Automated verification of network connectivity
An essential building block in the development of automated schemes to reduce CI/CML with associated quantifiable benefits. Understanding of the LV circuit and transformer each customer is fed from allows expected benefits of SM data to be realised. This potentially includes the ability to verify customer phase feeding arrangement, assuming sufficient SM data availability.
Near real-time network connectivity understanding
Visibility of running arrangements at configurable network split points (link boxes). This will provide verification of ongoing operational network changes in as near-to-real-time as possible. This is an enabler for the enhanced level of LV network control which is an expected future requirement for DSO operation.
Improved LV network modelling capability
Development of capability to export LV network and associated network metrics (SM data, EV data, etc) into PSSE systems. Expected to become an essential tool as pressure on the LV network increases through the penetration of LCTs. Quantifiable benefits in terms of saving in design resources and reduced reinforcement through more accurate designs.
Scenario analysis of investment requirements for EV penetration
Development of real SPEN network topologies archetypes, including analysis of EV penetration density from understanding of customer EV charging requirements using GIS data, i.e. distributed on- & off-street parking and/or cluster EV charging locations. Supports the EV NIC proposal and informs SPEN reinforcement requirements for ED2.
Platform for EV connection management
Through the integration of LCT location data and improved LV circuit and transformer connectivity and rating understanding. This would be an overall LCT penetration visualization and access management platform for both internal designer and external customer use. Benefits would be achieved through streamlining the design process and improving customer service.
Objectives
Automated verification of network connectivity
An essential building block in the development of automated schemes to reduce CI/CML with associated quantifiable benefits. Understanding of the LV circuit and transformer each customer is fed from allows expected benefits of SM data to be realised. This potentially includes the ability to verify customer phase feeding arrangement, assuming sufficient SM data availability.
Near real-time network connectivity understanding
Visibility of running arrangements at configurable network split points (link boxes). This will provide verification of ongoing operational network changes in as near-to-real-time as possible. This is an enabler for the enhanced level of LV network control which is an expected future requirement for DSO operation.
Improved LV network modelling capability
Development of capability to export LV network and associated network metrics (SM data, EV data, etc) into PSSE systems. Expected to become an essential tool as pressure on the LV network increases through the penetration of LCTs. Quantifiable benefits in terms of saving in design resources and reduced reinforcement through more accurate designs.
Scenario analysis of investment requirements for EV penetration
Development of real SPEN network topologies archetypes, including analysis of EV penetration density from understanding of customer EV charging requirements using GIS data, i.e. distributed on- & off-street parking and/or cluster EV charging locations. Supports the EV NIC proposal and informs SPEN reinforcement requirements for ED2.
Platform for EV connection management
Through the integration of LCT location data and improved LV circuit and transformer connectivity and rating understanding. This would be an overall LCT penetration visualization and access management platform for both internal designer and external customer use. Benefits would be achieved through streamlining the design process and improving customer service.
Learnings
Outcomes
The main outcomes of the project thus have been the development of two significant platforms, NAVI and LView, which will be incorporated into our BaU practices pending the successful conclusion of trials currently underway.
A key outcome has been the release of the NAVI platform to all district design engineers and rationalising/backfilling our asset information to provide a fully rationalised network model. The NAVI platform provides SPEN with the ability to automatically create a connected network model from GIS data, including backfilling of missing assets. Stakeholder engagement has seen project success through:
- Commencement of design business change for Point of Connection analysis and modelling through NAVI, including the development of exports at both LV and HV to multiple PSSE tools to automate building network models, fully annotated with all NAVI network data.
- LV network data analytical input into ongoing innovation projects including LV Engine, Charge, SDIF(Sinepost) and EvoLVe
- Initial use of available Smart Meter data to help develop methodologies to identify phase information and network constraint predictions.
The LView platform consolidates the view of all potential low voltage (LV) alarms/data into a single view, including smart meters, PowerOn incidents and calls, and LV monitors for near-real-time visualising of their LV alarms. This has allowed SPEN to internally manage this data and move away from several third-party platforms and display ‘our own’ data via a single platform. LView provides faster support to field personnel as the data is more accessible on mobile devices typically connected via slower data connections.
It is hoped that the level of sufficient Smart Meter data will continue to increase to allow us to continue the investigation of the connectivity improvement potential.
Lessons Learnt
The following lessons were learnt from the project:
- Due to the slower than anticipated rollout of smart meters in the UK, and comms issues in SPEN’s SPD area, some of the connectivity improvement Data Analytical techniques that the project sought to explore have not been able to be fully analysed. By working with other SP Energy Networks innovation projects, we are utilising available data sets, including that from substation monitors, to try and create a more complete picture of the network.
- This will assist with the growing requirement for verification and near-real-time connectivity understanding, including phase identification, which continues to be a major requirement within this project to maximise the benefit of LV data analytics going forward.
- Adopting an agile approach to development also ensures we remained on track and focused on the key business requirements, but also provided the ability to adapt to ever changing priorities. Involving key users in the testing of functionality enabled a smooth transition from project to BaU as the wider business has been part of the journey.
- Data is not perfect, and we therefore often need to “cleanse” data inputs. We need to provide the time and effort for this to happen. Adapting methodologies is also sometimes required in order to fulfil the use case with potentially incomplete or unstructured data.
- End user stakeholder engagement is critical. To fully understand the processes and requirements of the business, constant communications with the end users is crucial to ensure the platform remains of benefit to the business and requirements are met.
