
+
+    forecast_months = int(forecast_months)
+    if forecast_months <= 0:
+        raise ValueError("forecast_months must be > 0")
+
+    debt_rate = float(debt_rate)
+    if debt_rate < 0:
+        raise ValueError("debt_rate must be >= 0")
+
+    equity_return = float(equity_return)
+
+    debt_tenor_years = int(debt_tenor_years)
+    if debt_tenor_years <= 0:
+        raise ValueError("debt_tenor_years must be > 0")
+
+    # -----------------------
+    # Derive equity share
+    # -----------------------
+    if equity_eur is not None:
+        equity_eur = float(equity_eur)
+        if equity_eur < 0:
+            raise ValueError("equity_eur must be >= 0")
+        equity_share_derived = equity_eur / capex_eur
+    else:
+        if equity_share is None:
+            # default: 15% equity if nothing provided
+            equity_share_derived = 0.15
+        else:
+            equity_share_derived = float(equity_share)
+
+    # clamp to [0, 1)
+    equity_share_derived = max(0.0, min(equity_share_derived, 0.999999))
+
+    debt_share = 1.0 - equity_share_derived
+
+    equity_eur_final = capex_eur * equity_share_derived
+    debt_eur = capex_eur * debt_share
+
+    # -----------------------
+    # Annuity debt service (annual)
+    # -----------------------
+    r = debt_rate
+    n = debt_tenor_years
+
+    if debt_eur <= 0:
+        annual_debt_service_eur = 0.0
+        total_interest_paid_eur = 0.0
+    else:
+        if r == 0.0:
+            annual_debt_service_eur = debt_eur / n
+        else:
+            annuity_factor = r * (1.0 + r) ** n / ((1.0 + r) ** n - 1.0)
+            annual_debt_service_eur = debt_eur * annuity_factor
+
+        total_debt_repayment = annual_debt_service_eur * n
+        total_interest_paid_eur = total_debt_repayment - debt_eur
+
+    # -----------------------
+    # WACC (simple weighted blend)
+    # -----------------------
+    wacc = equity_share_derived * equity_return + debt_share * debt_rate
+
+    # -----------------------
+    # Build aligned debt schedule by project year
+    # -----------------------
+    horizon_years = int(math.ceil(forecast_months / 12.0))
+    years = list(range(1, horizon_years + 1))
+
+    debt_service = [
+        float(annual_debt_service_eur) if y <= debt_tenor_years else 0.0
+        for y in years
+    ]
+
+    debt_service_yearly_df = pd.DataFrame({
+        "project_year": years,
+        "debt_service_eur": debt_service,
+    })
+
+    return {
+        "capex_eur": float(capex_eur),
+        "equity_share_derived": float(equity_share_derived),
+        "debt_share": float(debt_share),
+        "equity_eur": float(equity_eur_final),
+        "debt_eur": float(debt_eur),
+        "debt_rate": float(debt_rate),
+        "equity_return": float(equity_return),
+        "debt_tenor_years": int(debt_tenor_years),
+        "annual_debt_service_eur": float(annual_debt_service_eur),
+        "total_interest_paid_eur": float(total_interest_paid_eur),
+        "wacc": float(wacc),
+        "forecast_months": int(forecast_months),
+        "horizon_years": int(horizon_years),
+        "debt_service_yearly_df": debt_service_yearly_df,
+    }
+
+
+# -------------------------------
+# Standalone test / example
+# -------------------------------
+if __name__ == "__main__":
+    capex = 80_000_000
+    equity_eur = 12_000_000
+    debt_rate = 0.045
+    equity_return = 0.085
+    tenor = 18
+    forecast_months = 240
+
+    result = financing_model(
+        capex_eur=capex,
+        equity_eur=equity_eur,
+        debt_rate=debt_rate,
+        equity_return=equity_return,
+        debt_tenor_years=tenor,
+        forecast_months=forecast_months,
+    )
+
+    print("---- German Onshore Wind Financing ----")
+    print(f"CAPEX:               {result['capex_eur']/1e6:.1f} M€")
+    print(f"Equity (input):      {equity_eur/1e6:.1f} M€")
+    print(f"Equity share:        {result['equity_share_derived']*100:.1f} %")
+    print(f"Debt:                {result['debt_eur']/1e6:.1f} M€")
+    print(f"Annual debt service: {result['annual_debt_service_eur']/1e6:.2f} M€ / year")
+    print(f"Total interest:      {result['total_interest_paid_eur']/1e6:.1f} M€ over {tenor} years")
+    print(f"WACC:                {result['wacc']*100:.2f} %")
+    print(result["debt_service_yearly_df"].head())

model/costs/opex/opex.csv

@@ -0,0 +1,3 @@
+decade,maintenance,lease,operations,insurance,marketing,decommissioning,other,total_eur_per_kw_year
+1,15,19,5,4,1,3,6,53
+2,14,20,5,4,1,3,6,55

model/costs/opex/opex.py

@@ -0,0 +1,35 @@
+import pandas as pd
+
+OPEX_DECADE_1 = 53   # €/kW/year (years 1–10)
+OPEX_DECADE_2 = 55   # €/kW/year (years 11–20)
+
+def windpark_opex_timeseries(park_mw, years=20):
+    """
+    Returns a DataFrame with yearly O&M costs and total lifetime OPEX.
+    """
+    park_kw = park_mw * 1000
+    data = []
+
+    for year in range(1, years + 1):
+        if year <= 10:
+            opex_per_kw = OPEX_DECADE_1
+        else:
+            opex_per_kw = OPEX_DECADE_2
+
+        annual_cost = park_kw * opex_per_kw
+
+        data.append({
+            "year": year,
+            "opex_eur_per_kw": opex_per_kw,
+            "annual_opex_eur": annual_cost
+        })
+
+    df = pd.DataFrame(data)
+    total = df["annual_opex_eur"].sum()
+
+    return df, total
+
+df, total = windpark_opex_timeseries(park_mw=50)
+
+print(df)
+print("Total OPEX over 20 years:", total/1e6, "million €")

model/costs/opex/opex_mod.py

@@ -0,0 +1,86 @@
+# opex.py
+#
+# Central callable OPEX module for power_to_profit.py
+# - Inputs come from profit.py:
+#     - park_mw (already computed from capex_res["park_mw"])
+#     - forecast_months (typically 240 for 20 years)
+# - Produces monthly + project-year OPEX aligned to the same 12-month blocks as revenue.
+
+import pandas as pd
+
+# €/kW/year (real terms)
+OPEX_DECADE_1 = 53.0   # years 1–10
+OPEX_DECADE_2 = 55.0   # years 11–20
+
+
+def windpark_opex_timeseries(
+    *,
+    park_mw: float,
+    forecast_months: int = 240,
+) -> tuple[pd.DataFrame, float]:
+    """
+    Compute OPEX time series aligned to project months/years.
+
+    Parameters
+    ----------
+    park_mw : float
+        Park capacity in MW.
+    forecast_months : int
+        Number of months in horizon (default 240 = 20y).
+
+    Returns
+    -------
+    yearly_df : DataFrame with columns:
+        - project_year (1..ceil(forecast_months/12))
+        - annual_opex_eur
+        - opex_eur_per_kw_year  (rate applied for that project year)
+    total_opex_eur : float
+        Total OPEX over the horizon (sum of monthly, equivalently yearly).
+    """
+    park_mw = float(park_mw)
+    if park_mw <= 0:
+        raise ValueError("park_mw must be > 0")
+
+    forecast_months = int(forecast_months)
+    if forecast_months <= 0:
+        raise ValueError("forecast_months must be > 0")
+
+    park_kw = park_mw * 1000.0
+
+    # Build monthly series (project month 1..N)
+    m = pd.DataFrame({"project_month": range(1, forecast_months + 1)})
+    m["project_year"] = ((m["project_month"] - 1) // 12) + 1  # 1..20 (for 240)
+
+    # Choose rate per project year (decade 1 vs decade 2)
+    m["opex_eur_per_kw_year"] = m["project_year"].apply(
+        lambda y: OPEX_DECADE_1 if y <= 10 else OPEX_DECADE_2
+    ).astype(float)
+
+    # Convert annual €/kW/year into monthly €:
+    # monthly_cost = park_kw * (€/kW/year) / 12
+    m["monthly_opex_eur"] = park_kw * m["opex_eur_per_kw_year"] / 12.0
+
+    total_opex_eur = float(m["monthly_opex_eur"].sum())
+
+    # Aggregate to project-year (sum of months in that year; last year might be partial if forecast_months not multiple of 12)
+    yearly_df = (
+        m.groupby("project_year", as_index=False)
+         .agg(
+            annual_opex_eur=("monthly_opex_eur", "sum"),
+            opex_eur_per_kw_year=("opex_eur_per_kw_year", "first"),
+            months_in_year=("monthly_opex_eur", "size"),
+         )
+         .sort_values("project_year")
+         .reset_index(drop=True)
+    )
+
+    return yearly_df, total_opex_eur
+
+
+# -----------------------
+# Optional standalone test
+# -----------------------
+if __name__ == "__main__":
+    yearly, total = windpark_opex_timeseries(park_mw=50, forecast_months=240)
+    print(yearly.head(12))
+    print("Total OPEX:", total / 1e6, "million €")

