https://github.com/ElektrikAkar/3d_milp

Public repository for 3D-MILP paper.
https://github.com/ElektrikAkar/3d_milp

Last synced: 3 months ago
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Public repository for 3D-MILP paper.

• Host: GitHub
• URL: https://github.com/ElektrikAkar/3d_milp
• Owner: ElektrikAkar
• License: other
• Created: 2020-11-06T13:20:26.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-07-22T01:16:21.000Z (over 2 years ago)
• Last Synced: 2024-06-11T19:04:04.323Z (5 months ago)
• Language: MATLAB
• Size: 2.08 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• open-sustainable-technology - 3d_milp - Energy Arbitrage Optimization With Battery Storage. (Energy Storage / Battery)

README

        


  



# Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models

The tool was created by [Volkan Kumtepeli](https://scholar.google.com/citations?user=Z43mRIsAAAAJ&hl=en) at the Energy Research Institute at Nanyang Technological University

in collaboration with Institute for Electrical Energy Storage Technology at the Technical University of Munich.

## How to cite: 

V. Kumtepeli, HC. Hesse, M. Schimpe, A. Tripathi, Y. Wang, and A. Jossen,

Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models.

IEEE Access, vol. 8, pp. 204325-204341, 2020. [Online]. Available:

https://doi.org/10.1109/ACCESS.2020.3035504

```

@article{kumtepeli2020energy,

  title={Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models},

  author={Kumtepeli, Volkan and Hesse, Holger C and Schimpe, Michael and Tripathi, Anshuman and Youyi, Wang and Jossen, Andreas},

  journal={IEEE Access},

  volume={8},

  pages={204325--204341},

  year={2020},

  publisher={IEEE}

  doi={10.1109/ACCESS.2020.3035504},

  ISSN={2169-3536},

}

```

## Dependencies / Requirements: 

* [Gurobi](https://www.gurobi.com/) 9.03

* [YALMIP](https://yalmip.github.io/download/) R20200116

* MATLAB >=2017a for string operations and >=2019a for readmatrix function.

* [Robust Statistical Toolbox](https://github.com/CPernet/Robust_Statistical_Toolbox) (not used but may be necessary for some functions in RainCloudPlots library) 

* Partially provided external libraries: 

  - [RainCloudPlots](https://github.com/RainCloudPlots/RainCloudPlots)

  - [cbrewer](https://www.mathworks.com/matlabcentral/fileexchange/34087-cbrewer-colorbrewer-schemes-for-matlab)

  - [Custom Colormap](https://www.mathworks.com/matlabcentral/fileexchange/69470-custom-colormap)

  - [MATLAB-Dataspace-to-Figure-Units](https://github.com/michellehirsch/MATLAB-Dataspace-to-Figure-Units)

  - [Tight Subplot](https://www.mathworks.com/matlabcentral/fileexchange/27991-tight_subplot-nh-nw-gap-marg_h-marg_w)

## How to use: 

Run Optimization_single.m or Optimization_batch.m file. Default settings are given in simulationSettings which can be called with additional settings.