Electricity assets such as towers and the equipment in substations have been designed to withstand a variety of environmental conditions. The increasing number and ferocity of extreme weather events exposes assets to a greater risk. For example, uncontrolled surface water flooding events are expected to rise along with the cost of damages.
To ensure the network continues to provide an uninterrupted electricity supply to its millions of customers, the ever-growing complexity of environmental risks needs to be constantly monitored.
Given the expected increase in extreme weather events, the current practice is likely going to be under increased pressure which will limit the ability to recognize escalating environmental risks.
Benefits
Benefit to consumers:
The detailed assessment of consumer benefits will be part of this research and will depend on the use cases, the chosen deployment model and the number of deployments which will deliver economies of scale. Benefits are expected in terms of operational efficiencies.
Benefit to energy networks:
The outputs of this research will support energy networks in providing high levels of assurance as to the most appropriate mitigation to prevent the environmental effect of flooding and erosion. They will also be able to benefit from new opportunities created by a more digitised environment for the management of assets and related processes.
Learnings
Outcomes
- Insights from Previsico’s technologies have supported operational activities in a variety of ways. In particular, site specific sensors allowed stakeholders to understand the situation significantly quicker than getting someone on site. For instance, particular sensors at specific locations provided reassurance that there was no major risk issue, despite some of the flood gauges being at their highest recorded values. This would ordinarily have taken significantly longer using the traditional method to get someone on site to check (as well as challenging because the access road was closed).
- automated weather alert (AWA) tool proves the possibility of bringing together flooding data sources to assess asset risk.
- AWA tool users get a singular picture of asset risk due to flooding alongside informative gauge/sensor data without having to consult numerous websites in limited time when the flooding picture is rapidly varying and decisions need to be made quickly. It is difficult to assess the situation for multiple sites simultaneously and AWA tool makes it easier.
- NGET personnel consider the tool easy-to-use to the extent that others with less flood management experience would be able to utilise the tool for situational awareness and recognise necessary actions.
- AWA tool considers numerous flooding data sources for different types of flooding i.e., surface water, coastal, ground water, and river flooding.
- AWA tool allows the user to interrogate long term erosion predictions for vulnerable tower sites/regions.
Recommendations for further work
NGET primary & secondary users support further work in future to upgrade the tool from the initial proof of concept into a long-term fully fledged BaU tool:
General
- Further proving of the concept leading through to BaU adoption within NGET and other areas of National Grid
- Consideration of integrating other weather events or parameters i.e.,
- wind
- extreme heat/fluctuations (hot and cold, swings)
- snow/ice
- lightning
- and more mature coastal flooding
- Installation of additional sensors and/or monitoring systems e.g.,
- weather stations
- land surface erosion monitors
- drainage (surface water/wastewater) level and flow sensors
- saline sensors
- Explore innovation opportunity of using lidar sensors both for validation of erosion modelling and/or early alerting. This could include updating the erosion modelling from annual lidar measurements or identify particular locations with additional risk
- Inform tower risk as part of maintenance planning
- Future work could also look at the erosion that is occurring near substation sites (rivers near the sites rather than sites themselves)
- Explore innovation opportunities to ascertain whether historic gauge data could be paired with required mitigations which in turn could inform an automated recommendation engine
- Strongly pursue the prospect of associating lightning strike data with local individual and tower routes to zone in and inform vulnerable substations to hidden lightning risk/impact
- Related to the point above, consideration of historic lightning strike data linked to the actions/outputs undertaken can inform a methodology for future operations, maintenance and planning schedules. The current approach or practise only logs issues without activating correct countermeasures to be undertaken
Tool functionality
- Implementation of further functionality:
- Allowing for free text data as input
- Allowing for exporting of data in best-practise formats
- Implementation of different user views/permissions
- Notification of alerts to designated personnel via email or any required channel
- Maturing Risk Calculation:
- Introduce a machine learning algorithm to retain and learn from historical data and respond to user input
- Improve the tool to adapt based on actual weather events to update forecasts
- Integration with NGET data
- ‘Live’ integration with NGET asset data and network status
- Integration with other related tools and TNCC data sources
Lessons Learnt
Year 2023/2024
Overall, it was noted that the site visits that NGET arranged for the innovators to the Transmission Network Control Centre (TNCC) early in the project helped the project partners with understanding of the current systems/approach which led to expected delivery output.
NGET together with all the project suppliers identified lessons during project delivery.
Previsico:
- Obtained greater awareness of NGET’s structures and processes associated with works planning and H&S aspects which will positively impact future sensor installations.
- Developed a robust internal workflow and process to support expedition of obtaining installation consents and delivery of installations on NGET sites. This awareness would avoid future delay risk for instance where at one point there was a time approximately 7 sites had protracted ownership queries.
- Now that the process to obtain site access has been established it should be easier to deliver the scale of similar projects in the future.
Frazer-Nash:
- Encountered issues whilst deploying the tool due to unforeseen technical issues and the major take-away is to aim to identify & mitigate these potential risks early at planning stage.
- Faced inconsistencies in open-source APIs and the future recommendation is to consider identifying data requirements and subscribing to a paid-for-service outside of a firm contract price.
- A small number of high impact technical issues during the project caused a budgetary overspend, suggesting a lesson that additional related contingency budget may be required for similar projects in the future.
University of Liverpool:
- Obtained experience involving gaining prior permission to access sites and ensuring all associated permissions are sought e.g., with critical stakeholders like EA or other relevant river corridor managers.
