The Cost of Carbon: Modeling Energy Economics & Emissions with Python

Environment Setup: Launching a Google Colab notebook (zero-install) and connecting it to Google Drive.

Library Imports: Writing the setup block: import pandas as pd, import numpy as np.

Data Ingestion:

• Loading the energy_data.csv file (containing costs and emission factors for Solar and Gas).

• Data Cleaning: Handling missing values, renaming columns, and setting the index to “Project Year (1-20).”

Initial Inspection: Using .head(), .describe(), and .info() to verify the data quality.

Defining Variables: Creating Python variables for Discount Rate ($r$), Fuel Price Growth, and Grid Degradation.

The LCOE Function: Writing a robust Python function calculate_lcoe(capex, opex, generation) that returns a single $/MWh value.

The Carbon Function: Writing a Python function calculate_emissions(fuel_usage, emission_factor) to sum total lifetime $CO_2$.

Running the Model: Applying these functions to the Solar and Gas datasets to get the baseline results (e.g., “Gas: $60/MWh, Solar: $45/MWh”).

Visualizing Baselines:

• Creating a Bar Chart comparing LCOE side-by-side using matplotlib.

• Creating a Dual-Axis Chart: Left Axis = Cost ($), Right Axis = Carbon ($CO_2$).

Building the “What-If” Machine:

• Writing a for loop to simulate a Carbon Tax rising from $0 to $100/ton.

• Recalculating the Gas LCOE for each tax step.

Plotting Sensitivity:

• Generating a Line Graph showing the Gas LCOE rising to meet/exceed Solar.

• Annotating the graph to highlight the “Breakeven Carbon Price.”