About the Project
• Depth of energy demand insights on the demand side, i.e. granularity in e.g. households will be further increased. For example, an LPG (SYNPRO-emobility) was co-developed by the Main Supervisor for the German context in order to generate EV charging profiles. The level of socioeconomic differentiation could be improved in this project. In addition, demand fractions classified as technically-flexible or non-flexible will be incorporated into these model developments in order to facilitate analyses of load shifting potentials etc.
• Improving sequences of activities over days and weeks: increased flexibility within the energy system is required in order to effectively integrate large volumes of renewable energy. One integration measure is energy storage, which exists in various forms with different techno-economic characteristics, for example the storage volume and time duration. In order to perform detailed energy system analyses of highly-renewable systems with storage, long time series of load profiles are required, which are typically not provided by state-of-the-art LPGs.
• Improving these models in the international dimension: open source models have been co-developed for the UK (CREST and CHAP) and Germany with members of the PhD supervision team, and are in development by the Main Supervisor for Austria and Denmark. Additional countries will be considered, based on data availability and the precise locations of the interventions, once the project commences.
• Exploit smart meter datasets to provide rich paramterisation for bottom up models: Hence in order delve deeper into the socioeconomic characteristics of the demand side, this PhD project will define a consistent set of energy archetypes in order to reduce the complexity and heterogeneity of the system being analysed (“the demand side”). The archetypes will be defined according to important socioeconomic parameters relating to individuals, households and buildings
Candidates should have (or expect to achieve) the UK honours degree at 2.1 or above (or equivalent) in Engineering, Mathematics, Energy Engineering, Industrial Engineering (and Management). It is essential that the applicant has a background in Energy Systems Modelling, Programming, Geographical Information Systems (GIS), Optimization, Simulation along with knowledge of MATLAB, GAMS, ArcGIS, R, Python, Java
• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
Informal inquiries can be made to Professor R McKenna (email@example.com), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School (firstname.lastname@example.org)
It is possible to undertake this project by distance learning. Interested parties should contact Professor McKenna to discuss this. Distance Learning applicants should have access to a good quality computer
• Zhang, T., Siebers, P.-O. and Aickelin, U. (2012) ‘A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK’, Energy Policy, vol. 47, pp. 102–110.
• Mata, É., Sasic Kalagasidis, A. and Johnsson, F. (2014) ‘Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK’, Building and Environment, vol. 81, pp. 270–282,
• McKenna, R. et al. (2013) ‘Energy efficiency in the German residential sector: A bottom-up building-stock-model-based analysis in the context of energy-political targets’, Building and Environment, vol. 62, pp. 77–88.
• McKenna, R., Kleinebrahm, M., Yunusov, T., Lorincz, M. J., Torriti, J. (2018): Exploring socioeconomic and temporal characteristics of British and German residential energy demand, Paper presented at the BIEE Annual Conference “Consumers at the Heart of the Energy System?”, September 2018, Oxford, UK.
• McKenna, E., Thomson, M. (2016): High-resolution stochastic integrated thermal–electrical domestic demand model, Applied Energy, 165, 445-461
• Ramirez-Mendiola, J., Grunewald, P. and Eyre, N. (2019) Residential activity pattern modelling through stochastic chains of variable memory length. App. Energy, 237: 417-430.
• Fischer, D., Harbrecht, A., Surmann, A., McKenna, R. (2019): Electric vehicles‘ impacts on residential electric load profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors, Applied Energy, 233-234, 644-658
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