Ms Marianne Zeyringer, UCL Energy Institute, United Kingdom
Dr Dennis Konadu, UCL Energy Institute,United Kingdom
Dr James Price, UCL Energy Institute, United Kingdom
Dr Sheila Samsatli, Imperial College London, United Kingdom
Dr Zenaida Sobral-Mourao, University of Cambridge,United Kingdom
The UK is committed to reduce its GHG emissions by at least 80% in 2050 from 1990 levels (Parliament of the United Kingdom, 2008). Pacala, (2004) states that the portfolio of energy technologies to tackle the climate problem in the next 50 years is already industrially available. This assumption implies large scale deployment of nuclear energy and/or renewable energy sources (RES). The location of RES determines the total output and the timing of production. This means that the LCOE and integration costs are location dependent. There is a significant body of literature that analyses spatially the potential of RES in the UK: Gooding et al., (2013) for PV; Aylott et al., (2010), Tenerelli and Carver, (2012) and Lovett et al., (2014) for biomass energy; Drew et al., (2013) and Samsatli et al., (2016) for wind turbines. Evidence also exists in the literature that landscape is very important in British cultural identity and (Toke, Breukers, & Wolsink, 2008) decisions are influenced by minority groups (Bell, Gray, & Haggett, 2005).
The current literature however is yet to assess the potential of wind, solar and biomass energy including limiting factors affecting the deployment. To compare a large scale deployment of RES with other options it is important to discuss from the point of view of research, social attitudes and policy the potential and cost implications of defining land constraints to development. This will help to decide which decarbonisation strategies would be more in line with political and social considerations of land use. We close this research gap by: (1) developing a framework of scenarios for areas excluded from development; (2) using GIS to evaluate how these scenarios affect the physical potential; and, (3) estimate the costs of renewable energy deployment under the different land exclusion scenarios. First results show that the overall potential and costs of deployment of RES at large scale in the UK depend strongly on the assumptions of exclusion areas.
Keywords: renewable energy deployment, constraints, costs, GIS
Aylott, M. J., Casella, E., Farrall, K., & Taylor, G. (2010). Estimating the supply of biomass from short-rotation coppice in England, given social, economic and environmental constraints to land availability. Biofuels, 1(5), 719–727. http://doi.org/10.4155/bfs.10.30
Bell, D., Gray, T., & Haggett, C. (2005). The “Social Gap” in Wind Farm Siting Decisions: Explanations and Policy Responses. Environmental Politics, 14(4), 460–477. http://doi.org/10.1080/09644010500175833
Drew, D. R., Barlow, J. F., & Cockerill, T. T. (2013). Estimating the potential yield of small wind turbines in urban areas: A case study for Greater London, UK. Journal of Wind Engineering and Industrial Aerodynamics, 115, 104–111. http://doi.org/10.1016/j.jweia.2013.01.007
Gooding, J., Edwards, H., Giesekam, J., & Crook, R. (2013). Solar City Indicator: A methodology to predict city level PV installed capacity by combining physical capacity and socio-economic factors. Solar Energy, 95, 325–335. http://doi.org/10.1016/j.solener.2013.06.027
Lovett, A., Sunnenberg, G., Dockerty, T. (2014). The availability of land for perennial energy crops in Great Britain. GCB Bioenergy, 6, 99-107. doi: 10.1111/gcbb.12147
Pacala, S. (2004). Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies. Science, 305(5686), 968–972. http://doi.org/10.1126/science.1100103
Parliament of the United Kingdom. The Climate Change Act 2008, Pub. L. No. 2008 c 27 (2008).
Samsatli, S., Staffell, I., Samsatli, N. (2016). Optimal design and operation on integrated wind-hydrogen-electricity networks for decarbonizing the domestic transport sector in Great Britain. Int. J. of Hydrogen Energy, 41(1), 447-475. doi:10.1016/j.ijhydene.2015.10.032
Tenerelli, P., & Carver, S. (2012). Multi-criteria, multi-objective and uncertainty analysis for agro-energy spatial modelling. Applied Geography, 32(2), 724–736. http://doi.org/10.1016/j.apgeog.2011.08.013
Toke, D., Breukers, S., & Wolsink, M. (2008). Wind power deployment outcomes: How can we account for the differences? Renewable and Sustainable Energy Reviews, 12(4), 1129–1147. http://doi.org/10.1016/j.rser.2006.10.021
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