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Developing geospatial methods for resource assessments of renewable energy technologies


School of Engineering

About the Project

Energy planning processes require detailed data relating to the technical potential and associated costs of renewable energies. These data are employed by stakeholders and researchers to inform decision-making relating to future energy systems. Often these data are used as input for energy system models, with which to analyze the implications of and trade-offs between individual measures and technologies such as solar PV, onshore wind and bioenergy plants. A very active research field is concerned with developing methods and tools to assess potentials and costs for these resources. This PhD project should build on existing research in resource assessment methods for renewable energy technologies (e.g. McKenna et al. 2015) referenced papers) along the following lines:

• Improve the consideration of non-technical aspects within these methods, for example public acceptance of wind turbines, through open data on landscape value or economic data relating to valuations in the vicinity of wind parks (Price et al. 2020, McKenna et al. 2020)• Improve the accuracy of existing methods for automatic recognition of rooftop geometries from Open Street Map and Bing Maps, in order to identify potential locations for solar PV modules (Mainzer et al. 2017).
• Consolidate and extend existing methods of land suitability assessment for renewable energy technologies based on open data. In particular, this means clarifying definitions of land use categories, offset distances and their implications. Ryberg et al. 2019, 2018.
• Explore possibilities to exploit remote-sensing data for energy system modelling, e.g. building outlines and geometry, energy infrastructure etc.
• Further developing potential assessment methods for offshore wind based on existing studies and literature in this area.

It is expected that the PhD project will build on existing tools and should therefore implement the above improvements within a modelling framework that can be interactively employed by diverse stakeholders (including experts and non-experts).

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

APPLICATION PROCEDURE:

• 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 (), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

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

Funding Notes

This project is advertised in relation to the research areas of the discipline of Engineering. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THIS PROJECT

References

- Mainzer, K., Killinger, S., McKenna, R., Fichtner, W. (2017): Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques, Solar Energy, 155, 561-573, https://doi.org/10.1016/j.solener.2017.06.065
- Ryberg, D. S., Tulemat, Z., Stolten, D., & Robinius, M. (2019). Uniformly constrained land eligibility for onshore European wind power. Renewable Energy, 146(0960–1481), 921–931. https://doi.org/10.1016/j.renene.2019.06.127
- Ryberg, D. S., Robinius, M., Stolten, D. (2018): Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe, energies, 11, 1246.
- Price, J., Mainzer, K., Petrovic, S., Zeyringer, M., McKenna, R. (2020): The implications of landscape visual impact on future highly renewable power systems: a case study for Great Britain, IEEE Transactions on Power Systems, in press, May 2020.
- McKenna, R., Hollnaicher, S., Ostmann v. d. Leye, P., Fichtner, W. (2015): Cost-potentials for large onshore wind turbines in Europe, Energy, Volume 83, 1 April 2015, Pages 217–229.
- McKenna, Russell; Weinand, Jann Michael; Mulalic, Ismir; Petrovic, Stefan; Mainzer, Kai; Preis, Tobias; Moat, Helen Susannah (2020), Improving renewable energy resource assessments by quantifying landscape beauty, IIP Working Paper Series in Production and Energy, No. 43, April 2020.
https://publikationen.bibliothek.kit.edu/1000118671

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