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

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

doi:10.1016/j.cie.2023.109009

Cited by 21 publications

(5 citation statements)

References 26 publications

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“…0.9) means a higher risk premium for future cash flows, i.e. investors or decision makers require higher returns to compensate for the higher risks they take [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Equal allocation, demand priority or negotiated allocation? How to allocate water resources in the Tigris and Euphrates River Basin efficiently

Bai,

Wang,

et al. 2024

Heliyon

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“…0.9) means a higher risk premium for future cash flows, i.e. investors or decision makers require higher returns to compensate for the higher risks they take [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Equal allocation, demand priority or negotiated allocation? How to allocate water resources in the Tigris and Euphrates River Basin efficiently

Bai,

Wang,

et al. 2024

Heliyon

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“…Due to the limitation of time and data, the study area of this paper only includes the south of the Yangtze River in China. Later, the investigation and calculation will be carried out in the north of the Yangtze River, so as to obtain the results of the proportion of people who rely on clean cooking fuel and utilization technology in the country, using relevant models to select energy supplied by renewable energy [43][44], thus providing a scientific data basis for the relevant policies and plans of China's carbon peaking and carbon neutrality goals [33]. The methodology presented in this paper can be applied to other areas, such as combining Earth Big Data with remote sensing imagery of nighttime lights.…”

Section: Discussionmentioning

confidence: 99%

Calculation of the Proportion of Population Using Clean Cooking Fuel and Technology Based on Earth Big Data: A Case Study in the South of the Yangtze River in China

Pan,

Yuan,

Guo

et al. 2023

IEEE Access

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Cooking is a very decentralized and private way of energy consumption in human activities. The existing investigation and statistical analysis can't effectively calculate the proportion of population relying on clean cooking fuel and utilization technology in the region. Therefore, based on the big data of the earth, this paper adopts the combination of spatial analysis and statistical analysis to determine the survey sample area, and according to the economic conditions, topographic characteristics, national policies for new energy construction and living habits of the provinces and cities under investigation, the questionnaire survey is conducted for the research area to calculate the proportion of the regional population relying on clean cooking fuels and utilization technologies. Taking the south of the Yangtze River in China as an example, the paper effectively calculates that 88.25% of the population depends on clean cooking fuel and technology in this region, of which 89.81% are in urban areas and 79.87% are in rural areas. Analysis of the survey data shows that the proportion of the population using clean cooking fuels and technologies is related to factors such as economic development, income and resource endowment. There is a large urban-rural gap in terms of energy consumption tendency and structure in the south of the Yangtze River in China and cooking energy consumption in rural households also varies from region to region. The technical ideas and conclusions of the paper have high reference application value, which can help promote the upgrading of clean energy utilization and provide data basis for relevant decision and policy making. INDEX TERMS Clean cooking fuel and utilization technology, population ratio, earth big data

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“…They utilized hybrid robust stochastic programming, worst case and conditional value at risk in order to cope with uncertainty and risks. Reza Lotfi et al (2023) suggested a robust, resilience MCDM with risk approach for renewable energy location. They proposed a novel risk approach by adding a risk function in MCDM.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting energy consumption through the LEAP model based on LMDI technique along with economic analysis: A case study

Abbas,

Waqas,

Lun

et al. 2023

Energy Exploration & Exploitation

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This research reports the implementation of logarithmic mean Divisia index (LMDI) and categorizes the growth of total energy usage in three different industrial sectors for the years of 2010 to 2021. Furthermore, it classifies and evaluates the factors influencing on energy consumption in Punjab province thru a sustainable way. The growth consumption is classified into scale influence, structure influence, and efficiency influence. Likewise, the long-term energy alternatives planning-Punjab model is executed with the energy consumption, scale impact, structure impact, and efficiency impact. Besides, comprehensive adjustment scenarios are also introduced to examine the impact of three different factors on overall energy usage. The results from the qualitative decomposition of LMDI indicate that the high scale can lead to high-energy consumption in Punjab Province. However, it can be reduced by high-efficiency reinforcement. The total energy consumption in 2024, 2036, and 2044 under reference scenario is 304.12, 460.01, and 590.04 million tons compared to structure influence analysis for slow terminology (SIAS) and comprehensive scenario. For that reason, it can predict and provide earlier energy management planning for the province. Conversely, the structure factor does not display obvious effect on the energy use. Equally, the quantitative results of the long-term energy alternative planning (LEAP) model are relatively consistent with those of LMDI model, whose advantageous impact on the structure influence is reasonably extrapolated. This phenomenon indicates that the structure influence and efficiency influence will maintain the disruptive impact on increasing overall energy use for the future perspectives. Consequently, the LEAP model predicts the energy consumption of Punjab Province among the years of 2020 to 2040 under medium-term development framework.

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

Cited by 21 publications

References 26 publications

Equal allocation, demand priority or negotiated allocation? How to allocate water resources in the Tigris and Euphrates River Basin efficiently

Equal allocation, demand priority or negotiated allocation? How to allocate water resources in the Tigris and Euphrates River Basin efficiently

Calculation of the Proportion of Population Using Clean Cooking Fuel and Technology Based on Earth Big Data: A Case Study in the South of the Yangtze River in China

Predicting energy consumption through the LEAP model based on LMDI technique along with economic analysis: A case study

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