Investment in Vehicle-to-Grid and Distributed Energy Resources 🏠🚗⚡

This repository contains the implementation of the research paper "Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures". The paper presents a cost-benefit analysis of various investment scenarios for a distributor and a prosumer, considering the impact of different rate structures.

Project Description 📖

The main objectives of this project are:

• To evaluate the profitability of investments in distributed energy resources (DER), vehicle-to-grid (V2G) technology, and energy management systems (EMS) from the perspectives of both the distributor and the prosumer.
• To analyze the impact of different rate structures on the viability of these investment scenarios.

The study utilizes real-world data from four single-detached houses in Vermont, USA, to model the electricity demand, solar PV generation, and the behavior of the distributor and prosumer under various investment and rate structure scenarios.

Implementation 💻

The implementation of the proposed models and analysis is provided in a Jupyter Notebook called Main.ipynb. This notebook includes the following steps:

1. Data Loading and Preprocessing: The electricity consumption and solar PV generation data for the four houses are loaded and preprocessed.
2. Modeling and Optimization: The distributor and prosumer models are implemented using the Gurobi optimization solver in Python. The models consider different investment scenarios and rate structures to evaluate their profitability.
3. Visualization and Analysis: The results of the optimization models are visualized and analyzed, including the net present value (NPV), internal rate of return (IRR), energy flows, and peak loads for each scenario.
4. Sensitivity Analysis: The sensitivity of the results to changes in load patterns, battery capacity, and solar panel system size is investigated.

Required Libraries 📚

The following Python libraries are used in the implementation:

• numpy: for numerical operations
• pandas: for data manipulation and analysis
• matplotlib: for static data visualization
• plotly: for interactive data visualization
• gurobipy: for optimization modeling and solving
• os: for file path operations

To run the Jupyter Notebook, you will need to have Gurobi Optimizer installed and a valid Gurobi license. You can obtain a free academic license from the Gurobi website.

How to Use 🚀

1. Clone the repository to your local machine:

   git clone https://github.com/srmadani/Investment_in_DER.git
   

2. Install the required libraries, including Gurobi Optimizer.

3. Open the Main.ipynb Jupyter Notebook and run the cells to reproduce the analysis and results.

Contribution 🤝

If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request. Contributions are welcome!

Acknowledgments 🙏

The authors would like to acknowledge the support from the Fonds de Recherche du Quebec – Nature et Technologies (FRQNT) [300234]. We also thank Eric Rondeau for his help and dcbel and a utility operating in the Northeast United States for their collaboration.

References 📚

Madani, S., & Pineau, P.-O. (2024). Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures. Utilities Policy, 88, 101736. https://doi.org/10.1016/j.jup.2024.101736