Energy System Optimization Model ⚡

This project implements a data-driven energy system optimization model using Python and Pyomo. It simulates electricity generation, capacity expansion, and transmission between two interconnected nodes under a cost-minimization framework.

The model uses:

• Hourly electricity demand data (imported from CSV files)
• Renewable generation profiles (wind and solar capacity factors)
• Linear optimization to determine optimal generation dispatch and capacity investments

This project is inspired by academic energy system modelling approaches and serves as a simplified implementation of real-world power system optimization problems.

📌 Overview

The model determines the optimal mix of:

• Wind generation
• Solar generation
• Gas generation
• Transmission capacity between nodes

It minimizes total system cost while meeting demand constraints.

📊 Data Sources

• Demand data: Demand data: manually extracted and structured from a reference academic energy system study
• Wind and solar profiles: representative capacity factor time series
• Time resolution: hourly (24-hour simplified case)

Note: This is a simplified representation of real-world datasets such as ENTSO-E (demand) and ERA5 (renewables), used in large-scale energy system models.

🧠 Key Features

• Multi-node energy system (Node A and B)
• Hourly demand balancing
• Renewable generation constraints using capacity factors
• Transmission flow limits
• Cost-based optimization using linear programming

⚙️ Technologies Used

• Python
• Pyomo
• HiGHS Solver

📊 Model Components

Decision Variables

• Generation: wind, solar, gas
• Capacity: wind, solar, gas, transmission
• Power flow between nodes

Constraints

• Demand satisfaction at each node
• Generation limits based on capacity
• Transmission capacity limits

Objective

Minimize:

• Generation cost (gas)
• Capacity investment cost (wind, solar, gas, transmission)

🚀 How to Run

1. Install dependencies: pip install -r requirements.txt

2. Run the model: python test.py

💡 Future Improvements

• Add storage (battery systems)
• Expand to multiple nodes
• Include emissions constraints
• Add visualization of results

📈 Example Output

Sample results from the model:

Hour 10:

• Flow A→B: 28.0
• A Wind: 74.48
• A Gas: 0.0
• B Wind: 0.0

Hour 15:

• Flow A→B: 22.0
• A Wind: 70.56
• A Gas: 0.0

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🌍 Real-World Relevance

This model demonstrates how optimization techniques are applied in real-world energy system planning.

It reflects key challenges in the energy transition, such as:

• Balancing variability in renewable energy (wind and solar)
• Meeting hourly electricity demand
• Optimizing power transmission between regions
• Minimizing system costs under operational constraints

Such models are widely used in energy system analysis, sustainability studies, and policy planning.

👩‍💻 Author

Ahalditha
Master’s in Sustainable Energy Engineering (Lund University)