With the rapid growth of inverter-based resource (IBR) and increasing power-electronics-driven system dynamics, EMT-level detail is increasingly required for dynamic studies involving connection applications, wide-area disturbances, and operational decision support. However, traditional electromagnetic transient (EMT) simulations are computationally intensive, which impose constraints on model fidelity and the number of scenarios that can be simulated. This project will advance and validate a GPU-accelerated EMT simulation capability, enabling significantly faster and more scalable EMT studies while maintaining high numerical fidelity. This will provide a significant increase in EMT simulation speed, allowing more complex simulations and more scenarios to be performed, ultimately leading to more informed, less conservative reinforcement decisions.
Benefits
The project seeks to alleviate the computational bottleneck that limits the ability to study and understand increasingly complex power-electronics-dominated networks. By enabling faster and more scalable EMT simulations, transmission owners will be able to make more informed and less conservative investment and connection decisions. This has the potential to reduce unnecessary reinforcement, accelerate the integration of low-carbon technologies, improve confidence in system stability assessments, and support the secure and efficient operation of the future GB electricity transmission network.
As the volume of inverter-based resources connected to the transmission system continues to increase, the requirement for EMT studies is expected to grow significantly. By improving the scalability of EMT simulations, the project could help transmission owners and system operators manage increasing study workloads without requiring proportionate increases in engineering effort or computing resources. This could improve the efficiency of study processes and reduce barriers to adopting higher-fidelity simulation techniques.
The project may also support the acceleration of network connections by reducing the time required to undertake detailed technical assessments. Where EMT studies form part of the evidence base for connection applications or network development activities, faster simulation capability could shorten assessment cycles and enable study results to be generated more quickly, supporting more timely decision-making across connection and network planning processes.
The ability to perform larger and more detailed EMT studies could improve understanding of emerging power-system phenomena associated with converter-dominated networks. This includes control interactions, harmonic behaviour, fault response, system-strength-related phenomena, and interactions between multiple inverter-based resources. Improved understanding of these behaviours can support the development of more robust technical requirements, operational practices and future network standards.
The project could also increase the economic value derived from existing EMT models. Considerable industry effort has been invested in the development of detailed EMT models for HVDC schemes, FACTS devices, renewable generation and other network assets. A high-performance simulation platform could allow these models to be used more extensively and across a wider range of studies, increasing the benefit obtained from existing modelling investments.
In the longer term, successful validation of GPU-accelerated EMT simulation may enable the practical consideration of larger network representations and more extensive future-energy-system scenarios than are routinely possible today. This could support evidence-based decisions associated with network reinforcement, asset replacement, system operability and net-zero transition planning, helping ensure that future investments are appropriately targeted and proportionate.
From a consumer perspective, the project has the potential to contribute to lower whole-system costs by enabling more informed planning decisions and reducing unnecessary conservatism in network design studies. Better-targeted investment decisions can help minimise avoidable expenditure while maintaining the levels of reliability, resilience and system security required to support consumers and the wider economy.
