Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin’s development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented.

The Aitken Δ² method for finding fixed points of scalar mappings is interpreted as a modification of the Wegstein method. Based on this approach, conditions for the quadratic convergence of this method are obtained for various situations of convergence/divergence of simple iteration. An algorithm for calculating fixed points that keeps track of these situations is presented.

Modern artificial intelligence methods are increasingly used in power systems for renewable energy generation and electricity load forecasting. However, the limited interpretability of complex machine learning and deep learning models constrains their adoption in critical energy applications where transparency and trust are essential. Explainable Artificial Intelligence (XAI) provides tools for interpreting model behavior, yet its application to multivariate time series remains associated with significant methodological challenges. This paper presents a systematic review of XAI applications in solar power, wind power, and electricity load forecasting based on 154 peer-reviewed journal articles published between 2019 and 2026, identified through searches in Scopus, IEEE Xplore, ScienceDirect, and MDPI, following the PRISMA 2020 methodology. The review covers widely used forecasting architectures, including LSTMs, Transformers, and tree-based ensembles, as well as XAI methods. The analysis identifies a fundamental limitation of conventional XAI approaches for multivariate time series, referred to as the curse of dimensionality in XAI-based interpretation of time series, in which each time step is treated as an independent feature, resulting in explanations that are difficult to interpret in practice. To address this challenge, eight categories of XAI adaptations for time series forecasting are systematized. A classification of knowledge extraction mechanisms is proposed, including feature-level, temporal, regime-based, causal, diagnostic, model-level, and decision-support knowledge. The results demonstrate a gradual transition from explainability toward knowledge extraction, where XAI serves not only to explain individual forecasts but also to generate actionable knowledge about data, models, and energy processes. The review is limited to peer-reviewed English-language journal articles published between 2019 and 2026. The findings suggest that Knowledge Extraction represents a key mechanism for building trust in intelligent energy forecasting systems.

The article is dedicated to the assessment of the correlation between the values of self-expression and survival among modern Russian students. The relevance of this topic is determined by the need to understand how to change the higher education system so that it correlates with the values of modern students and applicants, meets their expectations and needs. The article examines two aspects of the transformation of higher education: the institutional limitations of the modernisation of higher education and the value orientations of modern Russian students. Value orientations were operationalised using the approach of R. Inglehart and K. Welzel, based on the dichotomy of values of self-expression and survival, and institutional conditions - through the reproduction by the higher education system of the institutional trap “path-dependence”, to overcome which "critical mass" of subjects with dominant values of self-expression is required. The main research question was whether Russian bachelors have developed values of self-expression. The empirical basis of the study was the data of a mass survey of Russian students of 19 universities within the framework of the project "The Birth of a Russian Master's Degree" with the support of the Vladimir Potanin Charitable Foundation. The research toolkit included a number of categories reflecting the meanings that students use when analysing the reasons for choosing a university, the direction of training and learning outcomes. One of these categories is the category of choice (of the university and field of study). The data obtained indicate that when answering the question about the reasons for choosing a university and the direction of study, there are, on average, two and a half times more students who chose the values of self-expression than those who are clearly focused on the values of survival. This suggests that, firstly, the importance of the values of emancipation (freedom of choice and development) in higher education is growing. Secondly, the potential for changes at the value level is accumulated in groups of students with different cultural capital - that is, we can talk about universal tendencies among the entire studied group. Thirdly, the choice of the direction of study turned out to be a more significant factor in the distribution of values than the choice of a university, that is, the motives for choosing a student are biased towards the content of their professional education in the first place, and the prestige of the university in the second place. Based on their analysis of the collected data, the authors come to the conclusion that modern Russian students are ready for meaningful changes in higher education. The only question is what answer to expect from the system.