model/costs/planning/merge.py

@@ -0,0 +1,38 @@
+import pandas as pd
+
+# Load the three Bundesland tables
+planning = pd.read_csv("planning_phase_bundesland.csv")
+permitting = pd.read_csv("permitting_phase_bundesland.csv")
+total = pd.read_csv("total_bundesland.csv")
+
+# Remove rows without projects or without mean
+planning = planning.dropna(subset=["mean_eur_per_kw", "projects"])
+permitting = permitting.dropna(subset=["mean_eur_per_kw", "projects"])
+total = total.dropna(subset=["mean_eur_per_kw", "projects"])
+
+def weighted_mean(df):
+    df = df.dropna(subset=["mean_eur_per_kw", "projects"])
+    return (df["mean_eur_per_kw"] * df["projects"]).sum() / df["projects"].sum()
+
+# Compute national values
+national_planning = weighted_mean(planning)
+national_permitting = weighted_mean(permitting)
+national_total = weighted_mean(total)
+
+# Derive precheck
+national_precheck = national_total - national_planning - national_permitting
+
+# Create output table
+national_costs = pd.DataFrame({
+    "phase": ["Vorpruefung", "Planung", "Genehmigung", "Total_bis_Genehmigung"],
+    "mean_eur_per_kw": [
+        national_precheck,
+        national_planning,
+        national_permitting,
+        national_total
+    ]
+})
+
+national_costs.to_csv("national_wind_project_development_costs.csv", index=False)
+
+print(national_costs)

model/costs/planning/national_project_development_costs.csv

@@ -0,0 +1,4 @@
+phase,weighted_mean_eur_per_kw,projects_used
+Planning,29.03921568627451,204
+Permitting,29.986486486486488,148
+Total_to_Permit,70.06849315068493,146

model/costs/planning/national_wind_project_development_costs.csv

@@ -0,0 +1,5 @@
+phase,mean_eur_per_kw
+Vorpruefung,11.042790977923932
+Planung,29.03921568627451
+Genehmigung,29.986486486486488
+Total_bis_Genehmigung,70.06849315068493

model/costs/planning/permitting_phase_bundesland.csv

@@ -0,0 +1,16 @@
+bundesland,projects,min_eur_per_kw,max_eur_per_kw,mean_eur_per_kw
+Baden-Wuerttemberg,0,,,
+Bayern,13,15,117,47
+Brandenburg,11,21,55,38
+Bremen,0,,,
+Hessen,4,16,34,25
+Mecklenburg-Vorpommern,2,7,52,30
+Niedersachsen,17,12,60,24
+Nordrhein-Westfalen,6,6,10,8
+Rheinland-Pfalz,16,9,60,23
+Saarland,1,62,62,62
+Sachsen,3,14,53,30
+Sachsen-Anhalt,1,53,53,53
+Schleswig-Holstein,0,,,
+Thueringen,0,,,
+Gesamt,74,6,117,30

model/costs/planning/planning_phase_bundesland.csv

@@ -0,0 +1,16 @@
+bundesland,projects,min_eur_per_kw,max_eur_per_kw,mean_eur_per_kw
+Baden-Wuerttemberg,1,100,100,100
+Bayern,14,13,124,55
+Brandenburg,12,15,72,37
+Bremen,0,,,
+Hessen,12,5,69,21
+Mecklenburg-Vorpommern,2,18,48,33
+Niedersachsen,18,9,69,29
+Nordrhein-Westfalen,6,7,24,17
+Rheinland-Pfalz,17,5,40,15
+Saarland,1,40,40,40
+Sachsen,8,4,41,20
+Sachsen-Anhalt,5,6,69,23
+Schleswig-Holstein,1,5,5,5
+Thueringen,5,5,64,27
+Gesamt,102,4,124,29

model/costs/planning/total_bundesland.csv

@@ -0,0 +1,16 @@
+bundesland,projects,min_eur_per_kw,max_eur_per_kw,mean_eur_per_kw
+Baden-Wuerttemberg,0,,,
+Bayern,13,50,234,123
+Brandenburg,11,44,134,85
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+Hessen,4,38,82,56
+Mecklenburg-Vorpommern,2,36,104,70
+Niedersachsen,17,23,140,59
+Nordrhein-Westfalen,5,17,35,28
+Rheinland-Pfalz,16,17,120,46
+Saarland,1,134,134,134
+Sachsen,2,28,44,36
+Sachsen-Anhalt,1,67,67,67
+Schleswig-Holstein,0,,,
+Thueringen,0,,,
+Gesamt,74,17,234,70

