First Official Version for Thermal_Generator
Thermal_Generator is a package specifically designed to simulate the operational plan (dispatch profile) for thermal generators (most notably coal-fired, gas-fired and combined cycle gas turbine generators)
participating in the electricity day-ahead market. A mixed integer linear optimization model will be generated based on the given prices and configuration for the
generator within the specified time horizon. The model is then solved using glpk solver and then the operational and economic metrics will be calculated
based on the obtained solutions.
The package also provides the function EPEX_Scrapping for scrapping the electricity day-ahead market price published on
EPEX SPOT Aunction Data for three different regions : France, Germany and Czech Republic.
This project is created based on the need for a realistic and robust tool for feasibility analyses of energy portfolios involving coal-fired and gas-fired power generators participating in day-ahead markets. These analyses are a crucial part of the energy project management coursework in the Master Program - Energy Environment: Science Technology and Management (STEEM) at Ecole Polytechnique.
We would like to thank our fellow friends at Ecole Polytechnique who contributed passionatelly & significantly to the project, especially with the case study : Mr. Muhammad Zacky Asyari, Ms. Maria Luisa Scarano Pereir, Mr. Lugas Raka Adrianto, Ms. Benyakhlef Sara.
We are currently considering the possibility of incorporating a storage system into this project in order to widen the applicable types of generators (hydroelectric generator or a renewable system with a battery system). Any contribution and feedback about this possibility as well as the current project will be greatly appreciated.
This project relies on : the Python package for optimization problem optlang for the model formulation,
the interactive visualization library Bokeh and the standard Matplotlib and seaborn visualization packages.
The EPEX_Scrapping function also requires requests and BeautifulSoup which are already installed in the standard Anaconda Distribution.
We suggest you to read through the Model and Syntax notebook first to apprehend the basic assumptions of the model. Then the Tutorials notebook briefly demonstrates the procedure for working with Thermal_Generator. Finally, a case study is provided in Case Study - Carbon Price Mechanisms notebook, which will apply Thermal_Generator to investigate the carbon price mechanisms on generators running on coal and gas in two different market : Germany and the United Kingdom.