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Developing a high-resolution spatial model to assess solar photovoltaic panel suitability for reduction of greenhouse gas emissions and energy costs


   Faculty of Health and Life Sciences

  Dr Paul McKenzie, Dr Saad Bhatti, Prof P Jordan  Monday, February 06, 2023  Competition Funded PhD Project (Students Worldwide)

About the Project

Low Carbon Technologies such as solar PV can reduce greenhouse gas (GHG) emissions and generate electricity for self-consumption, which cut down the import of energy from the grid which is susceptible to energy price shocks.

Geographic Information Systems (GIS) and remote sensing have demonstrated high levels of effectiveness for identifying suitable sites for solar PV at a range of spatial scales. Light Detection and Ranging (LiDAR) data are highly suited for modelling small scale features in complex landscapes. This project aims to determine the potential of solar PV to reduce CO2 emissions and reduce energy demand for a region of Northern Ireland by using LiDAR and ancillary spatial data to create a high-resolution solar PV suitability model.

The successful candidate is expected to acquire, process, and analyse geospatial data, particularly collected through remote sensing methods including LiDAR, to develop a spatial model for solar PV panel suitability. The candidate will also calculate a range of metrics based on solar PV models relating to GHG emissions and residential energy demand, along with reviewing and analysing the opportunities and challenges towards adopting solar PV panels in Northern Ireland.


References

Department for Business, Energy, and Industrial Strategy (2022) 2020 UK Greenhouse Gas Emissions, Final Figures, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1051408/2020-final-greenhouse-gas-emissions-statistical-release.pdf [Accessed 20.10.22]
Dinesh, H and Pearce, JM (2016) The potential of agrivoltaic systems, Renewable and Sustainable Energy Reviews, 54, 299, https://doi.org/10.1016/j.rser.2015.10.024
Finn, T and McKenzie, P (2020) A high-resolution suitability index for solar farm location in complex landscapes, Renewable Energy, 158, 520, https://doi.org/10.1016/j.renene.2020.05.121
Gawley, D and McKenzie, P (2022) Investigating the suitability of GIS and remotely-sensed datasets for photovoltaic modelling on building rooftops, Energy and Buildings, 265, 112083, https://doi.org/10.1016/j.enbuild.2022.112083
Hoicka, CE, Lowitzsch, J, Brisbois, MC, Kumar, A and Camargo, LR (2021) Implementing a just renewable energy transition: Policy advice for transposing the new European rules for renewable energy communities, Energy Policy, 156, 112435,
https://doi.org/10.1016/j.enpol.2021.112435.
Jaeger, J, Gonçalves, T, Harsono A and Bird, L (2022) Renewable Energy Shouldn’t Be Blamed for Spiking Energy Prices — It's the Solution. https://www.wri.org/insights/why-renewable-energy-solution-high-prices [Accessed 20.10.22]
Jakubiec, JA and Reinhart, CF (2013) A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations, Solar Energy, 93, 127, http://dx.doi.org/10.1016/j.solener.2013.03.022
Lagendijk, A, Kooij H-J, Veenman, S, Oteman, M (2021) Noisy monsters or beacons of transition: The framing and social (un)acceptance of Dutch community renewable energy initiatives, Energy Policy, 159, 112580, https://doi.org/10.1016/j.enpol.2021.112580
Nonhebel, S (2005) Renewable energy and food supply: will there be enough land? Renewable and Sustainable Energy Reviews, 9(2), 191, https://doi.org/10.1016/j.rser.2004.02.003
Schulte, E, Scheller, F, Pasut, W and Bruckner, T (2022) Product traits, decision-makers, and household low-carbon technology adoptions: moving beyond single empirical studies, Energy Research & Social Science
83, 102313, https://doi.org/10.1016/j.erss.2021.102313
Stowell, D, Kelly, J, Tanner, D, Taylor, J, Jones, E, Geddes, J and Chalstrey, E (2020) A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK, Scientific Data, 7, 394, https://doi.org/10.1038/s41597-020-00739-0

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