model/park_gross_mwh_monthly_mock_2015_2046.csv

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model/profit.py

@@ -0,0 +1,344 @@
+# power_to_profit.py
+
+import numpy as np
+import pandas as pd
+import numpy_financial as npf
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+
+# Add module folders to Python import path
+sys.path.insert(0, str(ROOT / "revenue"))
+sys.path.insert(0, str(ROOT / "costs" / "capex"))
+sys.path.insert(0, str(ROOT / "costs" / "opex"))
+sys.path.insert(0, str(ROOT / "costs" / "finance"))
+
+import revenue_mod as revenue_mod
+import capex_mod as capex_mod
+import opex_mod as opex_mod
+import finance_mod as finance_mod
+
+
+FORECAST_MONTHS = 240  # 20 years
+
+
+def _compute_npv(cashflows, discount_rate):
+    return sum(cf / ((1 + discount_rate) ** t) for t, cf in enumerate(cashflows))
+
+
+
+def _compute_irr(cashflows, guess=0.08):
+    if all(cf >= 0 for cf in cashflows) or all(cf <= 0 for cf in cashflows):
+        return np.nan
+
+    r = guess
+    for _ in range(200):
+        npv = 0.0
+        d_npv = 0.0
+        for t, cf in enumerate(cashflows):
+            denom = (1 + r) ** t
+            npv += cf / denom
+            if t > 0:
+                d_npv += -t * cf / ((1 + r) ** (t + 1))
+
+        if abs(npv) < 1e-6:
+            return r
+
+        if abs(d_npv) < 1e-12:
+            break
+
+        r_new = r - (npv / d_npv)
+        # If Newton wants to run outside sane bounds, treat as failure
+        if r_new <= -0.99 or r_new >= 5.0:
+            return np.nan
+
+
+        if abs(r_new - r) < 1e-9:
+            return r_new
+
+        r = r_new
+
+    return np.nan
+
+
+def _month_index(dt: pd.Series) -> pd.Series:
+    """Integer month index for easy month-diff calculations: year*12 + (month-1)."""
+    return dt.dt.year * 12 + (dt.dt.month - 1)
+
+
+def power_to_profit(
+    mwh_monthly_20y,
+    turbine_type_id=1,   # 0=LOW_WIND, 1=BALANCED, 2=HIGH_WIND
+    n_turbines=12,
+    hub_height_m=160,
+    equity_eur=None,     # <-- absolute € amount (not share)
+    debt_rate=0.045,
+    equity_return=0.085,
+    debt_tenor_years=20,
+    discount_rate=None,
+    revenue_kwargs=None,
+):
+    """
+    Orchestrates: revenue + capex + opex + finance => yearly profit, NPV, IRR
+
+    IMPORTANT:
+    - Yearly aggregation is PROJECT YEARS (12-month blocks from COD),
+      not calendar years.
+    """
+
+    revenue_kwargs = revenue_kwargs or {}
+
+    # -----------------------
+    # 1) Prepare monthly MWh input -> DataFrame(date, mwh)
+    # -----------------------
+    if isinstance(mwh_monthly_20y, pd.Series):
+        mwh_df = mwh_monthly_20y.rename("mwh").to_frame()
+        mwh_df = mwh_df.reset_index().rename(columns={"index": "date"})
+    elif isinstance(mwh_monthly_20y, pd.DataFrame):
+        mwh_df = mwh_monthly_20y.copy()
+        if "date" not in mwh_df.columns:
+            raise ValueError("mwh_monthly_20y DataFrame must contain a 'date' column.")
+        if "mwh" not in mwh_df.columns:
+            for alt in ["mwh_gross", "MWh", "MWh_gross", "mwh_gross_park", "mwh_park"]:
+                if alt in mwh_df.columns:
+                    mwh_df = mwh_df.rename(columns={alt: "mwh"})
+                    break
+        if "mwh" not in mwh_df.columns:
+            raise ValueError("mwh_monthly_20y must contain an 'mwh' column (or mwh_gross).")
+    else:
+        raise TypeError("mwh_monthly_20y must be a pandas Series or DataFrame.")
+
+    mwh_df["date"] = pd.to_datetime(mwh_df["date"], errors="coerce")
+    mwh_df["mwh"] = pd.to_numeric(mwh_df["mwh"], errors="coerce")
+    mwh_df = mwh_df.dropna(subset=["date", "mwh"]).sort_values("date").reset_index(drop=True)
+
+    # -----------------------
+    # 2) Revenue module (NEW API)
+    # -----------------------
+    required = ["tso_id", "eeg_on", "cod_date"]
+    missing = [k for k in required if k not in revenue_kwargs]
+    if missing:
+        raise ValueError(
+            f"revenue_kwargs missing required keys: {missing}. "
+            f"Expected at least: {required} (manual_eeg_strike optional)."
+        )
+
+    tso_id = int(revenue_kwargs["tso_id"])
+    eeg_on = int(revenue_kwargs.get("eeg_on", 1))
+    manual_eeg_strike = revenue_kwargs.get("manual_eeg_strike", None)
+    cod_date = revenue_kwargs["cod_date"]
+
+    # COD normalized to month-start
+    cod = pd.Timestamp(cod_date)
+    cod = pd.Timestamp(year=cod.year, month=cod.month, day=1)
+
+    optional_keys = ["base_dir", "market_file", "cf_file", "curtail_file", "eeg_file"]
+    optional_args = {k: revenue_kwargs[k] for k in optional_keys if k in revenue_kwargs}
+
+    monthly_rev = revenue_mod.generate_monthly_revenue(
+        tso_id=tso_id,
+        mwh_monthly_20y=mwh_df,
+        eeg_on=eeg_on,
+        manual_eeg_strike=manual_eeg_strike,
+        cod_date=cod,
+        forecast_months=FORECAST_MONTHS,
+        **optional_args,
+    )
+
+    if "date" not in monthly_rev.columns or "revenue_eur" not in monthly_rev.columns:
+        raise ValueError("Revenue output must contain columns: 'date' and 'revenue_eur'.")
+
+    monthly_rev["date"] = pd.to_datetime(monthly_rev["date"], errors="coerce")
+    monthly_rev = monthly_rev.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
+
+    print("\n===== REVENUE DRIVERS =====")
+    print("Avg delivered MWh / month:", monthly_rev["mwh_delivered"].mean())
+    print("Avg realised price €/MWh:", monthly_rev["p_wind_realised"].mean())
+    print("Avg revenue per month (k€):", monthly_rev["revenue_eur"].mean() / 1e3)
+    print("Avg market price €/MWh:", monthly_rev["p_market"].mean())
+    print("Avg capture factor:", monthly_rev["cf"].mean())
+    print("Avg curtailment:", monthly_rev["cr"].mean())
+    print("==========================\n")
+
+
+    # safety net
+    monthly_rev = monthly_rev[monthly_rev["date"] >= cod].copy()
+    if len(monthly_rev) < FORECAST_MONTHS:
+        raise ValueError(f"Revenue series has only {len(monthly_rev)} months from COD, need {FORECAST_MONTHS}.")
+    monthly_rev = monthly_rev.head(FORECAST_MONTHS).copy()
+
+    # -----------------------
+    # 2c) Aggregate by PROJECT YEAR (Year 1..20 from COD)
+    # -----------------------
+    moff = _month_index(monthly_rev["date"]) - (cod.year * 12 + (cod.month - 1))
+    monthly_rev["project_month"] = moff + 1
+    monthly_rev["project_year"] = (moff // 12) + 1
+
+    yearly_revenue = (
+        monthly_rev.groupby("project_year", as_index=False)["revenue_eur"]
+        .sum()
+        .sort_values("project_year")
+        .reset_index(drop=True)
+    )
+
+    horizon_years = int(np.ceil(FORECAST_MONTHS / 12.0))
+    year_starts = [cod + pd.DateOffset(months=12 * (y - 1)) for y in range(1, horizon_years + 1)]
+    yearly_revenue["year_start"] = year_starts
+
+    # -----------------------
+    # 3) CAPEX module  (CAPEX -> finance dependency handled correctly)
+    # -----------------------
+    capex_res = capex_mod.windpark_capex(
+        n_turbines=n_turbines,
+        turbine_type_id=turbine_type_id,
+        hub_height_m=hub_height_m,
+    )
+    capex_total = float(capex_res["total_capex_eur"])
+    park_mw = float(capex_res["park_mw"])
+
+    # -----------------------
+    # 4) OPEX module (NEW: aligned to forecast_months, returns project_year already)
+    # -----------------------
+    opex_df, opex_total = opex_mod.windpark_opex_timeseries(
+        park_mw=park_mw,
+        forecast_months=FORECAST_MONTHS,
+    )
+
+    # NEW opex.py returns: project_year, annual_opex_eur
+    if "project_year" not in opex_df.columns or "annual_opex_eur" not in opex_df.columns:
+        raise ValueError("OPEX output must contain columns: 'project_year' and 'annual_opex_eur'.")
+
+    opex_df = opex_df.sort_values("project_year").reset_index(drop=True)
+
+    # -----------------------
+    # 5) Financing module (NEW: equity_eur input, debt schedule returned)
+    # -----------------------
+    if equity_eur is None:
+        equity_eur = 0.15 * capex_total  # default: 15% of capex
+
+    fin = finance_mod.financing_model(
+        capex_eur=capex_total,
+        equity_eur=float(equity_eur),         # <-- absolute €
+        debt_rate=debt_rate,
+        equity_return=equity_return,
+        debt_tenor_years=debt_tenor_years,
+        forecast_months=FORECAST_MONTHS,
+    )
+
+    # aligned yearly debt schedule
+    debt_service_df = fin["debt_service_yearly_df"]
+    if "project_year" not in debt_service_df.columns or "debt_service_eur" not in debt_service_df.columns:
+        raise ValueError("finance.py must return debt_service_yearly_df with columns: project_year, debt_service_eur")
+
+    debt_service_df = debt_service_df.sort_values("project_year").reset_index(drop=True)
+
+    print("\n===== DEBT SERVICE SCHEDULE =====")
+    print(debt_service_df.head(5))
+    print("...")
+    print(debt_service_df.tail(5))
+    print("================================\n")
+
+
+    print("\n===== FINANCIAL SANITY CHECK =====")
+    print("Park MW:", park_mw)
+    print("CAPEX total (M€):", capex_total / 1e6)
+    print("CAPEX €/kW:", capex_res["total_capex_eur_per_kw"])
+    print("Equity (M€):", equity_eur / 1e6)
+    print("Equity share:", fin.get("equity_share_derived"))
+    print("Debt (M€):", fin["debt_eur"] / 1e6)
+
+    print("Average yearly debt service (M€):",
+        debt_service_df["debt_service_eur"].mean() / 1e6)
+
+    print("=================================\n")
+
+
+    # -----------------------
+    # 6) Unite yearly cashflows (by project_year)
+    # -----------------------
+    yearly = pd.DataFrame({
+        "project_year": list(range(1, horizon_years + 1)),
+        "year_start": year_starts,
+    })
+
+    yearly = yearly.merge(yearly_revenue[["project_year", "revenue_eur"]], on="project_year", how="left")
+    yearly = yearly.merge(opex_df[["project_year", "annual_opex_eur"]], on="project_year", how="left")
+    yearly = yearly.merge(debt_service_df[["project_year", "debt_service_eur"]], on="project_year", how="left")
+
+    # Fill any missing (shouldn't happen, but keeps things robust)
+    yearly["revenue_eur"] = yearly["revenue_eur"].fillna(0.0)
+    yearly["annual_opex_eur"] = yearly["annual_opex_eur"].fillna(0.0)
+    yearly["debt_service_eur"] = yearly["debt_service_eur"].fillna(0.0)
+
+    yearly["profit_after_opex_and_debt_eur"] = (
+        yearly["revenue_eur"] - yearly["annual_opex_eur"] - yearly["debt_service_eur"]
+    )
+
+    # -----------------------
+    # 7) Equity cashflows + NPV + IRR
+    # -----------------------
+    equity_cashflows = [-float(equity_eur)] + yearly["profit_after_opex_and_debt_eur"].tolist()
+
+    # -----------------------
+    # Terminal value (residual value at end of year 20)
+    # -----------------------
+    TERMINAL_VALUE_SHARE = 0.30   # 10% of CAPEX as salvage / repowering option
+    terminal_value = TERMINAL_VALUE_SHARE * capex_total
+
+    equity_cashflows[-1] += terminal_value
+
+    print(f"Terminal value added in year 20: {terminal_value/1e6:.1f} M€")
+
+
+    used_discount_rate = discount_rate if discount_rate is not None else float(fin["wacc"])
+    npv_eur = _compute_npv(equity_cashflows, used_discount_rate)
+    irr = npf.irr(equity_cashflows)
+
+    return {
+        "monthly_revenue_df": monthly_rev,
+        "yearly_df": yearly,
+        "capex_summary": {**capex_res},
+        "opex_summary": {"total_opex_eur": float(opex_total)},
+        "finance_summary": fin | {
+            "equity_eur_input": float(equity_eur),
+        },
+        "equity_cashflows": equity_cashflows,
+        "npv_eur": npv_eur,
+        "irr": irr,
+        "discount_rate_used": used_discount_rate,
+    }
+
+
+# -------------------------
+# Example call
+# -------------------------
+if __name__ == "__main__":
+    cod = pd.Timestamp.today()
+    cod = pd.Timestamp(year=cod.year, month=cod.month, day=1)
+
+    dates = pd.date_range(cod, periods=FORECAST_MONTHS, freq="MS")
+    example_mwh = pd.Series(24000.0, index=dates)
+
+    result = power_to_profit(
+        mwh_monthly_20y=example_mwh,
+        turbine_type_id=1,
+        n_turbines=12,
+        hub_height_m=160,
+        equity_eur=60_000_000,
+        revenue_kwargs={
+            "tso_id": 0,
+            "eeg_on": 1,
+            "manual_eeg_strike": None,
+            "cod_date": str(cod.date()),
+            # If you run from a different working dir, set:
+            # "base_dir": str(ROOT),
+        }
+    )
+
+    print("NPV (equity):", f"{result['npv_eur']/1e6:.1f} M€")
+    print("IRR (equity):", f"{result['irr']*100:.2f}%")
+    print(result["yearly_df"].head())
+    print(result["yearly_df"].tail(5))
+
+

