High Voltage (HV) Auto Quote will provide self-service connection offers to customers enquiring about connections between 300 kVA and 1 MVA. Furthermore, it aims to provide budget estimates for connections from 1 MVA to 2.5 MVA. This project will codify the tacit logic used in HV quotes to enable this automation, together with development of logic to assess the cost of traffic management (TM). TM is an increasingly large component of quotes as voltage levels increase.
Budget estimates are currently offered to customers in other network areas for low voltage (LV) networks. However, lack of traffic management integration prevents connection offers being issued at HV levels. This innovation project will perform the necessary analysis and development to put connection offer requests in the hands of the consumer, allowing them to see the cost of different options interactively, reducing time to deliver connection offers and improving service delivery.
Benefits
This project has the potential to deliver substantial benefits to customers and society through greatly reduced times for HV connection offers which allow the requestor to interact with the connection offer process, altering parameters such as the connection point, to find the most optimal location to connect to the network before the quote is issued rather than requesting multiple manual quotes. This should facilitate the accelerated uptake of low carbon EV charge points and new housing projects.
This is estimated to provide £8.8m (NPV) of connection offer generation effort saved across RIIO-ED2. This effort can then be re-focused on providing higher impact services as well as minimising costs to DUoS customers from abortive work, particularly in light of Access and Charging Reforms which entail a greater proportion of connections costs being borne by DUoS customers in the future.
Learnings
Outcomes
There are limited outcomes to be reported as the project is still in the early stages of implementation. However, the project has so far discovered and documented the current manual quotation processes and logic, underlying data required and the requirements for integration into UK Power Networks’ systems. This has enabled the project to draft an initial costing logic and customer journey, draft specifications for integration with UK Power Networks’ enterprise systems and deploy a heatmap to UAT for a sample area of SPN which shows where there is capacity for customers to connect.
Lessons Learnt
The Project has not discovered any significant problems with the trialling of the Method. However, HV Auto Quote will not be integrating into UK Power Networks’ Smart Connect Platform as per the original Method as Smart Connect is not an appropriate platform for the integration of HV Auto Quote. HV Auto Quote will integrate with UK Power Networks’ website and is exploring the option to integrate into UK Power Networks’ new connections portal Smart Gateway. In addition, the Method describes that only location data will be processed by Auto Quote. However, the project has learnt that additional customer data is required to integrate with UK Power Networks’ enterprise systems.
Customer feedback sessions took place in February with a mix of HV customers. They provided feedback on the customer journey, data input questions and other topics which has helped to inform the design and requirements of HV Auto Quote. For example, from the sessions the project learnt that customers would be accessing HV Auto Quote via a tablet or a computer but not a mobile phone. We also learnt that different user types would follow different workflows which would requires different users to pause at different points in the customer journey whilst they gather the necessary information or seek approval for the next step. For example, consultancies working on behalf of the customer may not have access to the same level of information about their site and therefore more guidance will be needed on specific traffic management questions for them.
Future features and enhancements that were identified but are not within the scope of implementation have been documented in a backlog and are useful lessons learnt for future projects.
Through implementation, the project has come across various data quality challenges. These are challenges which not only apply to the project but also the wider business. We have learnt that identifying other business stakeholders that have an interest in the same problems and challenges we are finding can help to provide and develop longer term solutions that provide benefits beyond innovation and this project.
