init

.gitignore

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+venv/*
+.ipynb_checkpoints

hehe.py

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+# Re-run the computation after environment reset
+import math
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Parameters
+area_south_m2 = 30
+area_east_m2 = 44
+density_kwp_per_m2 = 0.19
+
+yield_south_kwh_per_kwp = 1000
+yield_east_kwh_per_kwp = 900
+
+price_grid = 0.34
+feedin_tariff = 0.0796
+
+capex_per_kwp = 1350
+battery_cost_6 = 2700  # 6 kWh * 450 €/kWh
+battery_cost_8 = 3600  # 8 kWh * 450 €/kWh
+
+discount = 0.03
+horizons = [15, 20]
+
+# Derived PV size and yield
+kwp_south = area_south_m2 * density_kwp_per_m2
+kwp_east = area_east_m2 * density_kwp_per_m2
+kwp_total = kwp_south + kwp_east
+
+gen_total = kwp_south * yield_south_kwh_per_kwp + kwp_east * yield_east_kwh_per_kwp
+load = 8000
+pv_capex = kwp_total * capex_per_kwp
+
+scenarios = [
+    {"name": "PV (Süd+Ost), ohne Speicher", "autarky": 0.55, "capex_extra": 0, "battery_cost": 0},
+    {"name": "PV + 6 kWh Speicher", "autarky": 0.78, "capex_extra": battery_cost_6, "battery_cost": battery_cost_6},
+    {"name": "PV + 8 kWh Speicher", "autarky": 0.82, "capex_extra": battery_cost_8, "battery_cost": battery_cost_8},
+]
+
+def npv(cashflows, r):
+    return sum(cf / ((1 + r) ** t) for t, cf in enumerate(cashflows))
+
+def analyze(horizon_years):
+    rows = []
+    curves = {}
+    for s in scenarios:
+        capex0 = pv_capex + s["capex_extra"]
+        cashflows = [-capex0]
+        
+        baseline_cost = load * price_grid
+        self_consumed = min(gen_total, s["autarky"] * load)
+        grid_import = load - self_consumed
+        feedin = max(0, gen_total - self_consumed)
+        annual_cost_with_pv = grid_import * price_grid - feedin * feedin_tariff
+        annual_savings = baseline_cost - annual_cost_with_pv
+        
+        for year in range(1, horizon_years + 1):
+            cf = annual_savings
+            if s["battery_cost"] > 0 and year == 12:
+                cf -= 0.5 * s["battery_cost"]
+            if year == 20:
+                cf -= 0.5 * pv_capex
+            cashflows.append(cf)
+        
+        cum = 0
+        curve = []
+        for cf in cashflows:
+            cum += cf
+            curve.append(cum)
+        curves[s["name"]] = curve
+        
+        payback = None
+        cum2 = 0
+        for t, cf in enumerate(cashflows):
+            cum2 += cf
+            if cum2 >= 0 and payback is None:
+                payback = t
+        
+        rows.append({
+            "Szenario": s["name"],
+            "PV-Größe (kWp)": round(kwp_total, 1),
+            "PV-Ertrag (kWh/a)": int(gen_total),
+            "Autarkie (Annahme)": f"{int(s['autarky']*100)} %",
+            "Start-Invest": int(capex0),
+            "Jährliche Ersparnis": int(annual_savings),
+            "Payback (Jahr)": payback if payback is not None else f"≥{horizon_years+1}",
+            f"Summe Cashflow {horizon_years}J (ohne Abzinsung)": int(sum(cashflows)),
+            f"NPV {horizon_years}J @3%": int(round(npv(cashflows, discount), 0)),
+        })
+    return pd.DataFrame(rows), curves
+
+dfs = {}
+curves_by_horizon = {}
+for H in horizons:
+    df, curves = analyze(H)
+    dfs[H] = df
+    curves_by_horizon[H] = curves
+
+from caas_jupyter_tools import display_dataframe_to_user
+display_dataframe_to_user("15 Jahre (Batterie 450 €/kWh): Ergebnisse", dfs[15])
+display_dataframe_to_user("20 Jahre (Batterie 450 €/kWh): Ergebnisse", dfs[20])
+
+plt.figure()
+for name, curve in curves_by_horizon[15].items():
+    plt.plot(range(0, 16), curve[:16], label=name)
+plt.xlabel("Jahr")
+plt.ylabel("Kumulierter Cashflow (€)")
+plt.title("Kumulierter Cashflow über 15 Jahre (Batterie 450 €/kWh)")
+plt.legend()
+plt.show()
+
+plt.figure()
+for name, curve in curves_by_horizon[20].items():
+    plt.plot(range(0, 21), curve[:21], label=name)
+plt.xlabel("Jahr")
+plt.ylabel("Kumulierter Cashflow (€)")
+plt.title("Kumulierter Cashflow über 20 Jahre (Batterie 450 €/kWh)")
+plt.legend()
+plt.show()