Local Joint Restoration Plans (LJRP) detail the agreed method and procedures used to restore the total system following a total or partial shutdown. For Transmission Licensee’s, an LJRP defines the critical assets and substations selected for initial re-energisation of the transmission network.
NGET forecasts a growth in LJRPs across the transmission system. In many cases this is where the network by design offers less redundancy or is depleted whilst network reinforcements are carried out. In these instances, optimising LJRP energisation strategies is critical.
This project seeks to demonstrate a rigorous quantitative risk analysis methodology for the pre-planning and selection of assets and substations included within LJRP’s. The project will enable a greater level of intelligence relating to network performance and decision optimisation following a shutdown.
Benefits
The assumption for the benefit estimation is that this quantitative approach would deliver 12.5% reduction in LJRP implementation time. This yields potential savings/NPV benefits over the next 6 years of approximately £3.410m in exposure costs, based on the value of lost load.
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.
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.
