Robust optimization for energy-aware cryptocurrency farm location with renewable energy. (March 2023) - Lotfi, Reza; Ghaboulian Zare, Sara; Gharehbaghi, Alireza; Nazari, Sima; Weber, Gerhard-Wilhelm
Highlights: An energy-aware cryptocurrency farm location problem is proposed. Supplying energy with renewable energy is suggested for cryptocurrency farms. A new robust stochastic optimization is proposed for this problem. The profit of robust stochastic is less than the stochastic form. Sensitivity analyses are used to evaluate the performance of the robust form. Abstract: 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.