Private Investor GEP

Private investor generation expansion planning example.

Overview

This project demonstrates a generation expansion planning model for a private investor using Pyomo, a Python-based open-source optimization modeling language. The model optimizes the investment and operation of a photovoltaic (PV) system to meet household electricity demand in Spain.

Project Structure

• inputs/: Contains input data files for the model.
  • dayahead_price_forecast.csv: Day-ahead electricity price forecast.
  • electricity_demand_profile_household_spain.csv: Household electricity demand profile in Spain.
  • solar_pv_spain_uncorrected.csv: Uncorrected solar PV generation profile in Spain.
• private-investor-gep.ipynb: Jupyter Notebook implementing the generation expansion planning model.
• LICENSE: License file for the project.
• README.md: This file.

Installation

To run this project, you need to have Python installed. You can install the required packages using pip:

sh pip install pyomo highspy pandas matplotlib seaborn

Usage

1. Clone the repository: sh git clone https://github.com/yourusername/private-investor-gep.git cd private-investor-gep
2. Open the Jupyter Notebook: sh jupyter notebook private-investor-gep.ipynb
3. Run the cells in the notebook to execute the model and visualize the results.

Model Description

The model includes the following components:

• Decision Variables:

  • v_production: Electricity production from the PV system.
  • v_purchasing: Electricity purchased from the grid.
  • v_selling: Electricity sold to the grid.
  • v_investment: Investment in the PV system.

• Expressions:

  • e_demand: Electricity demand.
  • e_savings: Savings from electricity production.
  • e_taxes: Taxes on electricity sales.
  • e_investment_cost: Investment cost.
  • e_fix_om_cost: Fixed operation and maintenance cost.

• Objective Function: Maximizes the net savings by considering savings from production, taxes, investment cost, and fixed operation and maintenance cost.

• Constraints:

  • Maximum production constraint.
  • Balance constraint ensuring supply meets demand.

• Results: The results of the model are stored in a DataFrame and visualized as a heatmap showing the production over time.

License

This project is licensed under the MIT License. See the LICENSE file for details.