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.

This paper proposes a general methodology for the development of power system models suitable for distributed real-time simulations (D-RTS) based on topology, simulator interfaces and data exchange. D-RTS have risen as functional alternatives that can combine remote and multi-vendor resources for large-scale power system simulations via a virtual connection. However, previous work focused on combination of separate models and not the performance of D-RTS when splitting a single monolithic model, failing to study the behaviors of D-RTS, including, synchronization and accuracy. The proposed methodology is used to develop distributed models of two widely used testbed large power systems, the IEEE Australian Benchmark model and the IEEE 300-Bus system. These testbeds are selected as they can be simulated as both monolithic and distributed models in available simulators in order to validate both the methodology and resulting model performance. The obtained results and comparison between monolithic and distributed models support the proposed approach and demonstrate the performance of D-RTS under both steady-state and transient operations in multiple scenarios.

Artificial Intelligence (AI) promises solutions to the challenges raised by the digitalization of power grids and their assets. Decision-making, forecasting and even operational optimization of grids and assets are just some of the solutions that AI algorithms can provide to operators, utilities and vendors. Nevertheless, barriers such as access to quality datasets, interpretability, repeatability, and availability of computational resources currently limit the extent of practical AI implementations. At the same time, Digital Twins (DTs) are foreseen as platforms that can overcome these barriers, and also provide a new environment for the development of enhanced and more intelligent applications. In this manuscript, we review the published literature to determine the existing capabilities and implementation challenges of AI algorithms in power systems, and classify AI-based applications based on their time scale to reveal their temporal sensitivity. By combining AI and DT, we outline multiple prospective use cases for AI-enhanced power grid and power asset DTs. Our review also identifies that the combination of AI-based solutions and DTs leverages new applications with the potential to fundamentally change multiple aspects of the power industry.