Sharp, RE;
(2016)
Spatiotemporal disaggregation of GB scenarios depicting increased wind capacity and electrified heat demand in dwellings.
Doctoral thesis , UCL (University College London).
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Abstract
National Grid’s future energy scenarios depict increased wind capacity and use of domestic heat pumps under four different pathways at a national annual resolution. The factors which will drive the resultant electricity generation and demand vary over significantly smaller resolutions in both space and time. This study presents a method which disaggregates these scenarios temporally to an hourly resolution and spatially to a 0.5o x 0.5o grid, which covers the GB land mass and offshore waters. The gridded framework facilitates the development of a wind generation simulation model, SpWind, and a hybrid energy demand simulation model, SpDEAM, that are both driven by climate reanalysis data, which provides spatiotemporally homogeneous and accurate hindcasted weather data over the 25 year period of the scenarios. A range of methods are identified and applied to disaggregate non spatial data and redistribute non gridded spatial data to the grid, which depict scenarios, and drivers of wind generation and energy demand. Evaluations of the reanalysis wind speed data, SpWind and SpDEAM demonstrate a reasonable degree of accuracy; the data, in combination with a gridded approach, is appropriate for simulating turbine output and electricity demand, though some uncertainty and error remains. Wind capacity and heat pumps are assigned to the grid, ensuring that each are exposed to realistic weather conditions. The implications of the scenarios on residual demand variability, geographical diversity and extreme events are explored in detail revealing the relative impact of different factors driving demand and supply.
| Type: | Thesis (Doctoral) |
|---|---|
| Title: | Spatiotemporal disaggregation of GB scenarios depicting increased wind capacity and electrified heat demand in dwellings |
| Event: | University College London |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Keywords: | Wind, GB, Spatiotemporal, Simulation, Python, Demand, Energy, Electricity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1476773 |
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