Electrical Power Generation I and II

Model: Part 1

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.

Model: Part 2

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.