This Streamlit application showcases a basic electricity generation capacity expansion model. The primary objective of this model is to minimise fixed and variable costs among a range of generators to match projected future electricity demands. Beyond presenting numerical results, our optimisation engine employs OpenAI's API to interpret outcomes in concise, user-friendly paragraphs. The diagram below illustrates the foundational structure of this model.

App is deployed on streamlit at the link below:

https://cem-openai.streamlit.app/

-Data Import: Users have the flexibility to import their own data in CSV format or utilise default values provided within the model.

-Optimisation Engine:: The model employs Pyomo and utilises the GLPK solver to efficiently solve the optimisation problem. It accounts for conventional and renewable power generators, integrating renewables capacity factors derived from time-series data.

-Decision Insights: After the optimisation process, the outputs are passed through the OpenAI API. This API interprets the results and presents users with a brief paragraph summarizing the decisions made by the model.

The model takes into account the renewable power sources by factoring in their capacities based on provided time series data. By utilising Pyomo and the GLPK solver, it optimises decisions that lead to cost-efficiency, ultimately minimising the overall expenses within the system. The OpenAI API aids in interpreting these outcomes, presenting users with insightful summaries of the decisions made by the model, rooted in cost-effective considerations

• Streamlit: For web development and user interface
• Pyomo: For optimisation modeling
• Plotly: For graphical representations
• Pandas and NumPy: For data manipulation and computation

To run the app:

1. Ensure Python and the required libraries are installed.
2. Clone the repository.
3. Install necessary dependencies (pip install -r requirements.txt).
4. Run the Streamlit app (streamlit run app.py).
5. Explore the model using either default values or by importing your own data.

Feel free to contribute, raise issues, or suggest enhancements to this project.