Overview

• Broadly interested in spatial science, weather, renewables, python
• Predominantly Python based simulation models of the GB and European electricity systems
• Usually based on meteorological data from climate reanalyses
  • ​Gridded, modelled, historical datasets of historical weather, 30 - 50 km, hourly over 37 years
  • High degree of accuracy allows analysis of absolute generation and demand, variability and relationships between different weather driven elements of the system
• ​Also working at different scale/resolutions with data and spatial analysis
  • City energy and environment modelling through SiCEDS with EST etc.
  • Spatial analysis of air pollution

• PV output simulated using top of atmosphere and ground level irradiance and ground temperature data and a physical model
• All hours from 1980 simulated for entire grid - can be aggregated using any desired geography. Similar simulation carried out for wind.

Example 1 - Global PV simulation

Simulated monthly mean global capacity factors using 1980 meteorology

• The Stakeholder Interactive City Energy Demand Simulation tool was developed fro Birmingham and Exeter
• Data work on Non-Domestic stationary demand, air pollution and transport
• Team effort including the Energy Savings Trust

Example 2: City simulation

Projected Nitrogen Dioxide concentrations in London to 2030

Example 3: Spatial Analysis of roadside pollution and schools

•  Spatial analysis describing the proximity of all schools in GB to roads where the Nitrogen Dioxide levels have been measured and modelled.
• Used in a campaign by ClientEarth with a metric of schools within 150 m of roads exceeding the EU limit value for annual mean. Therefore likely causing health impacts.
• Widely covered in Media. Also I got paid.

Example 4: PhD modelling of residual demand

Publications:

• Thesis available at: https://goo.gl/ETYWiO   
• Blog: www.esenergyvis.wordpress.com 
• Sharp, E. Dodds, P. Barrett, M. and Spataru, C. Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information. Renewable Energy, 77, 527-538. 2015. https://goo.gl/gJrcR1
• Sharp, E. Spatiotemporal Disaggregation of GB Scenarios Depicting Increased Wind Capacity and Electrified Heat Demand in Dwellings. 38th IAEE International Conference, Antalya, Turkey, 2015.
• Sharp, E. Spataru, C. Barrett, M. and Dodds, P. Incorporating building specific heat loss and associated energy demand into electricity demand models for Great Britain. Building Simulation and Optimisation UCL, London, UK, 2014.

Contact etc.:

Email: ed.sharp@ucl.ac.uk

Linkedin: ed.sharp.09

Web: www.bartlett.ucl.ac.uk/energy

Twitter: @ucl_energy | @steadier_eddy