Learnings

Outcomes

The supplier effort yielded several analysis products that are consequential for NGET’s effective management of risk:

• Analysis of the LJRP brought a mathematically rigorous assessment using a data-backed probabilistic risk analysis method. The PRA exercise delivers functional characterisation of the system by specifying the functions within the system, the relationships among the components within the system, and all possible failure modes and their probabilities.
• The PRA established a quantitative framework from which detailed economic analysis can be performed, ultimately which capacitates NGET (or any network operator) decision makers to make optimal management & operational decisions.
• The resultant model from the PRA is a quantitative representation (digital twin) of the LJRP that remains effective and applicable for future analyses, even to update the future risk analysis at low marginal cost.
• For the LJRP, a recommended & prioritised ranking was established, based on current configurations and asset models.
• Failure probabilities and re-energisation times of each LJRP option were presented.
• Expected annual risk costs for each LJRP option were given considering outage probability, respective probabilities of failure, and risk consequences.
• Risk posture improvements within the use case were noted together with recommendations regarding assets recapitalisation.
• The project discussed specific asset risk criticality as evaluated against general perceived risk intuitions.
• The insights from the PRA led to other general risk management recommendations, even risk management of restoration plans.

Net Benefit

The benefits of this project were premised on evaluating the exposure cost calculated from the probability of a total or partial shutdown taken from the National Risk Register and the value of lost load within the LJRP network. A conservative estimate that the effort i.e., migration from a limited subjective to holistic quantitative analysis would reduce LJRP implementation time by approximately 12.5% and overall exposure cost over a regulatory period (5 years) by at least approx. £3.41m was validated.

The same baseline PRA results from the project’s LJRP use case that prioritised restoration route assets by risk criticality can be used to determine assets replacements or optimise maintenance inspections. The PRA framework thus provides network managers with repeatable probabilistic methods to quantify expected benefits of such asset risk mitigation efforts to influence future LJRP revisions for NGET.

Recommendations for further work

It was observed that methods utilised in this project use case (LJRP) are broadly extensible to many other areas of NGET business areas, including demand at risk, resilience, business process optimisation, even cyber risk management, etc.

Some improvements to current asset models that were suggested within the project analysis regarding quantification of asset failure probabilities could be explored. The need to analyse the LJRP family as a whole and assess the impact to asset failure probabilities could be pursued together with assessing if quantitative methods could assist with decision-making in associated inspections and asset replacements.

Lessons Learnt

Year 2023/2024

The PRA effort has revealed where technology transition is possible, NGET or any network operator could develop capability to do PRA of all its assets and systems through advanced quantitative analysis methods for risk and resilience. PRA including advanced machine learning and artificial intelligence can serve as an initiative for improving asset decision-making models.

The project has proved that quantitative methods can be used to model physical systems as a functional digital twin. The enabling technology for this is probabilistic risk analysis, which invokes systems thinking to decompose a physical system top-down and encode the system into its digital representation that captures fundamental functions and relationships within the system. This same method of systems decomposition and quantification can be applied to other networks applicable systems.

Furthermore, by creating a quantitative representation of NGET’s use case physical system, coupled with the bottom-up approach in integrating asset data to build an overarching Bayesian belief network, this effort has proved that deep risk insights can be gathered. These insights enable decision makers to have a precise understanding of the risk state of the system, and distribution of monetised risk outcomes. 

Finally, by creating these quantitative system models and monetised risk distributions, this effort demonstrated that trade-offs can be assessed, which, coupled with advanced economic analyses, result in the identification of the most efficient spend (operational or capital expenditure) alternatives to mitigate system risks.

Dissemination 

Learning from this project has been shared with internal decision makers and shared with NGED innovation team who expressed interest from the start.