Robust optimization for energy-aware cryptocurrency farm location with renewable energy

Date

2023-03-01

Author

Lotfi, Reza
Ghaboulian Zare, Sara
Gharehbaghi, Alireza
Nazari, Sima
Weber, Gerhard Wilhelm

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The cryptocurrency industry has changed the human life and accelerated financial exchange in this decade. Many investors want to invest in this industry and establish cryptocurrency farms. This research sets up a CryptoCurrency Farm Location (CCFL) as a facility location that supplies energy by Renewable Energy (RE). We presented a new robust optimization with the stochastic approach for tackling uncertainty in CCFL and compared it with stochastic CCFL. Our objective function includes maximizing the mean and minimum profits coefficient under different scenarios by adding an energy-aware constraint. Our model enables us to select supply energy by RE or power network country. The results show that the profit of robust stochastic CCFL is 4.94% less than stochastic CCFL because the proposed model is more conservative. Also, by increasing the conservativity coefficient to 50%, the total profit decreases to 4.94%. In addition, the discount rate grew to 10%, the profit down to 12.31%, and the problem increased profit by increasing the scale. Finally, the probability of scenario is changed and affects to profit function.

Subject Keywords

Cryptocurrency farm location, Energy-aware, Facility location, Renewable energy, Robust Optimization

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147195465&origin=inward
https://hdl.handle.net/11511/102184

Journal

Computers and Industrial Engineering

DOI

https://doi.org/10.1016/j.cie.2023.109009

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

R. Lotfi, S. Ghaboulian Zare, A. Gharehbaghi, S. Nazari, and G. W. Weber, “Robust optimization for energy-aware cryptocurrency farm location with renewable energy,” Computers and Industrial Engineering, vol. 177, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147195465&origin=inward.