model/revenue/capture_factor_history_forecast/capture_factor_forecast_b2.csv

@@ -0,0 +1,373 @@
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model/revenue/capture_factor_history_forecast/capture_factor_monthly_historical.csv

@@ -0,0 +1,133 @@
+month,p_market_eur_mwh,wind_mwh,price_x_wind,p_wind_capture_eur_mwh,capture_factor
+2015-01-01,31.05307098765432,7830853.25,193848426.705,24.754445079787445,0.7971657646881044
+2015-02-01,36.71691964285714,5325677.75,169937033.715,31.908996693425546,0.8690542944179981
+2015-03-01,31.34314939434724,7010958.25,181407454.6275,25.87484451608309,0.8255342879088481
+2015-04-01,29.724375,5157796.5,133846308.9275,25.950288835067454,0.8730305964403778
+2015-05-01,25.357916666666668,5105062.25,117917513.8575,23.098153966193067,0.9108853171899531
+2015-06-01,30.06438888888889,3871212.5,108847568.965,28.117177490256605,0.9352319647730498
+2015-07-01,34.99568548387097,5401805.25,159520997.85,29.531053132654122,0.8438483980050792
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+2015-09-01,31.881319444444443,5328808.5,149441333.1175,28.044042700633735,0.8796387097310245
+2015-10-01,39.38145161290323,3438960.5,126051994.9375,36.654097927993064,0.9307452220979952
+2015-11-01,32.389375,9763092.0,264461081.3275,27.087840750399565,0.8363187233591128
+2015-12-01,27.780604838709678,10359892.25,247403719.2625,23.880916257840422,0.8596254975904842
+2016-01-01,29.035524193548387,8406655.5,205866880.8525,24.488559196044132,0.843399934260027
+2016-02-01,21.989554597701147,9366679.75,174598047.8475,18.640334943393363,0.8476904277698321
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+2020-01-01,35.0349059139785,12670559.0,394734120.9,31.153646883298517,0.8892173696652799
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model/revenue/capture_factor_history_forecast/cf_his.py

@@ -0,0 +1,96 @@
+import pandas as pd
+import numpy as np
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+PRICES_FILE = "../day_ahead_market_prices/prices_hourly_merged.csv"
+WIND_FILE   = "../produced_mwh_wind/wind_onshore_hourly_merged.csv"
+
+# Column names – ADJUST if needed
+PRICE_DATE_COL = "datetime"
+PRICE_COL      = "price_eur_mwh"        # day-ahead price €/MWh
+
+WIND_DATE_COL  = "datetime"
+WIND_COL       = "wind_onshore_mwh"     # hourly wind generation in MWh
+
+OUTPUT_FILE = "capture_factor_monthly_historical.csv"
+
+# -----------------------------
+# 1. Load data
+# -----------------------------
+prices = pd.read_csv(PRICES_FILE, parse_dates=[PRICE_DATE_COL])
+wind   = pd.read_csv(WIND_FILE,   parse_dates=[WIND_DATE_COL])
+
+# ensure numeric
+prices[PRICE_COL] = pd.to_numeric(prices[PRICE_COL], errors="coerce")
+wind[WIND_COL]    = pd.to_numeric(wind[WIND_COL], errors="coerce")
+
+prices = prices.dropna(subset=[PRICE_DATE_COL, PRICE_COL])
+wind   = wind.dropna(subset=[WIND_DATE_COL, WIND_COL])
+
+# -----------------------------
+# 2. Merge hourly price & wind
+# -----------------------------
+hourly = prices.merge(
+    wind,
+    left_on=PRICE_DATE_COL,
+    right_on=WIND_DATE_COL,
+    how="inner",
+    suffixes=("_price", "_wind")
+)
+
+# keep one timestamp column only
+hourly["timestamp"] = hourly[PRICE_DATE_COL]
+hourly = hourly[["timestamp", PRICE_COL, WIND_COL]]
+
+# -----------------------------
+# 3. Add month identifier
+# -----------------------------
+hourly["month"] = hourly["timestamp"].dt.to_period("M").dt.to_timestamp()
+
+# -----------------------------
+# 4. Monthly aggregation
+# -----------------------------
+# Market price: simple average
+monthly_market = (
+    hourly.groupby("month")[PRICE_COL]
+    .mean()
+    .rename("p_market_eur_mwh")
+)
+
+# Wind capture price: wind-weighted average
+hourly["price_x_wind"] = hourly[PRICE_COL] * hourly[WIND_COL]
+
+monthly_wind = (
+    hourly.groupby("month")
+    .agg(
+        wind_mwh=(WIND_COL, "sum"),
+        price_x_wind=("price_x_wind", "sum")
+    )
+)
+
+monthly_wind["p_wind_capture_eur_mwh"] = (
+    monthly_wind["price_x_wind"] / monthly_wind["wind_mwh"]
+)
+
+# -----------------------------
+# 5. Combine & compute CF
+# -----------------------------
+monthly = pd.concat([monthly_market, monthly_wind], axis=1).reset_index()
+
+monthly["capture_factor"] = (
+    monthly["p_wind_capture_eur_mwh"] / monthly["p_market_eur_mwh"]
+)
+
+# -----------------------------
+# 6. Clean up & save
+# -----------------------------
+monthly = monthly.sort_values("month").reset_index(drop=True)
+
+monthly.to_csv(OUTPUT_FILE, index=False)
+print(f"Saved historical capture factors to: {OUTPUT_FILE}")
+
+# Optional: quick sanity stats
+print("\nCapture factor summary:")
+print(monthly["capture_factor"].describe())

