The increase in inverter-based resources (IBRs) introduces new challenges for electromagnetic transient (EMT) modelling and stability analysis. The traditional short-circuit strength based Thevenin equivalents are no longer sufficient, which fail to capture the frequency-dependent impedance characteristics of the network.
The proposed approach fills this modelling gap by a Frequency-Dependent Network Equivalent (FDNE) for the transmission network. The FDNE is a data-driven model that accurately reproduces the network’s voltage-current behaviour across a broad range of frequencies. The aim of this 18-month project will be implemented via five work packages.
The FDNE solution directly supports NESO’s objectives of improving system operability and resilience in a high-IBR future. It offers a fast, accurate, and flexible simulation toolset that streamlines compliance processes and empowers users, all while maintaining confidentiality.
Benefits
1. What is the expected benefit delivered directly by THIS PROJECT?
1a.Short outcome statement (clear and specific)
Delivery of a validated Frequency Dependent Network Equivalent (FDNE) modelling methodology and toolset that improves the accuracy and efficiency of EMT simulations for inverter-based resource (IBR) integration studies.
1b. Who will benefit from this project and in what way?
NESO system planning and operations teams
>More accurate and scalable EMT studies for stability assessments
>Reduced simulation complexity and runtime
Network owners and developers (TOs, DNOs, IBR developers)
>Improved confidence in connection studies and compliance assessments
>Faster study turnaround enabling timely project delivery
Wider industry (consultants, OEMs, academia)
>Access to standardised FDNE methodology and guidance, improving consistency across studies
1c. How confident are you this project will deliver this benefit?
High
Rationale:
Builds on established impedance-based modelling techniques already used in industry
Supported by prior internal work (e.g. SSO guidance development)
Validation will be performed against benchmark EMT models and real case studies
1d. Can this benefit be quantified? (For development and demonstration projects provide quantified estimates where reasonably possible. If quantification is not currently feasible, explain why and what evidence supports the expected benefit)
Yes - provide metric, baseline, expected improvement
Metric 1: Simulation performance
Baseline: Full EMT models with large external network representations (high computational cost)
Expected improvement:
~30–70% reduction in simulation time (depending on case size)
Reduced model size and improved numerical robustness
Metric 2: Study efficiency
Baseline: Manual model reduction and ad hoc equivalents
Expected improvement:
Standardised FDNE approach reducing engineering effort by ~20–40%
Metric 3: Model fidelity
Baseline: Reduced models with limited frequency-dependent accuracy
Expected improvement:
Improved alignment with full EMT response across wide frequency range (validated via error metrics such as impedance mismatch or response deviation)
Future / Full-Scale Benefit (if applicable - think beyond direct project outputs, e.g. operational rollout, industry adoption, policy influence, future cost reduction, resilience improvement)
Apply relevant category (Knowledge, Social, Environmental, Financial)
2. What is the expected benefit if this idea is implemented at full scale?
2a. High-level outcome statement
At full scale, adoption of FDNE modelling enables faster, more reliable system studies across Great Britain, supporting accelerated integration of renewable generation while maintaining system stability.
2b. Who will benefit from this project in the future and in what way?
Link benefit - stakeholder clearly
GB electricity consumers
Benefit from faster and more cost-efficient connection of low-carbon generation
NESO and network operators
Improved system resilience and planning capability
Reduced operational risk from stability issues (e.g. oscillations, interactions)
Renewable developers
Shorter connection timelines and clearer technical requirements
Policy makers and regulators
Evidence base for improved modelling standards and network codes
2c. What needs to happen for this future benefit to be realised?
e.g. rollout, policy change, integration, further development
Integration of FDNE methodology into:
>NESO study processes and tools (e.g. EMT workflows)
> Industry guidance (e.g. SSO guidance updates, modelling standards)
Training and knowledge transfer:
> Workshops, documentation, and SOP deployment
Alignment with software vendors:
> Implementation within major EMT tools (e.g. PSCAD, PowerFactory)
Wider industry adoption:
> Engagement through innovation dissemination (CIGRE, EPRI, etc.)
2d. How confident are you this project will deliver this benefit?
High
Rationale:
> Strong technical foundation and clear industry need
> Dependent on successful rollout, standardisation, and stakeholder adoption
2e. Can this benefit be quantified? (For development and demonstration projects provide quantified estimates where reasonably possible. If quantification is not currently feasible, explain why and what evidence supports the expected benefit)
Yes - provide metric, baseline, expected improvement
Metric 1: System study throughput
Baseline: Limited EMT study capacity due to computational constraints
Expected improvement:
Increase in number of studies deliverable within same timeframe (indicatively +30–50%)
Metric 2: Connection timeline impact
Baseline: Delays due to complex EMT studies
Expected improvement:
Reduction in study-related delays. At this stage, the benefit is qualitative only. The project will develop appropriate metrics to quantify this impact; however, measurable outcomes will not be available until post‑delivery and subsequent BAU rollout (subject to successful implementation and adoption).
Metric 3: Carbon impact (indirect)
Baseline: Slower integration of renewable generation
Expected improvement:
Earlier connection of low-carbon assets (benefit evidenced but not directly attributable at project stage)
Note:
Some benefits (e.g. system resilience, reduced oscillation risk) are difficult to quantify at this stage and will require post-deployment evidence.
