In Work Package 1, we’ve continued to look at methods to integrate novel energy vectors into distribution network operation and planning. Using the St Andrews Eden Campus as a testbed for a multi-vector network, we have been developing a model for integrating behind-the-transformer technologies such as hydrogen electrolysis and new industrial demand from a gin and whisky distillery to evaluate how these non-traditional loads might impact the load at a secondary distribution transformer.
The next step for this work is to look at the flexibility inherent within these technologies, as well as working with the building and heat network modelling work package to understand how these vectors can best be represented in terms of electricity demand and operational envelopes within which a DNO can seek options for secure network operation.
These concepts were presented in collaboration with ScottishPower Energy Networks at a the annual SuperGen Energy Networks Conference at Bath University in September.
In parallel, we have been looking at optimisation and dispatch methods that a Distribution Network Operator might use to make use of this flexibility, looking at key questions such as:
Allowing us to refine multi-vector methods and demonstrate them before, in the next stage of ENSIGN, dealing with the real-world complexity of high-dimensional distribution networks at multiple voltage levels.
Over the past three months, our team has been driving forward a range of projects that blend technical research, energy modelling, and industry collaboration.
A major milestone has been the acceptance of our conference paper for IEEE PES International Meeting, Hong Kong SAR, China, 18 – 21 January 2026, which explores agent-based modelling of user behaviour at the feeder level, using a case study in Glasgow. This work helps fill gaps in our models where they extend beyond the core scope of ENSIGN, offering valuable insights into real-world energy use.
In parallel, we’ve been developing IES VE models for the University of St Andrews’ Eden Campus. These models provide high-fidelity digital representations of campus buildings, enabling detailed analysis of energy performance. By simulating various scenarios, we’re identifying opportunities for energy efficiency and supporting the university’s sustainability strategy.
A key development is our collaboration with IES and the University of St Andrews to establish a live data link between the VE models and real-time operational data. This marks a shift from static simulations to a dynamic, continuously updated platform—enabling real-time monitoring, performance optimisation, and scenario testing as conditions evolve.