model/revenue/capture_factor_history_forecast/reg_cf.py

@@ -0,0 +1,182 @@
+"""
+Scenario B2: Capture factor (CF) regression + forecast using scaled historical wind.
+
+We model:
+  z_t = log(CF_t)
+  z_t = const + phi*z_{t-1} + season(month) + beta*log(wind_mwh_t) + error
+
+Then we forecast CF for 20 years by:
+- building future wind_mwh_t by scaling a repeating monthly wind pattern
+  from a recent reference window, with an annual growth rate per scenario
+- recursively forecasting z_t with AR(1) dynamics
+
+Inputs (monthly, historical):
+  capture_factor_monthly_historical.csv
+    columns: month, p_market_eur_mwh, wind_mwh, p_wind_capture_eur_mwh, capture_factor
+
+Outputs:
+  capture_factor_forecast_b2.csv
+    month, wind_mwh_scen_*, cf_scen_*, p_wind_merchant_scen_* (optional)
+"""
+
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+INPUT_FILE = "capture_factor_monthly_historical.csv"  # created by your CF history script
+DATE_COL = "month"
+CF_COL = "capture_factor"
+WIND_COL = "wind_mwh"
+
+FORECAST_YEARS = 20
+
+# Reference window used to build the repeating seasonal wind pattern (monthly averages)
+REF_START = "2020-01-01"
+REF_END   = "2024-12-01"
+
+# Wind growth scenarios (annual growth of national wind generation proxy)
+WIND_GROWTH_SCEN = {
+    "low":  0.005,  # +0.5% p.a.
+    "base": 0.015,  # +1.5% p.a.
+    "high": 0.030,  # +3.0% p.a.
+}
+
+# CF forecast guardrails (applied after exp() back-transform)
+CF_FLOOR = 0.55
+CF_CAP   = 1.15
+
+OUTPUT_FILE = "capture_factor_forecast_b2.csv"
+
+# -----------------------------
+def to_month_start(s: pd.Series) -> pd.Series:
+    dt = pd.to_datetime(s)
+    return pd.to_datetime(dt.dt.to_period("M").dt.to_timestamp())
+
+# -----------------------------
+# 1) Load historical monthly CF
+# -----------------------------
+df = pd.read_csv(INPUT_FILE, parse_dates=[DATE_COL])
+df[DATE_COL] = to_month_start(df[DATE_COL])
+df = df.sort_values(DATE_COL).reset_index(drop=True)
+
+# Basic checks
+df[CF_COL] = pd.to_numeric(df[CF_COL], errors="coerce")
+df[WIND_COL] = pd.to_numeric(df[WIND_COL], errors="coerce")
+df = df.dropna(subset=[CF_COL, WIND_COL])
+
+# CF must be positive for log
+df = df[df[CF_COL] > 0].copy()
+
+# -----------------------------
+# 2) Build regression dataset: z_t = log(CF_t)
+# -----------------------------
+df["z"] = np.log(df[CF_COL].values.astype(float))
+df["z_lag"] = df["z"].shift(1)
+df["month_num"] = df[DATE_COL].dt.month.astype(int)
+
+# Seasonality via month dummies
+month_dummies = pd.get_dummies(df["month_num"], prefix="m", drop_first=True).astype(float)
+
+# Wind driver in logs (avoid log(0))
+df["log_wind"] = np.log(np.maximum(df[WIND_COL].values.astype(float), 1.0))
+
+X = pd.concat([df["z_lag"], df["log_wind"], month_dummies], axis=1)
+X = sm.add_constant(X)
+reg = pd.concat([df["z"], X], axis=1).dropna()
+
+model = sm.OLS(reg["z"].astype(float), reg[X.columns].astype(float)).fit()
+print(model.summary())
+
+params = model.params
+const = float(params["const"])
+phi = float(params["z_lag"])
+beta_wind = float(params["log_wind"])
+season_params = {k: float(v) for k, v in params.items() if k.startswith("m_")}
+
+# stability clamp (optional but sensible)
+phi = max(min(phi, 0.98), 0.0)
+
+print("\n--- Fitted ---")
+print(f"phi={phi:.4f}, beta_wind={beta_wind:.4f}, CF bounds [{CF_FLOOR}, {CF_CAP}]")
+
+# -----------------------------
+# 3) Build a repeating monthly wind pattern from the reference window
+# -----------------------------
+mask_ref = (df[DATE_COL] >= pd.Timestamp(REF_START)) & (df[DATE_COL] <= pd.Timestamp(REF_END))
+if mask_ref.sum() < 24:
+    raise ValueError("Reference window too short/missing. Adjust REF_START/REF_END.")
+
+ref = df.loc[mask_ref, [DATE_COL, WIND_COL]].copy()
+ref["m"] = ref[DATE_COL].dt.month
+
+# monthly seasonal wind pattern (average MWh for each month-of-year)
+wind_seasonal = ref.groupby("m")[WIND_COL].mean().to_dict()
+
+# anchor wind level: mean in ref window
+wind_anchor = float(ref[WIND_COL].mean())
+anchor_date = df.loc[mask_ref, DATE_COL].iloc[-1]
+
+print(f"Wind anchor (mean {REF_START}..{REF_END}): {wind_anchor:,.0f} MWh/month (anchor date {anchor_date.date()})")
+
+# -----------------------------
+# 4) Forecast horizon dates
+# -----------------------------
+last_date = df[DATE_COL].max()
+future_dates = pd.date_range(last_date + pd.offsets.MonthBegin(1), periods=FORECAST_YEARS * 12, freq="MS")
+
+# Starting state: last observed z
+z_prev = float(df["z"].iloc[-1])
+
+# -----------------------------
+# 5) Forecast CF under each wind-growth scenario
+# -----------------------------
+out = pd.DataFrame({DATE_COL: pd.concat([df[DATE_COL], pd.Series(future_dates)], ignore_index=True)})
+out["cf_hist"] = pd.concat([df[CF_COL], pd.Series([np.nan] * len(future_dates))], ignore_index=True)
+out["wind_mwh_hist"] = pd.concat([df[WIND_COL], pd.Series([np.nan] * len(future_dates))], ignore_index=True)
+
+for scen, g in WIND_GROWTH_SCEN.items():
+    # Build future wind series: seasonal shape * growth factor
+    wind_future = []
+    for d in future_dates:
+        months_from_anchor = (d.year - anchor_date.year) * 12 + (d.month - anchor_date.month)
+        growth_factor = (1.0 + g) ** (months_from_anchor / 12.0)
+
+        # seasonal baseline for month-of-year
+        base_m = float(wind_seasonal[int(d.month)])
+        # rescale so the mean matches wind_anchor
+        # (seasonal dict already in MWh, but this keeps growth around the anchor)
+        w = base_m * growth_factor
+        wind_future.append(w)
+
+    wind_future = np.array(wind_future, dtype=float)
+    log_wind_future = np.log(np.maximum(wind_future, 1.0))
+
+    # Forecast z_t recursively (deterministic P50 path)
+    z_path = np.zeros(len(future_dates), dtype=float)
+    z_prev_s = z_prev
+
+    for i, d in enumerate(future_dates):
+        season_effect = season_params.get(f"m_{d.month}", 0.0)  # month 1 baseline 0.0
+        z_now = const + phi * z_prev_s + beta_wind * log_wind_future[i] + season_effect
+        z_path[i] = z_now
+        z_prev_s = z_now
+
+    cf_future = np.exp(z_path)
+
+    # Apply guardrails
+    cf_future = np.clip(cf_future, CF_FLOOR, CF_CAP)
+
+    # Store
+    out[f"wind_mwh_{scen}"] = np.nan
+    out[f"cf_{scen}"] = np.nan
+
+    future_mask = out[DATE_COL].isin(future_dates)
+    out.loc[future_mask, f"wind_mwh_{scen}"] = wind_future
+    out.loc[future_mask, f"cf_{scen}"] = cf_future
+
+# Save
+out.to_csv(OUTPUT_FILE, index=False)
+print(f"\nSaved CF forecast (Scenario B2) to: {OUTPUT_FILE}")

