Electrical Power Generation I and II

Electrical power generation problem we select an optimal set of power stations to turn on to satisfy power demand

 

Electrical Power Generation I

This model is an example of an electrical power generation problem (also known as a unit commitment problem). It selects an optimal set of power stations to turn on in order to satisfy anticipated power demand over a 24-hour time horizon.

This model is example 15 from the fifth edition of Model Building in Mathematical Programming, by H. Paul Williams on pages 270 – 271 and 325 – 326.

 

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Modeling examples are coded using the Gurobi Python API in Jupyter Notebook. In order to use the Jupyter Notebooks, you must have a Gurobi License. If you do not have a license, you can request an Evaluation License as a Commercial User or download a free license as an Academic User.

 

Commercial Users: Free Evaluation Version Academic Users: Free Academic Version

 

Access the Jupyter Notebook Modeling Example

Click on the button below to be directed to GitHub where you can download the repository for the Electrical Power Generation I Jupyter Notebook modeling example.

 

Electrical Power Generation I

 


 

Electrical Power Generation II

This model is an extension of an earlier ‘Electrical Power 1 ‘ example (example 15 from HP Williams); the extension adds the option of using hydroelectric power plants to satisfy demand. This model is example 16 from the fifth edition of Model Building in Mathematical Programming, by H. Paul Williams on pages 271-272 and 326-327.

This example is of intermediate difficulty; we assume that you know Python and the Gurobi Python API and that you have some knowledge of building mathematical optimization models.

 

Access the Jupyter Notebook Modeling Example

Click on the button below to be directed to GitHub where you can download the repository for the Electrical Power Generation II Jupyter Notebook modeling example.

 

Electrical Power Generation II

 


 

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