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ARIES: Energy Systems

ARIES: Energy Systems. 10 th June 2014 , ARCC Assembly, Birmingham Prof Phil Banfill & Prof Gareth Harrison Centre of Excellence in Sustainable Building Design Heriot-Watt University. Institute for Energy Systems, University of Edinburgh. ARIES.

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ARIES: Energy Systems

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  1. ARIES: Energy Systems 10th June 2014, ARCC Assembly, Birmingham Prof Phil Banfill & Prof Gareth Harrison Centre of Excellence in Sustainable Building Design Heriot-Watt University. Institute for Energy Systems, University of Edinburgh.

  2. ARIES • Adaptation and Resilience In Energy Systems • University of Edinburgh (supply-side) and Heriot-Watt University (demand-side) • Modelling the effect of climate and future conditions on energy demand, supply and infrastructure • What problems might occur that are caused or exacerbated by climate change?

  3. Change of resource (e.g. wind/tidal/solar) Ability of generation portfolio to react Energy Supply Transmission/ Distribution Effect of climate shocks on system Reduced heating Increased cooling New technologies Change in peak demand Energy Demand

  4. Top-down descriptions Climate Demand-side drivers Energy generation Ensure these complement each other Bottom-up descriptions Behaviour HVAC tech Building stock Micro-gen

  5. The effect of future scenarios on demand: Power demand over a 24 hour period

  6. Energy efficient lighting, e.g. LED ?

  7. Charge cycle of electric vehicles?

  8. Continuing rise in consumer electronics?

  9. Continuing rise in consumer electronics? Climate Change?

  10. For this we need quite specific “scenarios”... • Archetypes of dwellings • Scottish Building Stock from Housing Surveys, and how these might change in the future • Apply these scenarios to “zones” of 500-6000 homes • Such bottom-up scenarios do not necessarily need to be paired with top-down scenarios • But we need to make sure they do not clash with them e.g. avoid high heat pump usage in higher grid carbon intensity scenarios

  11. How can we synthesize electrical demand profiles? • Individual dwelling demand profiles show a clear link with activity and technologies • Multi-dwelling demand profiles show periods of interest/concern for an energy supplier • Can a method utilise both of the above? • And demonstrate the effect of changing specific parameters on aggregated demand profiles • Particularly a challenge as high-resolution dwelling demand profiles are difficult to obtain in great number

  12. Synthesizing electrical demand profiles Small number of real dwelling profiles Synthetic profile generator (using Hidden Markov Modelling) n x individual dwelling synthetic profiles Aggregated multi-dwelling profile

  13. Diversity effect in electrical demand profiles

  14. Aggregated thermal demand profiles • Developed a method for dynamically simulating large numbers of dwellings (in IES-VE) • In effect, a Dynamic Local-Scale Stock model (DLSSM) • Accounts for important aspects of building physics but in a way that is suitable for extrapolation • Can look at effect of, e.g., large-scale changes in heating technology (in a warmer climate)

  15. A local scale stock model • Zones of Semi-dets, Dets, Terraces, Mid-terraces, Flats, etc • Zones of 1919-64, 1965-76, 1977-2002, etc • Building variants created and adjusted by floor area, % glazing, wall construction.

  16. Processing Information Thermal demand

  17. The Supply Side • Wide range of generation technologies commercially available now and even wider range by 2050 • These have diverse operational characteristics and response to changing climate • need to capture these robustly • Spatial pattern of generation deployment is important in credible scenarios • resource, economics, grid connection all have strong influence

  18. E.G. Solar Radiation / PV Output Baseline, relative change, and percentage change (from baseline) for 2050s medium emissions scenario with 50% probability Baseline - Summer months 2050s Medium Emissions 50% probability change (Wm-2) Summer months 2050s Medium Emissions 50% probability change (%) Summer months 18

  19. The Supply Side Wave Power time Wind Power time Wave Power Solar Power time time

  20. The Supply Side Capacity Technology …and match with demand Capacity Power Technology Capacity Capacity Technology Technology

  21. In conclusion we have... • An approach for modelling an aggregated thermal demand profile for a selection of buildings • A method for upscaling individual dwelling electrical demands to aggregated demands • A tool emulating the effect of climate on building simulations • A model for estimating the effect of climate on electricity transmission • Method demonstrating the effect of climate on renewable generation • Wind, Solar, Hydro, Tidal, Wave

  22. Non-Domestic Building typology (e.g. new build and retrofit targets) Offsite generation (e.g. % renewables) Grid C.I. kgCO2/kWh Micro-gen (e.g. solar panels) Working patterns (e.g. home-working) Domestic non-heating (e.g. consumer electronics growth) Non-domestic heating (e.g. boiler tech) Climate scenarios (e.g. UKCP’09) Domestic heating (e.g. heat pumps) Non-domestic non-heating (e.g. IT usage/tech) Domestic Building typology (e.g. new build and retrofit targets) Transport (e.g. elec vehicles) Infrastructure/ transmission (e.g. cables/wires, LV transformers) Climate Demand-side Supply/distribution-side

  23. So now we just have to put them all together!! P.F.G.Banfill@hw.ac.uk Gareth.Harrison@ed.ac.uk

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