Battery Optimization Strategy

This project explores and implements multiple strategies for battery optimization, including rule-based, threshold-based, and linear programming (LP) approaches. It provides a modular framework for simulation, evaluation, and sensitivity analysis of these strategies.

📁 Project Structure

battery_optimization_strategy-dev/
├── main.py                    # Main entry point
├── models/                   # Optimization strategies
│   ├── LP_optimization.py
│   ├── rule_based.py
│   ├── threshold_based.py
│   └── base_simulator.py     # Common simulation base
├── utils/                    # Utility functions
│   ├── plotting.py
│   └── utils.py
├── notebooks/                # Jupyter notebooks for analysis
│   ├── Battery_optimisation_strategy.ipynb
│   ├── EDA.ipynb
│   └── Sensitivity_Analysis.ipynb
└── .gitignore

🚀 Getting Started

Prerequisites

Ensure you have Python 3.8+ installed.

1. Install uv package manager:

pip install uv

2. Create a new environment using uv:

uv venv

3. Activate the environment:

• On macOS/Linux:

source .venv/bin/activate

• On Windows:

.venv\Scripts\activate

4. Install the required packages:

uv pip install .

This project uses pyproject.toml to manage dependencies with uv.

Data Preparation

Ensure that all required input data is placed in the data/ directory at the project root.

Running the Project

Command-Line Arguments

The main.py script accepts the following argument:

• --simulate: (optional)
  Run a new simulation instead of using saved results.

• --data-path: (required)
  Path to the CSV file containing price data. Example: data/Day-ahead_prices.csv

• --strategy: (required)
  Name of the strategy to simulate. Choices:

  • Threshold-Based
  • Rule-Based
  • Linear-Programming

Example Usage

python main.py --simulate \
               --data-path data/Day-ahead_prices.csv \
               --strategy Linear-Programming

⚙️ Optimization Models

• Rule-Based Strategy: Simple heuristics for charging/discharging based on predefined rules.
• Threshold-Based Strategy: Uses upper/lower thresholds for decision-making.
• LP Optimization: Applies Linear Programming to optimize energy storage/usage cost-effectively.

Each strategy is implemented in a modular fashion under models/.

📊 Notebooks

• EDA.ipynb: Exploratory Data Analysis of the input data.
• Battery_optimisation_strategy.ipynb: Demonstrates strategy simulations and visualizations.
• Sensitivity_Analysis.ipynb: Examines robustness of strategies to parameter variations.

📈 Visualizations

Custom plots and charts for performance evaluation are handled via utils/plotting.py.

📦 Utilities

Helper functions are available in utils/utils.py for common tasks like data handling, metrics, and logging.

🧠 Contributing

Contributions are welcome! Please fork the repo, create a feature branch, and submit a pull request.

To ensure code quality and consistency, this project uses pre-commit for automatic code formatting and static checks.

🔧 Pre-commit Setup

1. Install pre-commit (once per machine):

   pip install pre-commit

2. Install the Git hook (once per repo):

   pre-commit install

3. Run all checks manually (optional):

   pre-commit run --all-files

This will automatically run formatters like black, isort, and type checks (mypy) before every commit. Please make sure all checks pass before pushing.

📝 License

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