Leveraging energy data to democratise distribution network analysis

Context

The uptake of electric vehicles and other low carbon technologies (LCTs) is changing the energy system. Until now, the energy data system has largely been operated in a top-down and linear way. However, the growing magnitude, variability and direction of power flows are already having important impacts on electricity networks. In order to ensure a successful transition to a net zero energy system, we need to understand the impacts of these changing flows on networks. energy data

However, there isn’t enough data sharing taking place across the energy landscape. Network operators, electricity suppliers and infrastructure players can work together to intelligent design the future energy system. Yet at the moment, limitations in access to different types of energy data is creating a fragmented approach to future-proofing the grid.  

Purpose

This paper describes how the Electric VEhicle Network Analysis Tool (EVENT), developed by the Alan Turing Institute, can help make network analysis accessible to a wider range of stakeholders in the energy ecosystem. These stakeholders often don’t have the bandwidth to curate and integrate disparate datasets and carry out electricity network simulations. 

EVENT analyses the potential impacts of LCTs on congestion in electricity networks, helping to inform the design of products and services. To demonstrate EVENT’s potential, Centre for Net Zero provided valuable energy data. We shared smart meter profiles that enabled the tool to demonstrate the LCT penetration levels under which specific networks would come under strain.

Key conclusions

Network operators and energy suppliers will need to work much more closely together. Consumers can support the energy system via flexibility, whilst respecting safety and security constraints within networks. With this in mind, research into domestic flexibility is key. For further information about the largest study into this area, check out our work on CrowdFlex.

This paper was published by Data-Centric Engineering in January 2023. You can read the report in full by clicking on the link on the right-hand side.