suleimanelkhoury/ec-scheduling

Three implemented evolutionary strategies using DEAP to optimize energy scheduling tasks.

EC-Scheduler

Project Overview

Welcome to EC-Scheduler! This project provides a comprehensive solution for optimizing and scheduling general energy resources. It consists of three main components:

1. Optimization Method (ec-scheduler): This component implements various evolutionary computing methods for optimizing energy scheduling using the DEAP (Distributed Evolutionary Algorithms in Python) library. It includes:

   • NSGA2 (Non-dominated Sorting Genetic Algorithm II)
   • PSO (Particle Swarm Optimization)
   • CMAES (Covariance Matrix Adaptation Evolution Strategy)

2. Mockup Scheduler (mockup_scheduler): This component simulates the scheduling of five different types of energy facilities:

   • Photovoltaic (solar panels)
   • Wind Turbine
   • Two Batteries
   • Combined Heat and Power Plant

3. User Interface (user_interface): Built using PyQt5, this component provides a graphical interface to interact with the optimization methods. It allows users to configure and execute optimization tasks and view results.

Getting Started

To get started with ec-scheduler, simply execute the start_services.sh script to build and run the necessary Docker containers. This script handles the setup and execution of all services required for the project.

Project Structure

• start_services.sh: Script to build and run Docker containers.
• delete_services.sh: Script to delete Docker containers.
• optimization_method: Directory for the optimization methods.
• mockup_scheduler: Directory for a simple mockup scheduler project.
• user_interface: Directory for the PyQt5 UI.

Contributing

Contributions are welcome! To contribute, please fork the repository and submit a pull request.