/sed-evolutionary-computing-dragonfly

This work proposes a planning methodology of distribution systems formulated as a nonlinear optimization problem, which was solved through the heuristic Dragonfly Optimization Algorithm, which will be developed with the integration between OpenDSS for power flow calculation and Python for collection, modifying the feeder and displaying the results. The Dragonfly Algorithm is responsible for the reconfiguration of the feeder, with the objective function of minimizing the costs of expansion and technical losses. The proposed methodology was tested on the IEEE 123 feeder adapted buses, which is a test network with more than 123 buses, multiple switches, regulators, transformers, etc. Finally, the reconfiguration proposal brought an economy 22% compared to the original expansion plan, simulated with Dragonfly Algorithm, with 30 dragonflies and maximum number of iterations equal to 25, showing the effectiveness of the algorithm when applied to electric power distribution systems.

Primary LanguagePython

PESDEE-DRAGONFLY-ALGORITHM - Distribution System Planning with Dragonfly Optimization

name: Fernando Calenzani Muller
email: fernandocalenzani@gmail.com
instagram: @fernandocalenzani
wechat: fernandocalenzani

FEDERAL INSTITUTE OF ESPIRITO SANTO, BRAZIL

This work was done 27/12/2021. Sorry for the messed up code.
Feel free to email me

OBS:

  1. You should go to project/config and replace the paths in general.py
  2. replace the costs to your reality, this costs are in BRL

This repository presents a unique planning methodology for distribution systems, formulated as a nonlinear optimization problem, leveraging the heuristic Dragonfly Optimization Algorithm. Inspired by the swarming behaviors of dragonflies in nature, the algorithm incorporates both exploration and exploitation phases, mimicking the social interactions of dragonflies navigating, searching for food, and avoiding threats.

Methodology Overview

  • Algorithm Inspiration: Derived from the static and dynamic swarming behaviors of dragonflies, the Dragonfly Optimization Algorithm is designed for optimization tasks.
  • Integration with OpenDSS and Python: Utilizes OpenDSS for power flow calculations and Python for data collection, feeder modification, and result visualization.
  • Objective Function: The Dragonfly Algorithm focuses on reconfiguring the feeder to minimize both expansion costs and technical losses.
  • Testing and Results: The proposed methodology is tested on the IEEE 123 feeder adapted buses, demonstrating a 22% cost reduction compared to the original expansion plan. The simulation employs 30 dragonflies with a maximum of 25 iterations.

Article Link

For an in-depth exploration of the methodology and detailed results, please refer to the article.

Repository Information

  • GitHub Username: fernandocalenzani
  • Repository Name: evolutionary-computing-dragonfly

How to Access and Contribute

To access the PESDEE-DRAGONFLY-ALGORITHM repository and contribute to its development, follow these steps:

  1. Clone the Repository: Use the following command to clone the repository to your local machine: 
    git clone https://github.com/fernandocalenzani/evolutionary-computing-dragonfly.git
  2. Explore and Contribute: Familiarize yourself with the repository structure and explore opportunities for contribution.
  3. Fork and Pull Request: Contribute by forking the repository, creating a new branch, implementing changes, and submitting a pull request with a summary of your modifications.

License

This project is licensed under the MIT License, allowing for modifications and distribution.

Acknowledgments

We extend our gratitude to the contributors and researchers involved in the development of the Dragonfly Optimization Algorithm and its application in electric power distribution systems.