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

The tool was created by Volkan Kumtepeli 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 9.03
• YALMIP R20200116
• MATLAB >=2017a for string operations and >=2019a for readmatrix function.
• Robust Statistical Toolbox (not used but may be necessary for some functions in RainCloudPlots library)
• Partially provided external libraries:
  • RainCloudPlots
  • cbrewer
  • Custom Colormap
  • MATLAB-Dataspace-to-Figure-Units
  • Tight Subplot

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.