The spatiotemporal patterns of energy demand and supply in the UK
Ed Sharp, UCL
Both demand and supply characteristics are expected to change in the future, predominantly through changes in climate, loss of traditional fuels and demographic vicissitudes. This study intends to contribute to the understanding of potential future scenarios by modelling the spatiotemporal variations of both.
Existing research has focussed on what are perceived as the most prescient problems. Whilst this has been successful in modelling the impact of tractable technologies, the need to answer a specific question has resulted in a neglect of a number of important issues. Of these, the focus of this study will be the spatial disaggregation of models, particularly the demand side and the incorporation of increasingly sophisticated meteorological data, both measured and modelled.
Whilst it has been recognised that the spatial properties of supply are important, especially with respect to the smoothing effects of geographical diversity. The respective properties of energy demand have been neglected or ignored in all but a few studies. The intention of the research is to begin to address this problem. This will be done through the identification of available datasets and methods which allow the analysis of these in a spatially explicit manner. Some of the datasets that can be incorporated into the model are shown in Figure 1.
These are currently being reviewed to obtain a greater understanding of how the problem has been tackled. The likely outcome is that a combination of techniques will be used that will include demand profile adaptation to varying scales, interpolation of data from point to surfaces and aggregation of modelled building energy use data to the building stock divided into appropriate zones to provide spatially related hourly demand profiles for the UK.
Methods on the supply side are better developed and more sophisticated and therefore may follow what has been done in the literature, the best available datasets will be used, likely examples include NCEP CFSR. These methods and datasets will be used to create supply profiles that are spatially and temporally consistent with the demand side.
Demand and supply can then be compared at varying spatial scales. An example of the potential scale is shown in Figure 2 Complete research questions have not yet been developed. However a number of systems planning aspects could be investigated as well as the effects of spatial disaggregation on covariance between supply and demand based on the same drivers. Questions on the influence of meteorology on the field may also be an area of interest.
Data citations: Offshore data: Proudman Oceanographic Laboratory © Crown Copyright. All rights reserved 2008. Solar Data: PVGIS © European Communities, 2001-2008. Population: Centre for International Earth Science Information Network (CIESIN), Columbia University; United Nations Food and Agriculture Programme (FAO); and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3). Power stations: CARMA © Copyright 2007 Centre for Global Development. Demand Statistics: DECC sub national energy consumption statistics.
Keywords: Spatial, Energy, Disaggregate, Demand, Model, Modelling, Disaggregation, Intermittency, Electricity, Gas, Wind, Solar, Wave, Tidal, Heat, Photovoltaics, Population, Meteorology, Weather, Buildings, Environmental, GIS, Geographical Information Science, Spatially, Spatiotemporal, Geostatistics.
Bursary to attend the Royal Geographical Society Annual Conference
Awarded by the GIScience Research Group to postgraduates presenting quality work at the conference
Interdisciplinary experiences: a postgraduate perspective
Paper presented at the Royal Geographical Society Annual Conference. The presentation describes my experiences as a postgraduate in an interdisciplinary environment relating the positives and the negatives.
Life, Death and Gridded Population Datasets
A paper presented at the Royal Geographical Annual Conference 2012. The paper details a quantitative analysis of the divergence between global population datasets used in UK energy models.
Spatiotemporal disaggregation of energy demand supply matching models in the UK
Poster created for the LoLo annual colloquium 2012
The role of meteorological data in the spatiotemporal disaggregation of energy demand supply matching models
A poster describing my PhD in the context of meteorology, how it will be incorporated and what kind of products are available
Spatial disaggregation of demand supply matching
Poster completed for the LoLo summer stakeholder event
Lecture given to the EDE MSc course on how to analyse large datasets
A short introduction into the range of datasets available and the first steps to achieving a usable source and some meaningful analysis. Containing theory, an exercise and a case study