Energy-Alternatives-Planing
This project contains code and data to model the electric system. It relies mainly on a combined use of two well known python packages (panda and pyomo) for linear programming adapted for the specific use of energy system modeling.
The installation relies on the use of a conda environment. Instruction is below #TODO : add links to illustrative exemples here.
Table of content
- 1. Installation
- 2. Models Folder
- 3. Functions Folder
- 4. Pycharm tips
- 5. Getting help
- 6. Getting involved
1 - Installations
Once you have downloaded the Energy-Alternatives-Planing folder, in the terminal, you need to create the conda environment thanks to the conda.yml file:
conda env create --file conda.yml
conda activate energyalternatives
2- Models Folder
Contains folders with Models. Each folder in Models Folder contains a set of Models and associated data and case studies. See the corresponding README. You can add your own Models folder to contribute, or by creating a case study in an existing Models Folder.
If you want to learn how to use the energy system planing tool, you can jump directly into the one node operation example or mode generally into the one node model Basic_France_models or the multinode Basic_France_Germany_models. There, you will find tutorials on how to use the simulation/modelisation tool, how to use pyomo and you will find a mathematical description of the models.
If you want to learn how to use our simple consumption prospective models for France, you can jump directly in Prospective_conso.
If you want to use a more advanced case with 7 nodes in Europe to model the future european electric system, you can jump directly in Seven_node_Europe.
list of available web page with examples/documentation : one node operation optim one node planification optim [two node operation optim] [two node planification optim] 7 node EU model Vs historical data 7 node EU model for 2030 time series of consumption model time series of availability/production residential heating consumption evolution transport consumption evolution industry consumption evolution
3- functions folder
Contains:
- tools that can be used to facilitate the interface between pyomo optimisation models results and parameters and panda.
- a set of predefined planing and operation constraints to model the electric system,
- demand modeling tools in (f_consumptionModels.py)
- graphical tools.
- tools that are under developpement for a more "automatic" construction of models : f_model_cost_functions.py and f_model_definition.py
4 Pycharm tips
If you're using PyCharm you should fix the environement in settings by choosing the right "python interpreter"
I strongly recommend to use the keyboard shortcut "crtl+enter" for action "Execute selection". This can be set in PyCharm Settings -> keymap This project also contains Jupyter Notebook.
5 Getting help
If you have questions, concerns, bug reports, etc, please file an issue in this repository's Issue Tracker.
6 Getting involved
BuildingModel is looking for users to provide feedback and bug reports on the initial set of functionalities as well as developers to contribute to the next versions, with a focus on validation of models, cooling need simulation, adaptation to other countries' datasets and building usages.
Instructions on how to contribute are available at CONTRIBUTING.
Open source licensing info
Credits and references
Energy-Alternatives-Planing models are directly derived from work performes for several courses given at MINES ParisTech by Robin Girard, and by students.
Main contributors :
- Robin Girard -- blog gitHub LinkedIn google Scholar
- Antoine Rogeau
- Quentin Raillard Cazanove
- Pierrick Dartois
- Anaelle Jodry
