ARIES: Energy Systems
10th June 2014, ARCC Assembly, Birmingham
Prof Phil Banfill & Prof Gareth HarrisonCentre of Excellence in Sustainable Building DesignHeriot-Watt University.Institute for Energy Systems, University of Edinburgh.
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?
Energy Supply
Transmission/ Distribution
Energy Demand
Change of resource (e.g. wind/tidal/solar)
Ability of generation portfolio to react
Effect of climate shocks on system
Reduced heatingIncreased coolingNew technologiesChange in peak demand
Climate Demand-side drivers
Energy generation
Building stock
Behaviour Micro-gen HVAC tech
Top-down descriptions
Bottom-up descriptions
Ensure these complement each other
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Current
The effect of future scenarios on demand:Power demand over a 24 hour period
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Current
Future 1
Energy efficient lighting, 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
Continuing rise in consumer electronics?
Continuing rise in consumer electronics?00
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Climate Change?
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
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
Synthesizing electrical demand profiles
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Elec
tric
al D
eman
d (k
W)
Small number of real dwelling profiles
Synthetic profile generator (using Hidden Markov
Modelling)
n x individual dwelling synthetic profiles
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Dem
and
per d
wel
ling
(kW
)
Aggregated multi-
dwelling profile
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Dem
and
per d
wel
ling
(kW
)
Single dwelling
9 dwellings
45 dwellings (synthetic)
90 dwellings (synthetic)
Diversity effect in electrical demand profiles
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)
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.
Processing Information
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
Ther
mal
dem
and
• 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
The Supply Side
18
E.G. Solar Radiation / PV OutputBaseline, 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
The Supply Side
Pow
er
Pow
er
Pow
er
Pow
er
time
time
time
time
Wind
Solar
Wave
Wave
The Supply Side
Cap
acity
Technology
Cap
acity
TechnologyCap
acity
TechnologyC
apac
ity
TechnologyPow
er
…and match with demand
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
Climate scenarios (e.g. UKCP’09)
Offsite generation (e.g. % renewables)
Grid C.I. kgCO2/kWh
Micro-gen (e.g. solar panels)
Infrastructure/ transmission
(e.g. cables/wires, LV transformers)
ClimateDemand-side
Supply/distribution-side
Transport(e.g. elec vehicles)
Working patterns (e.g. home-
working)
Non-domestic heating
(e.g. boiler tech)
Non-domestic non-heating
(e.g. IT usage/tech)
Domestic heating (e.g. heat pumps)
Domestic non-heating (e.g. consumer
electronics growth)
Domestic Building typology
(e.g. new build and retrofit targets)
Non-Domestic Building typology (e.g. new build and
retrofit targets)
So now we just have to put them all together!!
[email protected]@ed.ac.uk