Optimizing energy consumption for blockchain adoption through renewable energy sources

The adoption of blockchain technology across various industries and systems has garnered significant attention due to its myriad benefits, leading to widespread popularity today. However, the energy-intensive nature of blockchain, attributed to extensive computations and data mining, poses substantial operational and environmental challenges, hindering its widespread acceptance. To mitigate these limitations, leveraging renewable energy sources emerges as a viable and crucial solution. These options are assessed across various dimensions including sustainable energy transfer, physical attributes, legal regulations, energy supply costs, technological infrastructure, and climatic constraints. To achieve this, we present four optimization models. Initially, three optimization models, rooted in risk aversion, fairness, and weighted sum principles, are meticulously solved. Subsequently, leveraging the insights garnered from these models, a multi-objective optimization model is developed based on Percentage Multi-Choice Goal Programming (PMCGP) method. This framework facilitates the scoring and ranking of renewable energy sources, culminating in informed decision-making. Our investigation, anchored by a case study, underscores the significant potential of utilizing blockchain technology in conjunction with wind energy. In the initial step, our models grounded in risk, optimization, and fairness concepts establish targets for the subsequent stage. Consequently, the proposed methodology offers diverse analytical capabilities tailored for supply chain managers and decision-makers.