The programme of research that constitutes AMIDiNe will address the increasingly problematic management of electrical load on distribution networks as the UK transitions to a low carbon energy system. Traditionally, distribution networks had no observability and power flowed from large generation plant to be consumed by customers in this ‘last mile’. Now, and even more so in future, those customers are generators themselves and the large generators that once supplied them have been supplanted by intermittent renewables. This scenario has left the GB energy system in position where it is servicing smaller demands at a regional or national level but faces abrupt changes in the face of weather and group changes in load behaviour, therefore it needs to be more informed on the behaviour of distribution networks. Increasing availability of Smart Meter data through the Data Communications Company has the potential to address this but only when placed within the context of analytical and physical models of the wider power system.

The UK government’s initiative to roll out Smart Meters across the UK by 2020 has the potential to illuminate the true nature of electricity demand at the distribution and below levels which could be used to inform network operation and planning. Current research broadly involving Smart Metering focuses on speculative developments of future energy delivery networks and energy management strategies. Whether the objective is to provide customer analytics or automate domestic load control, the primary issue lies with understanding then acting on these data streams. Challenges that are presented by customer meter advance data include forecasting and prediction of consumption, classification or segmentation by customer behaviour group, disambiguating deferrable from non-deferrable loads and identifying changes in end use behaviour. Transferring this to an operational scenario will require understanding of where and how computing resource is placed and specifically which functionality we are asking of these resources, the interchange formats between substation automation data and premises level, the operational forms of models with which the intelligent grid is informed and the predicates on which they act.

Moving from a distribution network with enhanced visibility to augmenting an already ‘smart’ transmission system will need understanding of how lower resolution and possibly incomplete representations of the distribution network(s) can inform more efficient operation and planning for the transmission network in terms of control and generation capacity within the context of their existing models. Improving various distribution network functions such as distribution system state estimation, condition monitoring and service restoration is envisaged to utilise analytics to extrapolate from the current frequency of data, building on successful Big Data techniques already used in other domains. This extrapolation would require significant scaling of the analytics used to make Distribution System State Estimation and Dynamic Optimal Power Flow techniques useful at a full network scale and would be able to support the introduction of automation. The overall aim is that strategic investment decisions for network infrastructure components can be made on the back of improved information availability. These decisions could be deferred or brought forward in accordance with perceived threats to resilience posed by overloaded legacy plant in rural communities or in highly urbanised environments; similarly, operational challenges presented by renewable penetrations could be re-assessed according to the true nature of demand and export profiles and their relation to network voltage and emergent protection configuration constraints.