ARIES: Energy Systems

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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700

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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!!

P.F.G.Banfill@hw.ac.ukGareth.Harrison@ed.ac.uk