Power system digital twins (PSDTs) are foreseen as an essential step toward future grids that will positively affect network monitoring, operation, and planning across the whole power industry. Existing frameworks for developing a DT are bounded to particular power system components, applications and/or users. This article proposes a modular framework for the implementation of PSDTs that is flexible, robust, and cost-effective. The modularity in design enables the expansion of the twin beyond power system components and facilitates the integration of multiple services and users, without affecting the functionality of existing modules. Each module can be independently built, modified, replaced, or exchanged with other modules, unlocking multiple, more advanced, dedicated, and multidomain applications for the operation and planning of power systems. The first step toward a PSDT is illustrated based on a real-time compatible model of the Australian National Electricity Market. The model can be seen as the core of one or more distinct modules in the DT, which can be adapted and scaled for particular applications. The EMT real-time compatible model is employed to demonstrate four potential services, including renewable energy integration and “what-if” scenario cases, which are expected applications in forward-looking PSDTs.

Australia is one of the leading countries in energy transition, and its largest power system is intended to securely operate with up to 75% of variable renewable generation by 2025. High-inertia synchronous condensers, battery energy storage systems, and grid-forming converters are some of the technologies supporting this transformation while facilitating the secure operation of the grid. Synchronous condensers have enabled 2500 MW of solar and wind generation in the state of South Australia, reaching minimum operational demands of ≈100 MW. Grid-scale battery energy storage systems have demonstrated not only market benefits by cutting costs to consumers but also essential grid services during contingencies. Fast frequency response, synthetic inertia, and high fault currents are some of the grid-supporting capabilities provided by new developments that strengthen the grid while facilitating the integration of new renewable energy hubs. This manuscript provides a comprehensive overview, based on the Australian experience, of how power systems are overcoming expected challenges while continuing to integrate secure, low cost, and clean energy.

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit ($\mu$PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of $\mu$PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive $\mu$PMU on a densely-noded distribution network.

Benchmark models provide a common platform that facilitates comparison of different methods and strategies that can be further validated in a real representation of the power system. Nevertheless, when using RMS test cases, transients present in modern power systems may be neglected. Modern energy conversion systems require an enhanced level of detail in order to properly represent their behavior. Moreover, due to the steady increase of non-synchronous generation, new operation and management strategies need to be tested to ensure power system security. One way to overcome these constraints is to use EMT real-time digital simulations which allows to obtain high precision results in a timeframe suited to a specific study. This work presents an EMT real-time model for integration studies of advanced energy conversion systems that can be flexibly expanded to higher renewable penetration. The model, based on the Simplified Australian 14-Generator test system, has been expanded to represent in an improved way the dynamics of power system components as well as the high penetration of non-synchronous power electronics generation. Transient simulations are undertaken to illustrate system's response due to non-synchronous generation and when faults are applied. This work also addresses the current gap in benchmark models for real-time simulations.