The transition from fossil fuels to "green" energy involves increasing energy efficiency from existing energy systems and reducing harmful emissions into the atmosphere. Currently, renewable energy sources (RESs) are a priority way of generating energy for Smart Grid systems that meet the requirements of efficiency and safety. Classification of regions by similar climatic characteristics helps as an effective tool for avoiding various risks when implementing a Smart Grid based on RES. In this paper, the clustering method is considered by the authors as a tool to achieve the goal of the study: the division of regions into clusters with similar climatic characteristics, which will allow to choose the most efficient RES for each specific region. The authors considered the selected climatic characteristics based on certain factors after a review of existing sources, for example, the level of solar insolation, average annual wind speed, average annual temperature, and average annual precipitation for 84 subjects of the Russian Federation. As a result, five clusters were identified by the k-means method using the Stata software. For each cluster, the characteristics and the most preferred type of RES for the implementation of Smart Grid are described. Cluster analysis based on climatic characteristics is the first stage of a comprehensive methodology for selecting the most favorable regions for the development of both a particular type of RES and Smart Grid systems in general, as proposed by the authors.
In recent decades, there has been a positive trend in world politics in the field of promoting territories’ sustainable development. At the same time, one of the most relevant areas is to promote the transition to renewable energy sources (RES), which correspond to one of the UN’s goals—Sustainable Development Goal 7 (SDG 7) “Ensuring universal access to affordable, reliable, sustainable and modern energy sources for all”. This article is devoted to the study of the renewable energy sources’ impact on the sustainable development of the Russian Arctic zone. The authors chose the level of carbon dioxide (CO2) emissions as an indicator reflecting the impact of RES on sustainable development, since this factor is one of the main factors for assessing trends in the activities of countries aimed at achieving progress on most of the Sustainable Development Goals of territories. The hypothesis of the relationship between the use of renewable energy sources and the achievement of progress on the Sustainable Development Goals, one of the indicators of which is the level of CO2 emissions, was tested and confirmed. An econometric analysis of panel data for 15 countries that are actively implementing the concept of sustainable development, including decarbonizing policies, was carried out ,where the resulting indicator for achieving progress on the SDG was the amount of CO2 emissions. The factors influencing the resulting variable were indicators selected based on a review of existing models, as well as indicators of the Sustainable Development Goals’ achievement. Using an econometric analysis of interdependence, the indicators of progress towards the Sustainable Development Goals that are more likely to have an impact on the level of CO2 emissions were identified. These are electricity consumption, the share of renewable energy sources in the energy balance, the average per capita income of the population, and carbon intensity. Based on the results obtained, it can be concluded that renewable energy sources are a factor contributing to the achievement of progress on the Sustainable Development Goals. The results obtained are also applicable to the Arctic region, since all countries that have territories in the Arctic zone adhere to the policy of decarbonization and try to reduce the use of fossil fuels.
This article reveals the problem of implementing and evaluating the readiness of the energy industry to implement digital innovations in the countries of the world. Qualitative and quantitative methods were used to achieve the purpose of this study, namely, the indicator system development to evaluate the level of readiness of certain countries for the potential digitalization of the energy industry. System, comparative and content analysis are the qualitative methods used in this work. The quantitative methods include collecting and processing statistical information, and fuzzy logic. As a result of the study, a list of indicators for monitoring was determined, and based on them, a scale for evaluating readiness of the energy industry for digitalization in the countries under consideration was formed. Based on the formed pool of indicators, a quantitative evaluation of the level of readiness of the electric power industry in 10 countries was carried out and presented as an aggregated indicator of the overall evaluation for each of the studied countries -Japan,
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