model/revenue/curltailment_rate_per_tso/curltailment_rate_quarterly_by_tso.csv

@@ -0,0 +1,31 @@
+quarter,TenneT_curtailment_rate,TenneT_production_mwh,TenneT_curtailed_mwh,50Hertz_curtailment_rate,50Hertz_production_mwh,50Hertz_curtailed_mwh,Amprion_curtailment_rate,Amprion_production_mwh,Amprion_curtailed_mwh,TransnetBW_curtailment_rate,TransnetBW_production_mwh,TransnetBW_curtailed_mwh
+2015Q1,0.09530035207443505,8194858.75,863240.0,0.029447978426624857,8396098.5,254750.0,0.0045541750385832145,3597806.0,16460.0,0.0007189108500621559,250198.75,180.0
+2015Q2,0.09232223551591376,5428926.0,552190.0,0.03301157413500447,5115625.75,174640.0,0.0043909375926906415,2244743.75,9900.0,0.0010237457834470544,146370.75,150.0
+2015Q3,0.08906269152732005,5464026.75,534220.0,0.049698280341429805,5116318.5,267570.0,0.004713182524425895,2436911.75,11540.0,0.00911726229951419,204322.25,1880.0
+2015Q4,0.1376137133917989,9454346.25,1508660.0,0.056710695709236376,8709226.25,523600.0,0.0006524281568595988,3967195.75,2590.0,0.0022775368439033185,315411.0,720.0
+2016Q1,0.11403908314741337,9088086.75,1169800.0,0.03968417234191625,7965088.75,329150.0,0.002492969903268064,4453424.5,11130.0,0.002325947310452397,437511.0,1020.0
+2016Q2,0.06500282591989906,5783928.75,402110.0,0.02620616497341991,4798336.5,129130.0,0.0009612773559822682,2255243.0,2170.0,0.0017322469861923801,247802.5,430.0
+2016Q3,0.08397410061565538,5023442.75,460510.0,0.018784136594201518,4313682.75,82580.0,0.004142343442756978,1781432.5,7410.0,0.002580140321565849,235811.25,610.0
+2016Q4,0.2693268665746293,7883700.25,2905940.0,0.09684204258521692,7401934.0,793680.0,0.012692774868053544,3131623.25,40260.0,0.007125029600306795,473791.0,3400.0
+2017Q1,0.10103203552398203,9546415.0,1072890.0,0.041128314496888876,7744028.0,332160.0,0.001289273869082042,4415393.25,5700.0,0.0017965299333364555,650085.5,1170.0
+2017Q2,0.13542094040218902,7453511.0,1167460.0,0.028476218172948722,6627914.0,194270.0,0.00029222431572862825,2531561.25,740.0,0.0007084531402526044,366736.75,260.0
+2017Q3,0.04962306812140752,6028640.75,314780.0,0.020139071371826293,5215786.25,107200.0,0.004346506438805504,2636593.75,11510.0,0.0014176682356437237,366279.5,520.0
+2017Q4,0.12889244238320588,13156996.0,1946760.0,0.028161149640748697,11688162.75,338690.0,0.0033190713664527044,5939718.25,19780.0,0.002631969944601278,822307.5,2170.0
+2018Q1,0.1268028912140319,11630541.25,1688950.0,0.027486834090733287,9202966.0,260110.0,0.0031937154148919313,6164270.0,19750.0,0.002292579942596469,896491.0,2060.0
+2018Q2,0.09039803222877749,7801113.5,775290.0,0.02474019609469666,6392749.75,162170.0,0.002022288394415014,3163266.5,6410.0,0.0024771385095120036,479203.0,1190.0
+2018Q3,0.08619250707598622,6527082.25,615650.0,0.018843631395328308,5251611.5,100860.0,0.002359892078568851,2629493.75,6220.0,0.001063332210128814,347592.75,370.0
+2018Q4,0.12072562638072601,11807743.25,1621220.0,0.011933023176121825,10264847.5,123970.0,0.002576862337850747,5898929.25,15240.0,0.00394989642255305,811991.25,3220.0
+2019Q1,0.16309594006510778,13995837.0,2727510.0,0.03772119552742623,12866117.0,504350.0,0.004159575569904906,7141574.75,29830.0,0.002630054861863574,1107323.0,2920.0
+2019Q2,0.0757248899629744,8192464.25,671200.0,0.028003505063692098,6566750.0,189190.0,0.004115823000902675,3460096.25,14300.0,0.0013681578577072531,576628.75,790.0
+2019Q3,0.09242527777578635,7215897.0,734850.0,0.01889591780924663,5820917.5,112110.0,0.005497930961294054,3008144.25,16630.0,0.0013535094869534376,413179.25,560.0
+2019Q4,0.09914484544370618,11869281.5,1306290.0,0.020015040696809004,9827738.25,200720.0,0.004669104478904939,6663814.0,31260.0,0.0007175833586439269,1002647.75,720.0
+2020Q1,0.1263987966246904,16649725.75,2409000.0,0.03622689605066002,14179825.5,533000.0,0.001965673444636316,9139167.0,18000.0,0.0007300217236214406,1368822.25,1000.0
+2020Q2,0.08958656425205197,7652278.25,753000.0,0.026092787961127305,6307885.5,169000.0,0.005638221673873841,3527217.75,20000.0,0.008817720090293454,562040.0,5000.0
+2020Q3,0.10387380167918364,6547943.5,759000.0,0.023614342558073015,5044351.75,122000.0,0.004351101236528583,3203576.25,14000.0,0.016443234431193287,418707.0,7000.0
+2020Q4,0.0909823250777832,11889463.5,1190000.0,0.01652246982319331,9523782.25,160000.0,0.0012697024591160767,6292688.75,8000.0,0.0,776930.25,0.0
+2021Q1,0.1232079070182118,11194037.75,1573000.0,0.029212089188968154,8706889.5,262000.0,0.004553583535321305,5902396.0,27000.0,0.001115655236871736,895334.25,1000.0
+2021Q2,0.12498272369689316,8037269.5,1148000.0,0.04886486485043129,7124044.25,366000.0,0.006656793655483931,4029006.75,27000.0,0.003013372215879492,661708.25,2000.0
+2021Q3,0.09737752558343477,6562671.5,708000.0,0.03304682037784841,5413118.0,185000.0,0.011511429537566408,2919586.25,34000.0,0.0024022408102277806,415278.0,1000.0
+2021Q4,0.08872773855692674,11472073.25,1117000.0,0.034816724980992794,9813527.25,354000.0,0.0021196948398987865,5649192.25,12000.0,0.00129169552798862,773176.25,1000.0
+2022Q1,0.12810022516371697,15416467.75,2265000.0,0.06746653111498681,13366032.75,967000.0,0.006943418185353013,7437106.75,52000.0,0.0008432725298702942,1184856.25,1000.0
+2022Q2,0.1514992349312791,8781887.25,1568000.0,0.07451236551741768,6806484.0,548000.0,0.004101540635169408,4127784.0,17000.0,0.0,595612.0,0.0

model/revenue/curltailment_rate_per_tso/curltailmentcalc.py

@@ -0,0 +1,103 @@
+import os
+import pandas as pd
+
+# -------------------------------------------------
+# CONFIG
+# -------------------------------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+PRODUCTION_FILE = "wind_onshore_daily_by_tso.csv"
+CURTAILMENT_FILE = "curtailment.csv"
+
+OUTPUT_FILE = "curtailment_rate_quarterly_by_tso.csv"
+
+# TSO column names (must match both files)
+TSOS = ["TenneT", "50Hertz", "Amprion", "TransnetBW"]
+
+# -------------------------------------------------
+def parse_german_number(x):
+    """
+    Convert strings like '863,24' to float 863.24
+    """
+    if pd.isna(x):
+        return float("nan")
+    return float(str(x).replace(".", "").replace(",", "."))
+
+def parse_quarter_label(q):
+    """
+    'Q1 2015' -> pandas Period('2015Q1')
+    """
+    q = q.strip()
+    year = int(q[-4:])
+    quarter = int(q[1])
+    return pd.Period(year=year, quarter=quarter, freq="Q")
+
+def main():
+    # -----------------------------
+    # Load production data (daily)
+    # -----------------------------
+    prod = pd.read_csv(
+        os.path.join(BASE_DIR, PRODUCTION_FILE),
+        parse_dates=["date"]
+    )
+
+    # Ensure numeric
+    for tso in TSOS:
+        prod[tso] = pd.to_numeric(prod[tso], errors="coerce")
+
+    # Create quarter index
+    prod["quarter"] = prod["date"].dt.to_period("Q")
+
+    # Aggregate daily → quarterly (MWh)
+    prod_q = (
+        prod.groupby("quarter")[TSOS]
+        .sum()
+        .reset_index()
+    )
+
+    # -----------------------------
+    # Load curtailment data (quarterly)
+    # -----------------------------
+    curt = pd.read_csv(os.path.join(BASE_DIR, CURTAILMENT_FILE))
+
+    # Parse quarter
+    curt["quarter"] = curt["Quarter"].apply(parse_quarter_label)
+
+    # Parse German numbers & convert GWh → MWh
+    for tso in TSOS:
+        curt[tso] = curt[tso].apply(parse_german_number) * 1_000
+
+    # Keep only relevant columns
+    curt_q = curt[["quarter"] + TSOS]
+
+    # -----------------------------
+    # Merge production + curtailment
+    # -----------------------------
+    df = prod_q.merge(curt_q, on="quarter", how="inner", suffixes=("_prod", "_curt"))
+
+    # -----------------------------
+    # Compute curtailment rates
+    # -----------------------------
+    out = pd.DataFrame({"quarter": df["quarter"]})
+
+    for tso in TSOS:
+        prod_col = f"{tso}_prod"
+        curt_col = f"{tso}_curt"
+
+        out[f"{tso}_curtailment_rate"] = (
+            df[curt_col] / (df[curt_col] + df[prod_col])
+        )
+
+        out[f"{tso}_production_mwh"] = df[prod_col]
+        out[f"{tso}_curtailed_mwh"] = df[curt_col]
+
+    # Sort and save
+    out = out.sort_values("quarter").reset_index(drop=True)
+    out.to_csv(os.path.join(BASE_DIR, OUTPUT_FILE), index=False)
+
+    print(f"Saved: {OUTPUT_FILE}")
+    print("\nPreview:")
+    print(out.head(5).to_string(index=False))
+
+if __name__ == "__main__":
+    main()

