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Details of Grant 

EPSRC Reference: EP/S030131/1
Title: Analytical Middleware for Informed Distribution Networks (AMIDiNe)
Principal Investigator: Stephen, Dr B
Other Investigators:
Wallom, Professor D Galloway, Professor S Browell, Dr J
Researcher Co-Investigators:
Mr R Granell
Project Partners:
Bellrock Technology CountingLab Ltd Drax Power Limited
Scottish and Southern Energy (SSE)
Department: Electronic and Electrical Engineering
Organisation: University of Strathclyde
Scheme: Standard Research
Starts: 01 June 2019 Ends: 31 May 2021 Value (£): 703,091
EPSRC Research Topic Classifications:
Energy Efficiency Sustainable Energy Networks
Sustainable Energy Vectors
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Apr 2019 Engineering Prioritisation Panel Meeting 9 and 10 April 2019 Announced
Summary on Grant Application Form
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.
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Organisation Website: http://www.strath.ac.uk