smart-networks

PILLAR: SMART NETWORKS

Envision and Foresight

65-Envision-and-Foresight-e1687188576904

What we will do and why

Application of machine learning to model and estimate utilisation at low voltage substation and transformer level, targeting reduction in the need to install physical sensors and monitoring.

Key deliverables

  • Model estimating capacity for all secondary substations.

Target outcomes

  • Targeted, proactive reinforcement.
  • Ensuring customer LCT connections.

Measures of success

  • Reduction in number of capacity constraints across the LV network.
  • Customer satisfaction.

Stakeholder Engagement

We employed third party data-science expertise in the development of the model, checking results with real measurements through work with field staff. Work involved collaboration between data scientists, subject matter experts, IT professionals, and business stakeholders. In parallel with Envision, we developed alternate models using different methodologies and compared results. Through collaboration between Distribution Network Operator (DNO) and Distribution System Operator (DSO) data teams we have arrived at a process for use of these predictive models in our operations.

Planned start:

01.04.2023

Target completion:

01.04.2024

Key milestones:

  • Base model created (Oct 2023)
  • Internal apps and dashboards (Dec 2023)
  • Enhanced model with smart meter data (Apr 2024)

Status:

Complete

Date of change Category Description
24.12.2023 Update

We added links to additional information and added a summary of our stakeholder engagement as part of Envision and the further development we have been doing to this model under the title “Foresight”.