Analytical Middleware for Informed Distribution Networks (AMIDiNe)

The programme of research that constitutes AMIDiNe will devise analytics that link point measurement to whole system to 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. 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. 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 - unlike many recent 'Big Data' applications of machine learning, power systems applications encounter lower coverage of exemplars, feature well understood system relations but poorly understood behaviour in the face of uncertainty in established power system models.

AMIDiNe sets out its analytics objectives in 3 interrelated areas, those of understanding how to incorporate analytics into existing network modelling strategies, how go from individual to group demand behavioural anticipation and the inverse problem: how to understand the constituent elements of demand aggregated to a common measurement point.

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.

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 machine learning techniques already used in other domains. Strategic investment decisions for network infrastructure components can be made on the back of this 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 their actual behaviour and its relation to network voltage and emergent protection configuration constraints.

The GB power system is transitioning to new operating models to address decarbonisation and resilience challenges at a rate not seen in generations. AMIDiNe is focused on developing the analytical tools that will enhance the understanding of localised demand and generation characteristics on distribution network behaviour to DNOs as they transition to Distribution System Operators (DSO), and to the Transmission System Operator (TSO) as it deals with the transition away from the traditional generation and load operating model of the past. For Distribution Network Operators the increasing penetrations of distributed generation have presented a challenge to installing and maintaining the regional down to neighbourhood levels of network - setting demand behaviour models in the context of power system models will assist in informing actual impacts on network performance and the resulting control strategies required. From the perspective of control with new technology, energy storage operators would benefit from understanding how to identify and anticipate the emerging opportunities in providing grid services at distribution level. AMIDiNe partners are well placed in the industry to propagate these innovations through to enhance their respective operational practices and those of their clients.

AMIDiNe will work with network SSEN to both build understanding of the requirements of analytics for a DSO and for a transmission and distribution network owner facing increasing challenges to its assets from low carbon technologies gaining them understanding of hierarchical and grouped load which will be beneficial as they approach the need to interface more with distribution level players. Drax will benefit from understanding how industrial and commercial loads are evolving, the LCTs 'behind the meter' that are influencing these and the opportunities for flexibility that can be presented to the system operator. Power systems have data available to them in increasing volumes, but this is disparate and can really only be leveraged to its full potential when used in conjunction with other data and domain knowledge. Working with The CountingLab will enable them to continue to introduce powerful analytical tools with greater reflection of the changing environment to their energy sector clients, while Bellrock will be able to demonstrate how diverse data streams unified through their Lumen(TM) platform can provide enhanced operational situational awareness when leveraged with advanced analytics.