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ARIES: Energy Scenario requirements 56 th Feb 2014, ARCC Scenarios Workshop Dr David Jenkins Centre of Excellence in Sustainable Building Design HeriotWaI University
ARIES: Energy Scenario requirements 5-‐6th Feb 2014, ARCC Scenarios Workshop Dr David Jenkins Centre of Excellence in Sustainable Building Design Heriot-‐WaI University
ARIES
� Adapta4on and Resilience In Energy Systems � University of Edinburgh (supply-‐side) and Heriot-‐Wa@ University (demand-‐side)
� Modelling the effect of climate and future condi4ons on energy demand, supply and infrastructure � What problems might occur that are caused or exacerbated by climate change?
Energy Supply
Transmission/ Distribution
Energy Demand
Change of resource (e.g. wind/4dal/solar) Ability of genera4on porKolio to react
Effect of climate shocks on system
Reduced hea4ng Increased cooling New technologies Change in peak demand
Synthesizing electrical demand profiles
� Individual dwelling demand profiles show clear link with ac4vity and technologies
� Mul4-‐dwelling demand profiles show periods of interest/concern for an energy supplier
� Can a method u4lise both of the above? � And demonstrate the effect of changing specific parameters on aggregated demand profiles
� Par4cularly a challenge as high-‐resolu4on dwelling demand profiles are difficult to obtain in great number
Synthesizing electrical demand profiles
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Electrical Dem
and (kW)
Small number of real dwelling profiles
Synthe:c profile generator (using Hidden Markov Modelling)
n x individual dwelling synthe4c profiles
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Demand pe
r dwelling (kW)
Aggregated mul4-‐dwelling profile
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Demand pe
r dwelling (kW)
Single dwelling
9 dwellings
45 dwellings (synthetic)
90 dwellings (synthetic)
Diversity effect in electrical demand profiles
Do synthe:c aggregated profiles mimic real data?
Aggregated thermal demand profiles
� Developed a method for dynamically simula4ng 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 way that is suitable for extrapola4on
� Can look at effect of, e.g., large-‐scale changes in hea4ng technology (in warmer climate)
• Text Box
Whin Ø Pre 1919 to 1964
Brick & Block (unins) Ø 1919 to 1975 Ø 1976 to 1983 Ø 1984 to 1991
Brick & Block (ins) Ø 1919 to 1975 Ø 1976 to 2002 Ø 2003 to 2008 onwards
Timber Ø 1950 to 1975 Ø 1976 – 2002 Ø 2003 to 2008 onwards
Concrete Ø 1919 to 1964 Ø 1965 to 1983
Sandstone Ø Pre 1919 to 1964 Dynamic Local-‐Scale Stock Model
Processing Informa:on
0%10%20%30%40%50%60%70%80%90%100%
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Time of Day (hh:mm) 21st Jan
SemiD
MidT
Flat
EndT
Detached
Thermal dem
and
For this we need quite specific “scenarios”...
� Archetypes of dwellings � Scodsh Building Stock from Housing Surveys, and how these might transform in the future
� Apply theses scenarios to “zones” of 500-‐6000 homes � Such bo@om-‐up scenarios do not necessarily need to be paired with top-‐down scenarios � But we need to make sure they do not clash with these overarching scenarios
� e.g. avoid high heat pump usage in higher grid carbon intensity scenarios
• Wide range of genera4on technologies commercially available now and even wider range by 2050
• These have diverse opera4onal characteris4cs and response to changing climate – need to capture these robustly
• Spa4al pa@ern of genera4on deployment important in credible scenarios – resource, economics, grid connec4on have
strong influence
The Supply Side
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Solar Radia4on
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
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Solar PV Output
Percentage change for 2080s high emissions scenario (10%, 50% & 90% probabilities)
What we have...
� An approach for modelling an aggregated thermal demand profile for a selec4on of buildings
� A method for upscaling individual dwelling electrical demands to aggregated demands
� Tool emula4ng effect of climate on building sims � A model for es4ma4ng the effect of climate on electricity
transmission � Method demonstra4ng effect of climate on renewable
genera4on � Wind, Solar, Hydro, CCGT, Nuclear, Coal, Tidal, Wave (nearly….)
Climate scenarios (e.g. UKCP’09)
Offsite genera:on (e.g. % renewables)
Grid C.I. kgCO2/kWh
Micro-‐gen (e.g. solar panels)
Infrastructure/ transmission
(e.g. cables/wires, LV transformers)
Climate Demand-‐side
Supply/distribu:on-‐side
Transport (e.g. elec vehicles)
Working paSerns (e.g. home-‐working)
Non-‐domes:c hea:ng
(e.g. boiler tech)
Non-‐domes:c non-‐hea:ng
(e.g. IT usage/tech)
Domes:c hea:ng (e.g. heat pumps)
Domes:c non-‐hea:ng (e.g. consumer
electronics growth)
Domes:c Building typology
(e.g. new build and retrofit targets)
Non-‐Domes:c Building typology (e.g. new build and retrofit targets)
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Current
The effect of scenarios on demand...
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Current
Future 1
Energy efficient ligh4ng, e.g. LED ?
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Current
Future 1
Future 2
Charge cycle of electric vehicles?
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Current
Future 1
Future 2
Future 3
Con4nuing rise in consumer electronics?
Con4nuing rise in consumer electronics? 00
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Climate Change?
The need to define an external scenario
� We have bo@om-‐up demand modelling methods � Millions of possibili4es of how these are applied so specific case-‐studies and scenarios are needed
� Geographical breakdown of � Genera4on � Infrastructure � Demand
� Highlight regionally-‐specific issues
The Supply Side
Pow
er
Pow
er
Pow
er
Pow
er
The Supply Side
Cap
acity
Technology
Cap
acity
Technology Cap
acity
Technology C
apac
ity
Technology Pow
er
…and match with demand
The need to define an external scenario
� Should we define scenarios that are: � Likely to happen? � Describe a mid-‐range of possibili4es? � Rela4vely extreme scenarios that test the limits of the systems being studied? – What breaks and when?
� Quite possible to point to a scenario that “fails” but is not likely to happen
� Can assign probability to climate scenarios but more arbitrary for non-‐climate parameters
An example
� Climate: UKCP’09, Medium-‐emission, � Loca:on: London, Regional info � Building types: % mix of low/zero-‐carbon dwellings � Technology: % use of heat-‐pumps in domes4c and non-‐domes4c sectors
� Onsite genera:on: Assumed GW/yr growth of PV � Demand response: Use of controls/storage, DSM and micro-‐grids to allow demand to follow supply
� Offsite genera:on: Assumed mix of renewables
Climate Demand-‐side drivers
Energy genera4on
Building stock
Behaviour Micro-‐gen HVAC tech
Top-‐down descrip:ons
BoSom-‐up descrip:ons
Ensure these compliment each other
