Introduction: The energy market is complex and evolving – particularly with growing smart technologies and embedded, renewable generation. For DNOs, the increasing number of ‘invisible’ changes (growth of Electric Vehicles (EV), photovoltaic (PV) and other Low Carbon Technologies (LCTs)) challenge existing network practices. At present, technology change is outpacing changes in modelling and forecasting of consumer uptake of ‘smart’, Distributed Energy Resources (DER) or Electric Vehicle (EV) technology; therefore, it is difficult to monitor or understand the change in requirements on the LV network under existing arrangements, without monitoring EV and DER impacts directly at source (or substation level). While smart meters will improve the visibility of network load and generation in the longer term, there is a need for a solution that can identify unregistered equipment. The problem this project addresses is how to improve WPD’s ability to identify EVs, DERs and other LTCs connected to its network so that future operational and future investment decisions can be improved. It will also support some of the informational requirements needed in its transition to a DSO.

Objectives: By using Electralink’s DTS dataset, combining this with a range of other structured and unstructured data and then applying IBM’s Cognitive analytics, the objective is to identify patterns in the data that indicate the presence of EV, PV or other LCTs that had not previously been identified. IBM will apply its Watson technology to perform advanced analytics on the ElectraLink, combined with other datasets. IBM will use a progressive and iterative methodology to detect patterns in the data that was not detected hitherto. By improving detection of LCT on the network, the project will also build the foundation for improving forecasting capabilities and, ultimately, garner an understanding the effectiveness and costs for the various options would allow for the validation process to be optimised.