Digital twins (DTs) are increasingly being considered as enabling technologies for advanced condition diagnosis and prognosis in power and energy systems. By combining physical models, operational data, and synchronization with physical assets, DTs can potentially support physically consistent fault diagnosis, degradation assessment, and remaining useful life (RUL) estimation in electrical machines, power electronic systems, renewable energy plants, energy storage systems, and power networks. However, the engineering maturity of DTs for these capabilities remains heterogeneous, and some reported implementations might be more accurately interpreted as monitoring-oriented digital shadows, periodically updated models, or application-specific analytical tools. This perspective critically examines the readiness of DTs specifically for condition diagnosis and prognosis in power and energy systems. Rather than providing another systematic review of DT technologies, it evaluates the DT layer itself against the functional requirements imposed by diagnosis and prognosis. Particular attention is given to model adequacy, synchronization and state updating, degradation representation, uncertainty handling, validation credibility, and lifecycle integration. An application-driven distinction among digital models, digital shadows, and DTs is established, followed by a readiness framework that relates these representations to diagnosis- and prognosis-oriented decision support. Representative power and energy applications are then examined to identify the gap between demonstrated capability and engineering-ready deployment. The resulting perspective highlights the validation evidence and research advances required to move DT-based diagnosis and prognosis from research demonstrations toward validated and scalable engineering deployment.
The Industrial Internet of Things (IIoT) has driven the automation-centered vision of Industry 4.0 (I4.0). As industries move toward the emerging goals of Industry 5.0 (I5.0), IIoT must evolve to support human-machine collaboration, sustainability, resilience, and adaptive intelligence. However, existing IIoT approaches remain largely focused on automation and do not fully reflect these broader values. This article introduces the concept of a Collaborative Industrial Internet of Things (CIIoT) as a forward-looking progression designed to address this gap. This concept brings together a set of coordinated mechanisms across system layers that enhance context awareness, support human-machine interaction, and improve industrial adaptability and resilience, without altering the fundamental structure of IIoT. The discussion highlights how advances in networking, distributed computing, and intelligent processing contribute to this progression and how these developments can operationalize the key I5.0-oriented principles across system layers. By connecting current technology trends with the emerging priorities of I5.0, this article provides a high-level perspective on the evolution from today's automation-centric IIoT toward more sustainable, adaptive, and collaborative industrial systems, positioning CIIoT as a practical pathway for implementing I5.0 values in real-world environments.
The rapid adoption of battery-electric vehicles is transforming electricity demand and introduces a growing pool of distributed, mobile energy storage into low-voltage power systems. While electric vehicles are increasingly discussed as flexibility providers through bidirectional charging, their effective integration into grid-responsive energy management systems is constrained by the stochastic nature of vehicle mobility and charging behavior. This article examines the role of predictive information by traffic cameras for exploiting predictive electric flexibility beyond static assumptions such as simultaneity factors. It proposes a conceptual framework that utilizes vehicle re-identification using existing infrastructure with probabilistic spatio-temporal route prediction to estimate future charging demand and storage availability at feeder and energy-community level. The article discusses technical prerequisites for vehicle-to-grid operation, including charging infrastructure, and communication standards, regulatory constraints, and highlights both the challenges in vehicle re-identification and limitations of current market and tariff designs. Finally, economic and computational considerations are addressed, illustrating how probabilistic electric vehicle availability information can improve grid operation, resource planning ability, reduce peak demand, and support cost-optimal scheduling in smart grids and energy communities.
The convergence of energy transmission and data communication has become a key feature of decentralized energy systems across a broad spectrum of voltage/power ranges, including smart grid applications and cyber-physical power systems to enable digital power. This article compares two distinct approaches—power line communications (PLC) and talkative power conversion (TPC)—and proposes a hybrid combination thereof to inherit their advantages and improve system performance. While PLC leverages existing power infrastructure for data transmission by using external data transmitters and receivers, TPC integrates communication capabilities directly into power electronic converters. We present the technical foundations and applications of PLC and TPC, benchmark their strengths and bottlenecks, and outline future research directions regarding TPC that could bridge the gap between power and communication technologies. The benefits of hybrid PLC-TPC are identified, and coexistence is verified experimentally.
Creating a sustainable future through the development of environmentally friendly products and industry is just one of the sustainable development goals (SDGs) where power electronics (PEs) will play a major role. Moreover, biomedical applications, economic growth, and efficient production are among the areas directly impacted by PEs developments. This article presents how industrial electronics (IEs) and, particularly, PEs are shaping multiple activity sectors, helping them to accomplish the SDGs defined by the United Nations.
