Emerging real-time applications in information technology, and operational technology enable new innovative concepts to design and operate cyber-physical systems. A promising approach, which has been discovered recently as key technology by several industries is the Digital Twin (DT) concept. A DT connects the virtual representation of a physical object, system or process by available information and sensor data streams, which allows to gather new information about the system it mirrors by applying analytic functions. Thereby the DT technology can help to fill sensor data gaps, e. g., to support anomaly detection, and to predict future operating conditions and system states. This paper discusses a dynamic power system DT as a cornerstone instance of a new generation of EMS, and a prospective new EMS architecture, to support the increasingly complex operation of electric power systems. Unlike in traditional offline power system models, the parameters are updated dynamically using measurement information from the supervisory control and data acquisition (SCADA) and a wide area monitoring system (WAMS) to tune the model. This allows to derive a highly accurate virtual representation of the mirrored physical objects. A simulation engine, the Digital Dynamic Mirror (DDM) is introduced, in order to be able to reproduce the state of a reference network in real-time. The validation of the approach is carried out by a case study. In a closed loop within EMS applications, the DDM can help to assess contingency mitigation strategies, thus it can support the decision-making process under variable system conditions. The next generation of control centre Energy Management System (EMS) can benefit from this development by augmentation of the dynamic observability, and the rise of operator situation awareness.

Power system operation is gaining complexity due to the changes imposed by the energy transition. Especially, the increased share of intermittent and decentralized renewable generation units in the energy mix, an increased uncertainty regarding the supply of energy, and the predominantly market-driven cross-region and cross-border transport of electricity impose new challenges on the operation of power systems in Europe. In particular, power system operators must facilitate higher utilization of the grid capacity and coordinate more with neighboring transmission system operators (TSOs) and distribution system operators (DSOs). To deal with these new challenges, there is a pressing need to improve the observability and controllability of key system parameters to safeguard the reliability of power systems. Furthermore, the aforementioned developments and challenges go hand in hand with the need to improve the system resilience from the cybersecurity and system stability points of view. In the future, these challenges cannot be met without innovation towards intelligent decision support systems and assistant functions, which allow a look ahead combined with fast response and proactive actions. Here, the rather novel digital twin (DT) concept in combination with data-driven (i.e., machine learning) applications can be purposefully applied.

Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.

The operation of power systems is becoming more intricate due to the energy transition’s effects. This includes a rising share of intermittent and decentralized renewable generation, increased uncertainty in energy supply, and market-driven cross-border electricity transportation in Europe. These changes necessitate improved observability and control of critical system parameters to ensure power system reliability. Additionally, the transition requires enhanced resilience against cyber threats and system stability issues. To address these challenges, innovative approaches like the Digital Twin (DT) concept and data-driven techniques are proposed. The Digital Twin involves virtual representations of systems that mirror physical conditions, connecting the physical and digital realms through sensor data streams. This concept holds potential for applications demanding better observability and predictive capabilities. As power systems evolve towards higher automation, the DT becomes integral to discussions on autonomous systems and advanced power grid operation. This paper introduces the concept of DT-centered Energy Management Systems (EMS) and highlights its advantages in enabling future power grid automation. To implement DT in EMS, high-fidelity analytical models are required for accurate representation of the physical system. Combining DT with analytics allows holistic understanding, enabling novel automation and control methods by linking the physical and digital realms. A Single Source of Truth (SSoT) is foundational for creating a DT of complex infrastructures. The SSoT integrates diverse data sources, eliminating translation issues between software tools and data silos. This consolidates grid data, enhancing efficiency, flexibility, and reliability. SSoT-based DT enables adaptability to changing conditions, supporting efficient market participation and network reliability. The proposed EMS architecture employs DT as the core component for monitoring and control. It combines predictive modeling, dynamic state estimation,, model validation as well as parameter tuning to enhance the model fidelity and simulation accuracy. The ained trust in the decisions derived from the model results facilitates the developement of advanced decision support and operator assistance systems. To address power system stability challenges, Dynamic Security Assessment (DSA) using a DT is introduced. DSA extends beyond traditional Static Security Assessment by considering transient dynamics. DT-based DSA involves real-time and offline components, continuously updating models and assessing grid security against congestions and contingencies. A trust model is introduced to assess the coherence and state of a DT. It encompasses facets like functional correctness, safety, security, reliability, credibility, and usability. By applying trust across subsystems and services, a holistic view of the digitalized energy system’s health and state is achieved. This paper presents the concept of applying the DT as the core instance in the next generation of EMS and discusses the advantages of this prospective novel EMS architecture. It describes the evolutionary development in the power system domain from basic Digital Twin applications to systems that enable automated power grid operation in future. In conclusion, the integration of Digital Twin concept into EMS offers novel opportunities for power system operation and automation. It enhances observability, predictive capabilities, and decision support, enabling resilient and efficient grid management in the context of the energy transition. The presented trust model ensures system reliability and facilitates informed decision-making.