Expert Systems in the Energy Transition as a Tool for Intelligent Support of Decarbonization and Sustainable Development

The article explores the evolution of research on the energy transition through a biblio-metric co-occurrence analysis of author keywords extracted from 146 scientific publica-tions. Rather than analysing publication content directly, the study examines the rela-tionships among keywords related to the energy transition and intelligent deci-sion-support technologies. Recent research (2022-2024) increasingly focuses on renewable energy, sustainable development, and intelligent systems, which reflects the shift towards the digitalization of energy systems and the growing importance of sustainable develop-ment. Between 2018 and 2021, research shifted toward a broader understanding of the en-ergy transition, emphasizing climate change, decarbonization, renewable energy, invest-ments, and energy policy within the context of sustainable development. In contrast, pre-vious studies (2014-2018) have mainly focused on the technical aspects of energy systems and traditional decision-support approaches. The study focuses on demonstrating the outcomes of such evolution and considers the decision-making process for building an methodological concept integrated with expert systems to optimize and manage car-bon-reduction strategies under dynamic energy transition conditions. In this study, the concept of a DT is considered as a methodological direction for extending the capabilities of modern expert systems rather than as a fully implemented digital twin. Accordingly, an algorithmic model of an expert system is proposed, in which the analytical core is based on engineering (η), economic (F), and optimization models that are consistent with the digital twin concept. Particular emphasis is placed on the economic module, which ena-bles the assessment of the economic value of CO₂ capture, break-even carbon price, mini-mum functional point, optimal operating conditions, and the sensitivity of results to key economic parameters. The proposed approach contributes to the development of intelli-gent tools to support decarbonization and the transition to sustainable energy supply.