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https://github.com/datejada/generation-expansion-planning-models-jump

Generation Expansion Planning Models in Julia/JuMP

https://github.com/datejada/generation-expansion-planning-models-jump

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Generation Expansion Planning Models in Julia/JuMP

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  • Host: GitHub
  • Owner: datejada
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 2.95 MB
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Created over 2 years ago · Last pushed almost 2 years ago
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Readme License

README.md

Generation Expansion Planning (GEP) models considering uncertainties on renewable energy resources (RES) using Julia/JuMP

Binder

The following files solve the GEP problem for three scenarios of wind and solar production using different approaches:

  • Stochastic-GEP-two-stage-nb.ipynb
  • Stochastic-GEP-two-stage-explicit-nb.ipynb
  • Stochastic-GEP-two-stage-Benders-nb.ipynb
  • Stochastic-GEP-two-stage-Benders-multicut-nb.ipynb
  • Stochastic-GEP-two-stage-LR-nb.ipynb
  • Stochastic-GEP-multi-stage-nb.ipynb
  • Static-robust-optimization-GEP-nb.ipynb
  • Adaptive-robust-optimization-GEP-nb.ipynb

These examples show basic concepts for learning optimization under uncertainty in power systems.

The models are developed in Julia, using the package JuMP, and solved using HiGHS.

The main references to model the optimization problems are:

[1] Optimization Techniques by Andrs Ramos Galn

[2] A. J. Conejo, L. Baringo, S. J. Kazempour and A. S. Siddiqui, Investment in Electricity Generation and Transmission, Cham, Zug, Switzerland:Springer, 2016.

[3] Sun X.A., Conejo A.J. (2021) Static Robust Optimization. In: Robust Optimization in Electric Energy Systems. International Series in Operations Research & Management Science, vol 313. Springer, Cham.

Owner

  • Name: Diego Alejandro Tejada Arango
  • Login: datejada
  • Kind: user
  • Location: Amsterdam
  • Company: TNO