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 part of our work implements three various evolutionary algorithms using the DEAP (Distributed Evolutionary Algorithms in Python) library. It includes:

   • NSGA2.py : Non-dominated Sorting Genetic Algorithm II.
   • PSO.py : Particle Swarm Optimization.
   • CMAES.py : Covariance Matrix Adaptation Evolution Strategy.
   • ea_utils.py: This file contains the helper, statistics, plotting, and parallelization functionalities for each evolutionary algorithms.
   • Strategy.py: This file contains the strategy logic for generating and updating the population in CMA-ES.
   • genotyp_phenotyp.py: This file contains logic used to convert chromosomes into the corresponding scheduling plans, and send and receive the values to and from the Mockup Scheduler.
   • main.py: This file contains the main Flask app for the project.
   • container_handling.py: This file contains the logic for handling the containers on the IAI cluster using Kubernetes.

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.