Enhanced RMS (e-RMS) models for stability assurance

Project summary

Threats of instabilities posed by high fractions of inverter-based resources (IBRs) force the system operators to operate conservatively by curtailing wind or limiting interconnector flows, for example.

Name Status Project reference number Start date Proposed End date
Enhanced RMS (e-RMS) models for stability assurance Live NIA2_NGESO050 Oct 2023 Jul 2025

 

Strategy theme Funding mechanism Technology Expenditure
Net zero and the energy system transition NIA_RIIO-2 Modelling £400,000
Summary

Threats of instabilities posed by high fractions of inverter-based resources (IBRs) force the system operators to operate conservatively by curtailing wind or limiting interconnector flows, for example. System studies with existing RMS models (e.g., ‘GB master’) or EMT simulation can’t necessarily foresee or replicate such stability problems. The aim of this project is to develop an enhanced RMS (e-RMS) modelling framework that can provide dynamic stability assurance in planning studies and at operation timescale without carrying the cost of being overly conservative. This would be achieved by an e-RMS model of IBRs as a digital twin with modelling adequacy of both IBRs and the network in the sub-synchronous frequency range. The e-RMS model will provide early warning of any incipient instability and identify its root cause allowing targeted intervention and effective mitigation. 

Benefits

Currently, existing simulation models struggle to anticipate instability issues caused by inverter-based resources (IBRs), leading operators to limit renewable generation from IBRs to ensure grid security. The project's development of an enhanced RMS (e-RMS) model for IBRs, functioning as digital twins of high-fidelity IBR models, addresses these challenges. It enables a more thorough analysis of IBR-dominated systems, allowing for greater renewable integration without compromising grid stability. The e-RMS model facilitates advanced stability studies, real-time applications, and root cause analysis, ultimately ensuring a reliable and affordable power supply during the transition to net zero emissions. 

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Learnings

Outcomes

The outcomes at this stage include: #

  • Developing a ‘GB-like’ test case for EMT benchmark 
  • EMT modelling of IBR-dominated power systems  
  • Studying IBR model parameterisation  
  • Modelling adequacy – role of network dynamics  
  • Studying the feasibility of the root cause using participation factor  

Lessons Learnt

The lessons learnt at this stage include: 

  • Choosing a suitable reduced model; 
  • Ensuring the availability of required data can reflect the real issues on the GB network.