incorporate power generation sensivitity into doc

Author: softwareengineerprogrammer

docs/Fervo_Project_Red.md.jinja

@@ -178,9 +178,9 @@ Both models demonstrate high predictive fidelity, tracking steady-state flowing
 
 <div id="long-term-forecast-section"></div>
 
-### Long-Term Forecast (8-Year Horizon)
+### Long-Term Forecast ({{ long_term_forecast_years }}-Year Horizon)
 
-To evaluate the model's predictive behavior over a longer timeframe, the GEOPHIRES simulation was extended to an 8-year horizon.
+To evaluate the model's predictive behavior over a longer timeframe, the GEOPHIRES simulation was extended to an {{ long_term_forecast_years }}-year horizon.
 This timeframe aligns with the redrilling interval modeled in the [Cape Station case study](Fervo_Project_Cape-5.html)
 and provides a plausible view of the anticipated thermal decline before major wellfield intervention would be required.
 
@@ -225,6 +225,14 @@ While the {{ input_params['Number of Fractures'] }}-fracture baseline is current
 estimate based on existing data alignment, time and additional multi-year operational data will ultimately determine
 which structural interpretation within this predictive envelope is the most accurate.
 
+To further quantify the impact of these varying fracture geometries, the corresponding average annual net electricity
+generation over the {{ long_term_forecast_years }}-year horizon was also evaluated. While the flowing temperatures across all sensitivity cases
+essentially converge by year {{ long_term_forecast_years }}, their distinct intermediate thermal decline paths result in measurable differences in
+total power output. Utilizing Average Annual Net Electricity Generation (GWh) isolates these aggregate differences,
+providing a relative basis for comparing the lifecycle performance of each sensitivity case.
+
+![](_images/fervo_project_red-2026_production-temperature-data-vs-modeling-power-sensitivity.png)
+
 ## Discussion
 
 ### Evaluating the Predictive Power of Gringarten

docs/_images/fervo_project_red-2026_production-temperature-data-vs-modeling-1.png

@@ -4,6 +4,7 @@
 from typing import Any
 
 import cv2
+import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -509,9 +510,9 @@ def _generate_fracture_sensitivity_graph(
     show_excluded_measured_temperatures: bool = False,
     calculate_stats: bool = True,
 ) -> pd.DataFrame:
-    import matplotlib.patches as mpatches
-
-    _log.info('Running 8-year fracture sensitivity analysis (including power generation)...')
+    _log.info(
+        f'Running {_LONG_TERM_FORECAST_PLANT_LIFETIME_YEARS}-year fracture sensitivity analysis (including power generation)...'
+    )
 
     is_steady_state = _get_steady_state_mask(df_prod, steady_state_start_years)
 
@@ -613,7 +614,7 @@ def _generate_fracture_sensitivity_graph(
         final_temp_degc = float(geophires_y[-1]) if geophires_y else 0.0
         power_data.append(
             {
-                'Number of Fractures': frac_count,
+                number_of_fractures_param_name: frac_count,
                 #'Average Net Electricity Production (MW)': avg_generation_v,
                 f'{avg_generation_param} ({avg_generation_u})': avg_generation_v,
                 f'Year {_LONG_TERM_FORECAST_PLANT_LIFETIME_YEARS} Flowing Temperature (°C)': final_temp_degc,
@@ -672,16 +673,16 @@ def _savefig(version: int | str) -> None:
     plt.close(fig)
 
     df_power = pd.DataFrame(power_data)
-    df_power = df_power.sort_values('Number of Fractures').reset_index(drop=True)
+    df_power = df_power.sort_values(number_of_fractures_param_name).reset_index(drop=True)
     df_power.to_csv(power_csv_path, index=False)
 
     fig_pwr, ax_pwr = plt.subplots(figsize=(8, 5))
-    x_labels = [str(x) for x in df_power['Number of Fractures']]
+    x_labels = [str(x) for x in df_power[number_of_fractures_param_name]]
     y_values = df_power[f'{avg_generation_param} ({avg_generation_u})']
 
     bars = ax_pwr.bar(x_labels, y_values, color='#1f77b4', alpha=0.8, edgecolor='black')
 
-    baseline_idx = df_power.index[df_power['Number of Fractures'] == base_number_of_fractures].tolist()[0]
+    baseline_idx = df_power.index[df_power[number_of_fractures_param_name] == base_number_of_fractures].tolist()[0]
     bars[baseline_idx].set_color('green')
     bars[baseline_idx].set_edgecolor('black')
 
@@ -762,6 +763,7 @@ def _get_input_params_dict_with_nbsp() -> dict[str, Any]:
         'geophires_rmse_degc': f'{geophires_stats_alignment.rmse_degc:.2f}',
         'geophires_r2': f'{geophires_stats_alignment.r2:.4f}',
         'geophires_bias_degc': f'{geophires_stats_alignment.bias_degc:.2f}',
+        'long_term_forecast_years': _LONG_TERM_FORECAST_PLANT_LIFETIME_YEARS,
     }
 
     # Set up Jinja environment