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

Major electric power companies around the world utilize mathematical optimization to manage the flow of energy across their electrical grids. In this example, you’ll discover the power of mathematical optimization in addressing a common energy industry problem: electrical power generation. We’ll show you how to figure out the 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.

This example is at the intermediate level, where 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 link below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.

 

Electrical Power Generation I

 

How to Run the Jupyter Notebook Modeling Example

-To run the example the first time, choose “Runtime” and then click “Run all”.

-All the cells in the Jupyter Notebook will be executed.

-The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.

-You can also modify and re-run individual cells.

-For subsequent runs, choose “Runtime” and click “on “Restart and run all”.

-The Gurobi Optimizer will find the optimal solution of the modeling example.

Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.


 

Electrical Power Generation II

This example (which is an extension of the earlier ‘Electrical Power Generation 1 ‘ example) will teach you how to choose an optimal set of power stations to turn on in order to satisfy anticipated power demand over a 24-hour time horizon – but gives you the option of using hydroelectric power plants to satisfy that 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 at the intermediate level, where 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 link below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.

 

Electrical Power Generation II

 


 

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