I joined the Department of Electronic and Electrical Engineering at Strathclyde in 1998, and am currently a Reader in Electrial and Electronic Engineering.
Based within the Advanced Electrical Systems group within the Institute for Energy and Environment (InstEE), I currently lead two research groups,
the first in Novel Electrical Systems and the second in Data Analytics. My research is concerned with multi-domain modelling of aircraft, marine
and micro-grid applications and the data analytics related to smart energy, smart grid and behavioural change.
As such, the research spans two of the defining themes of the University’s Technology and Innovation Centre, namely, Power & Energy and
Human & Social Aspects of Technology
I have been fully committed to and engaged with Departmental undergraduate and MSc course delivery for more than 10 years, accumulating extensive
experience of teaching, assessment, module design and course development at all levels.
- Industrial applications
- Experimental design
- Human factors in energy
- Scenario development
- QFF Quarterly Forecasting Forum, June 2018
- IET Electrical Systems in Transportation (Journal)
more professional activities
- The Impact of Energy System Decarbonisation and Decentralisation on Scotland's Electrical System CxC Project J Bowes
- Galloway, Stuart (Principal Investigator)
- 06-Jan-2020 - 05-Jan-2021
- University of Strathclyde contribution to SMPP(Scaleable Multi-Platform Power Systems)
- Norman, Patrick (Principal Investigator) Burt, Graeme (Co-investigator) Galloway, Stuart (Co-investigator)
- 09-Jan-2019 - 31-Jan-2021
- Analytical Middleware for Informed Distribution Networks (AMIDiNe)
- Stephen, Bruce (Principal Investigator) Browell, Jethro (Co-investigator) Galloway, Stuart (Co-investigator) Wallom, David (Co-investigator)
- 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.
- 01-Jan-2019 - 31-Jan-2021
- AGILE - Aggregators as diGital Intermediaries in Local Electricity markets: EPSRC/ESC Follow on Funding
- Galloway, Stuart (Principal Investigator) Irvine, James (Co-investigator) Stephen, Bruce (Research Co-investigator)
- 01-Jan-2018 - 30-Jan-2020
- Rural Energy Access through Social Enterprise and Decentralisation (EASE)
- Galloway, Stuart (Principal Investigator)
- 01-Jan-2018 - 31-Jan-2020
- EPSRC Centre for Doctoral Training in Future Power Networks and Smart Grids | McGarry, Connor
- Galloway, Stuart (Principal Investigator) Burt, Graeme (Co-investigator) McGarry, Connor (Research Co-investigator)
- 01-Jan-2018 - 01-Jan-2022
Electronic and Electrical Engineering
Royal College Building
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