model/revenue/curltailment_rate_per_tso/curtailment.csv

@@ -0,0 +1,31 @@
+Quarter,TenneT,50Hertz,Amprion,TransnetBW,GesamtWindOnshore
+Q1 2015,"863,24","254,75","16,46","0,18","969,69"
+Q2 2015,"552,19","174,64","9,90","0,15","588,37"
+Q3 2015,"534,22","267,57","11,54","1,88","733,45"
+Q4 2015,"1508,66","523,60","2,59","0,72","1819,05"
+Q1 2016,"1169,80","329,15","11,13","1,02","1451,41"
+Q2 2016,"402,11","129,13","2,17","0,43","481,78"
+Q3 2016,"460,51","82,58","7,41","0,61","497,90"
+Q4 2016,"2905,94","793,68","40,26","3,4","3498,02"
+Q1 2017,"1072,89","332,16","5,70","1,17","1300,17"
+Q2 2017,"1167,46","194,27","0,74","0,26","1162,75"
+Q3 2017,"314,78","107,20","11,51","0,52","336,05"
+Q4 2017,"1946,76","338,69","19,78","2,17","1654,56"
+Q1 2018,"1688,95","260,11","19,75","2,06","1431,34"
+Q2 2018,"775,29","162,17","6,41","1,19","765,82"
+Q3 2018,"615,65","100,86","6,22","0,37","575,48"
+Q4 2018,"1621,22","123,97","15,24","3,22","1111,99"
+Q1 2019,"2727,51","504,35","29,83","2,92","2520,17"
+Q2 2019,"671,20","189,19","14,30","0,79","730,51"
+Q3 2019,"734,85","112,11","16,63","0,56","700,43"
+Q4 2019,"1306,29","200,72","31,26","0,72","1199,15"
+Q1 2020,"2409,00","533,00","18,00","1,00","2169,00"
+Q2 2020,"753,00","169,00","20,00","5,00","636,00"
+Q3 2020,"759,00","122,00","14,00","7,00","520,00"
+Q4 2020,"1190,00","160,00","8,00","0,00","804,00"
+Q1 2021,"1573,00","262,00","27,00","1,00","1145,00"
+Q2 2021,"1148,00","366,00","27,00","2,00","995,00"
+Q3 2021,"708,00","185,00","34,00","1,00","527,00"
+Q4 2021,"1117,00","354,00","12,00","1,00","741,00"
+Q1 2022,"2265,00","967,00","52,00","1,00","1638,00"
+Q2 2022,"1568,00","548,00","17,00","0,00","1069,00"

model/revenue/curltailment_rate_per_tso/curtailment_forecast_quarterly_by_tso_2021_2046.csv

@@ -0,0 +1,417 @@
+quarter,TSO,wind_mwh,load_mwh,S,cr_hat
+2021Q1,50Hertz,10332139.598789917,26458894.51705215,0.3904977810819379,0.037591391757116115
+2021Q2,50Hertz,6250356.920074381,23348612.198897533,0.2676971490566582,0.033994561506153556
+2021Q3,50Hertz,5208241.8405525815,23550796.659246545,0.22114928492271257,0.03263117466462488
+2021Q4,50Hertz,9703123.698236724,25964529.24843711,0.3737068985689693,0.03709958684400646
+2022Q1,50Hertz,10435460.994777817,26591188.98963741,0.39244055611219636,0.037648295633622306
+2022Q2,50Hertz,6312860.489275125,23465355.25989202,0.2690289756688804,0.03403357070479426
+2022Q3,50Hertz,5260324.258958107,23668550.64254278,0.222249530121333,0.0326634008441535
+2022Q4,50Hertz,9800154.935219092,26094351.894679293,0.37556613687030754,0.03715404392990016
+2023Q1,50Hertz,10539815.604725594,26724144.934585594,0.39439299668986894,0.0377054826139917
+2023Q2,50Hertz,6375989.094167876,23582682.03619148,0.27036742828414845,0.034072773979050085
+2023Q3,50Hertz,5312927.501547689,23786893.39575549,0.22335524917666308,0.032695787352933506
+2023Q4,50Hertz,9898156.484571284,26224823.654152684,0.3774346251134435,0.037208771946569456
+2024Q1,50Hertz,10645213.76077285,26857765.65925852,0.3963551509022564,0.03776295410670125
+2024Q2,50Hertz,6439748.985109555,23700595.446372434,0.2717125398676517,0.03411217229447137
+2024Q3,50Hertz,5366056.776563165,23905827.862734266,0.22446646932182057,0.03272833498862286
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model/revenue/curltailment_rate_per_tso/merge.py

@@ -0,0 +1,106 @@
+import os
+import glob
+import re
+import pandas as pd
+
+# -------------------------------------------------
+# CONFIG
+# -------------------------------------------------
+# Put the script in the same folder as your CSVs (recommended).
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Which column to extract from each file:
+VALUE_COL = "Wind onshore [MWh] Calculated resolutions"
+
+# Output file
+OUT_FILE = os.path.join(BASE_DIR, "wind_onshore_daily_by_tso.csv")
+
+# Expected TSOs and filename patterns.
+# Adjust these if your filenames differ.
+TSO_PATTERNS = {
+    "TenneT":      "Tennet*.csv",       # note: your file uses 'Tennet' not 'TenneT'
+    "50Hertz":     "50Hertz*.csv",
+    "Amprion":     "Amprion*.csv",
+    "TransnetBW":  "TransnetBW*.csv",
+}
+
+# If your filenames are slightly different, add more patterns or rename keys.
+
+# -------------------------------------------------
+def parse_numeric_series(s: pd.Series) -> pd.Series:
+    """
+    Convert strings like '29,339.75' (US format with thousands comma)
+    or '29339.75' into float.
+    """
+    s = s.astype(str).str.strip()
+    # remove thousands separators (commas) but keep decimal dot
+    s = s.str.replace(",", "", regex=False)
+    return pd.to_numeric(s, errors="coerce")
+
+def read_one_tso(files, tso_name: str) -> pd.DataFrame:
+    """
+    Read all files for one TSO, extract Start date + VALUE_COL,
+    return a daily series with columns: date, <tso_name>.
+    """
+    parts = []
+    for f in sorted(files):
+        df = pd.read_csv(f, sep=";", dtype=str, encoding="utf-8", engine="python")
+
+        # Basic column checks
+        if "Start date" not in df.columns:
+            raise ValueError(f"'Start date' column not found in {f}")
+        if VALUE_COL not in df.columns:
+            raise ValueError(f"'{VALUE_COL}' not found in {f}. Available columns: {list(df.columns)}")
+
+        tmp = df[["Start date", VALUE_COL]].copy()
+        tmp = tmp.rename(columns={"Start date": "date", VALUE_COL: tso_name})
+
+        # Parse date (examples: 'Jan 1, 2015')
+        tmp["date"] = pd.to_datetime(tmp["date"], errors="coerce")
+
+        # Parse numeric
+        tmp[tso_name] = parse_numeric_series(tmp[tso_name])
+
+        parts.append(tmp)
+
+    out = pd.concat(parts, ignore_index=True)
+
+    # Drop bad dates, sort, dedupe (keep last in case of overlaps)
+    out = out.dropna(subset=["date"]).sort_values("date")
+    out = out.drop_duplicates(subset=["date"], keep="last").reset_index(drop=True)
+
+    # Keep only date part (daily frequency)
+    out["date"] = out["date"].dt.normalize()
+
+    return out
+
+def main():
+    series_list = []
+
+    # Find and read each TSO
+    for tso, pattern in TSO_PATTERNS.items():
+        paths = glob.glob(os.path.join(BASE_DIR, pattern))
+        if not paths:
+            raise FileNotFoundError(f"No files found for {tso} using pattern: {pattern}")
+
+        df_tso = read_one_tso(paths, tso)
+        series_list.append(df_tso)
+
+    # Merge all TSOs on date (outer join to keep full coverage)
+    merged = series_list[0]
+    for df in series_list[1:]:
+        merged = merged.merge(df, on="date", how="outer")
+
+    merged = merged.sort_values("date").reset_index(drop=True)
+
+    # Optional: check missingness
+    print("Rows:", len(merged))
+    print("Date range:", merged["date"].min().date(), "→", merged["date"].max().date())
+    print("\nMissing values per column:")
+    print(merged.isna().sum())
+
+    merged.to_csv(OUT_FILE, index=False)
+    print(f"\nSaved: {OUT_FILE}")
+
+if __name__ == "__main__":
+    main()

