Mapping the Scientific Interest in Integrating Digital Twin Technology into Renewable Energy Systems: Efficiency-Oriented Trends, Evidence-Based Gaps and Strategic Directions

Digital twin (DT) technology is increasingly mobilized to improve the efficiency and operational performance of renewable energy (RE) systems. This study maps the efficiency-oriented segment of DT research in RE through a bibliometric analysis of 383 documents indexed in Web of Science and Scopus (2018–September 2026), obtained after screening out records in which the acronym DT denotes another concept, and compares the results with two earlier versions of the corpus. Research gaps are derived through a three-level triangulation of keyword prevalence, co-occurrence cluster composition, and position on the strategic diagram; they are tested across keyword thresholds, clustering algorithms, corpus subsets, and 246 runs of the strategic diagram, and are interpreted as gaps in salience within the analyzed corpus rather than as proof of absence from the wider literature. Annual output roughly doubled each year from 2021 to 2025 under every growth estimator, with China being the leading contributor. Machine learning and energy management form the most developed themes, whereas economic appraisal remains marginal: no economic term reaches the keyword core, no theme is organized around an economic construct in any run, and the two economic magnitudes reported by the most cited documents never set the cost of the twin against its benefit. Three gaps are retained—the absence of standardized appraisal frameworks for the twin itself, the weak consolidation of interoperability research, and the scarcity of work at the integrated, multi-energy scale—together with the peripheral coverage of hydropower and retrofit. Four stakeholder-specific recommendations follow, each linked to its evidence and to an existing practical precedent.