Cities and municipalities are crucial areas of focus due to the large and growing populations living in them. But they are also complex dynamic systems, which present challenges to energy system modellers. As well as technical and economic feasibility, sustainable energy measures require social acceptance to be adopted. This PhD project takes this starting point in order to further enhance existing and develop new energy system models, to provide decision support for stakeholders within municipal energy systems.
One distinguishing characteristic of decentralized energy system models at the municipality or city scale is a high spatial and temporal resolution for both supply and demand systems (Scheller et al. 2019), which in turn result in very large data requirements. Another common feature of these models is that they tend to be specific to one area of application, such as a specific city or region, and thus are not highly transferable to other areas. This is a substantial weakness of many energy system models, as well as being the main motivation behind the development of RE³ASON, which carries out cost-potential analyses and system optimization based on open data (McKenna et al. 2018). RE3ASON is a powerful, highly transferable model for municipal-scale energy system analysis, but focusses on the techno-economic dimensions of the energy system. However, the optimal energy transition pathway is not simply one of technological diffusion but of systemic enforcement. Existing transition concepts hardly show detailed interrelations between the supply and demand side since the applied energy system models leave stakeholder behaviour, last mile infrastructure, and critical sectors outside the scope.
This PhD project should therefore focus on further developments in the following areas:
• Social-technical energy transitions (STET): despite the suggestions of numerous studies, the practical adoption of the energy technologies required to deliver substantial reductions in emissions too often shows little success (Li et al. 2015). RE³ASON takes a central planner perspective, which results in optimum results that often diverge from measures taken by individual stakeholders. The STET framework should be adopted to extend the modelling perspective beyond that of a central planner and to incorporate inertia in the diffusion process.
• Energy infrastructure: the RE³ASON model takes an aggregated view of energy system infrastructure, including power, methane/hydrogen and heat networks. Especially in the context of the need for more flexibility, storage and sector coupling, this should be improved through integration/coupling with other methods/models (e.g. Hotmaps or Thermos). This will enable the competition between district heating, decentralised heat generation and insulation measures, to be adequately captured.
• Demand side: the focus on the residential sector means that important sectors such as personal mobility are overlooked, despite contributing a large proportion of local greenhouse gas emissions. Hence this research area involves increasing the detail of the demand side modelling, both in terms of the parallel demand profiles for electricity, heat and mobility, as well as the differentiation between socio-economic groups and archetypes. This area builds upon previous work of the main supervisor with Time Use data, e.g. McKenna et al. 2018, Fischer et al. 2019.
The extended model framework should be validated with historical data of the case studies. Consequently, this research contributes to the current discussions around decentralised energy systems, especially about the heterogeneity of decision-making, socio-technical energy transitions, and flexibility from sector-coupling.
Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Engineering, Mathematics, Energy Engineering, Industrial Engineering (and Management).
Essential background: Energy Systems Modelling, Programming, Geographical Information Systems (GIS), Optimization, Simulation and knowledge of MATLAB, GAMS, ArcGIS,R, Python, Java
Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• Apply for the Degree of Doctor of Philosophy in Engineering
• State the name of the lead supervisor as the Name of Proposed Supervisor
• State the exact project title on the application form
When applying please ensure all required documents are attached:
1. All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
2. 2 Academic Reference (on official headed paper and signed or emailed directly to us from referees official email address)
3. Detailed CV
• 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, 644-658.
• Li, F. G., Trutnevyte, E., Strachan, N. (2015): A review of socio-technical energy transition (STET) models. Technology Forecasting and Social Change, 100, 290-305.
• McKenna R., Bertsch, V., Mainzer, K., Fichtner, W. (2018): Combining local preferences with multi-criteria decision analysis and linear optimisation to develop feasible energy concepts in small communities, European Journal of Operational Research, 268 (3), 1092-1110.
• McKenna, R., Hofmann, L., Kleinebrahm, M., Fichtner, W. (2018): A stochastic multi-energy simulation model for UK residential buildings, Energy and Buildings, 168, 470-489.
• Scheller, F., Bruckner, T. (2019): Energy system optimization at the municipal level: An analysis of modeling approaches and challenges, Renewable and Sustainable Energy Reviews, 105, 444-461.