model/revenue/curltailment_rate_per_tso/pred_cr.py

@@ -0,0 +1,175 @@
+import numpy as np
+import pandas as pd
+import statsmodels.formula.api as smf
+
+# =============================
+# CONFIG
+# =============================
+WIND_DAILY_FILE = "wind_onshore_daily_by_tso.csv"
+LOAD_MONTHLY_FILE = "../monthly_gridload_per_tso/tso_grid_load_monthly_wide.csv"
+CURTAIL_FILE = "curltailment_rate_quarterly_by_tso.csv"
+
+FUTURE_START = "2021Q1"
+FUTURE_END   = "2046Q4"
+
+CR_FLOOR = 0.0
+CR_CAP = 0.30
+
+TSOS = ["TenneT", "50Hertz", "Amprion", "TransnetBW"]
+LOAD_COL_MAP = {
+    "TenneT": "load_mwh_tennet",
+    "50Hertz": "load_mwh_50hertz",
+    "Amprion": "load_mwh_amprion",
+    "TransnetBW": "load_mwh_transnetbw",
+}
+
+# Option B growth assumptions (scenario knobs)
+G_WIND = {tso: 0.01  for tso in TSOS}   # +1%/yr wind proxy
+G_LOAD = {tso: 0.005 for tso in TSOS}   # +0.5%/yr load proxy
+
+LEVEL_QTRS = 8  # last 8 quarters average = baseline level
+
+OUTPUT_FILE = "curtailment_forecast_quarterly_by_tso_2021_2046.csv"
+
+# =============================
+# HELPERS
+# =============================
+def to_quarter_period_from_dates(s):
+    return pd.to_datetime(s).dt.to_period("Q")
+
+def wide_to_quarter_long(df, date_col, value_cols, value_name):
+    df = df.copy()
+    df[date_col] = pd.to_datetime(df[date_col])
+    df["quarter"] = to_quarter_period_from_dates(df[date_col])
+    long = df.melt(id_vars="quarter", value_vars=value_cols,
+                   var_name="TSO", value_name=value_name)
+    return long.groupby(["quarter", "TSO"], as_index=False)[value_name].sum()
+
+def make_season_factors(df_q, value_col):
+    """
+    Returns season_factor per (TSO, q_num) normalized to mean=1 within each TSO.
+    """
+    tmp = df_q.copy()
+    tmp["q_num"] = tmp["quarter"].dt.quarter.astype(int)
+    seas = tmp.groupby(["TSO", "q_num"])[value_col].mean().reset_index()
+    seas["mean_tso"] = seas.groupby("TSO")[value_col].transform("mean")
+    seas["season_factor"] = seas[value_col] / seas["mean_tso"]
+    return seas[["TSO", "q_num", "season_factor"]]
+
+def make_future_series(df_q, value_col, g_map, future_q, last_q):
+    """
+    level = mean of last LEVEL_QTRS for each TSO
+    season = avg by q_num per TSO (normalized)
+    growth = (1+g)^(years_ahead) where years_ahead = quarters_ahead/4
+    """
+    df_q = df_q.copy().sort_values(["TSO", "quarter"])
+
+    # level per TSO (last LEVEL_QTRS) — warning-free
+    levels = (
+        df_q.groupby("TSO", as_index=True)[value_col]
+            .apply(lambda s: float(s.tail(LEVEL_QTRS).mean()))
+            .to_dict()
+    )
+
+    seas = make_season_factors(df_q, value_col)
+
+    future = pd.MultiIndex.from_product([future_q, TSOS], names=["quarter", "TSO"]).to_frame(index=False)
+    future["q_num"] = future["quarter"].dt.quarter.astype(int)
+
+    # FIX: last_q is a scalar Period
+    quarters_ahead = future["quarter"].apply(lambda p: p.ordinal) - last_q.ordinal
+    years_ahead = quarters_ahead / 4.0
+
+    future = future.merge(seas, on=["TSO", "q_num"], how="left")
+    future["season_factor"] = future["season_factor"].fillna(1.0)
+
+    out = []
+    for tso in TSOS:
+        g = float(g_map[tso])
+        lvl = float(levels[tso])
+
+        idx = future["TSO"] == tso
+        growth = (1.0 + g) ** years_ahead[idx].values
+        vals = lvl * future.loc[idx, "season_factor"].values * growth
+
+        out.append(pd.DataFrame({
+            "quarter": future.loc[idx, "quarter"].values,
+            "TSO": tso,
+            value_col: vals
+        }))
+
+    out = pd.concat(out, ignore_index=True).sort_values(["quarter", "TSO"]).reset_index(drop=True)
+    return out
+
+# =============================
+# LOAD & PREPROCESS
+# =============================
+# Wind (daily -> quarterly)
+wind = pd.read_csv(WIND_DAILY_FILE)
+wind_q = wide_to_quarter_long(wind, "date", TSOS, "wind_mwh")
+
+# Load (monthly -> quarterly)
+load = pd.read_csv(LOAD_MONTHLY_FILE)
+load = load.rename(columns={v: k for k, v in LOAD_COL_MAP.items()})
+load_q = wide_to_quarter_long(load, "date", TSOS, "load_mwh")
+
+# Curtailment (quarterly by TSO in wide columns)
+curt = pd.read_csv(CURTAIL_FILE)
+
+# Parse quarter like "2015Q1" safely
+curt["quarter"] = pd.PeriodIndex(curt["quarter"].astype(str), freq="Q")
+
+records = []
+for tso in TSOS:
+    records.append(pd.DataFrame({
+        "quarter": curt["quarter"],
+        "TSO": tso,
+        "cr": pd.to_numeric(curt[f"{tso}_curtailment_rate"], errors="coerce"),
+        "prod_mwh": pd.to_numeric(curt[f"{tso}_production_mwh"], errors="coerce"),
+        "curt_mwh": pd.to_numeric(curt[f"{tso}_curtailed_mwh"], errors="coerce"),
+    }))
+curt_q = pd.concat(records, ignore_index=True).dropna(subset=["quarter", "TSO", "cr"])
+
+# Merge historical modeling frame
+df = curt_q.merge(wind_q, on=["quarter", "TSO"], how="inner").merge(load_q, on=["quarter", "TSO"], how="inner")
+df["S"] = df["wind_mwh"] / df["load_mwh"]
+
+# =============================
+# FIT MODEL 0 ON ALL HISTORY
+# =============================
+model0 = smf.ols("cr ~ S * C(TSO)", data=df).fit()
+print(model0.summary())
+
+# =============================
+# FUTURE QUARTERS 2021Q1–2046Q4
+# =============================
+future_q = pd.period_range(pd.Period(FUTURE_START, freq="Q"),
+                           pd.Period(FUTURE_END, freq="Q"),
+                           freq="Q")
+
+# last observed quarter for growth baseline
+last_q_wind = wind_q["quarter"].max()
+last_q_load = load_q["quarter"].max()
+last_q = max(last_q_wind, last_q_load)
+
+# =============================
+# OPTION B: forecast wind & load -> future S
+# =============================
+wind_f = make_future_series(wind_q, "wind_mwh", G_WIND, future_q=future_q, last_q=last_q)
+load_f = make_future_series(load_q, "load_mwh", G_LOAD, future_q=future_q, last_q=last_q)
+
+future = wind_f.merge(load_f, on=["quarter", "TSO"], how="inner")
+future["S"] = future["wind_mwh"] / future["load_mwh"]
+
+# =============================
+# PREDICT CURTAILMENT
+# =============================
+future["cr_hat"] = model0.predict(future).clip(CR_FLOOR, CR_CAP)
+
+out = future[["quarter", "TSO", "wind_mwh", "load_mwh", "S", "cr_hat"]].copy()
+out = out.sort_values(["TSO", "quarter"]).reset_index(drop=True)
+
+out.to_csv(OUTPUT_FILE, index=False)
+print(f"\nSaved: {OUTPUT_FILE}")
+print(f"Quarter parsing OK (example): {curt['quarter'].iloc[0]}")
+print(f"Baseline last_q used for growth: {last_q}")

model/revenue/day_ahead_market_prices/market_price_forecast_20y_monthly.csv

@@ -0,0 +1,241 @@
+date,price_eur_mwh_forecast,log_price_shifted_forecast,mu_log_shifted
+2026-01-01,44.594443155319794,5.499601504905559,10.090382171916277
+2026-02-01,39.8876563183496,5.480170715076063,10.091211366154042
+2026-03-01,40.31309723395208,5.481942644935687,10.092040560391805
+2026-04-01,33.71001488980778,5.4540810942415465,10.092869754629568
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