transforming utilities’ conversion points – tucp final ......the uk's average new car fuel...

Transforming Utilities’ ConversionPoints – TUCPFinal DisseminationFriends Meeting House, Euston Road, London

Liz Varga, Tonci Grubic, Paul Raven, Yvonne Hübner, Colin Taylor03.09.2013

Grant refEP/J005649/1

Objectives

Describe the project aims

Review the project contribution

Describe the conversion points concept

Review the data and partner contributions

Discuss the modeling work and results

State project outcomes and future plans

© Cranfield University 2013 2

Agenda

13.30 introduction to the project 13:40 the conversion point ontology 13:50 overview of the final model, including presentations from partners on data and

practices in industry

14:30 ** coffee/tea and networking 14:50 model experiments, outcomes 15:20 discussion 15:50 next steps, research outputs 16:00 finish


Project Overview

Started Sep 2011

18 months, extended by 6 months

Cranfield CSRC leadership Conversion Points Ontology

Agent Based Models

Four partners: Cranfield IVHM – power

Newcastle – EV transport

Sheffield – water

Bristol – telecommunications


EPSRC grant aims

This project looks across utilities atinterdependencies and efficiencies of infrastructure atpoints of energy conversion.

The hypothesis is that changes to these conversionpoints are critical to systemic sustainability andadaptability.

We focussed on domestic demand as homes are a conversion point for all

utilities micro-level technologies power, mobility and water services a nationally representative sample


Infrastructure


From the Institution of Civil Engineers (2009, p. 2): “(I)nfrastructure refers to the physical assets

underpinning the UK’s networks for transport, energygeneration and distribution, electronic communications,solid waste management, water distribution and wastewater treatment.”

These assets are networked to provide servicesand include micro-level assets such as boilers

There is ‘agency’ in all life-cycle stages ofinfrastructure: different agents use infrastructuredifferently

Agents

Chappin and Dijkema (2007)

EPSRC grant aims

This project cuts across all utilities addressinginterdependencies, efficiencies, and wastecreated at points of utility infrastructureconversion.


Peerenboom et al (Little, 2005)

Conversion PointOntology

Explicit specification of a conceptualization(Gruber,1993)

For TUCP – the conceptualization is of pointsof critical infrastructure

The representation needs to reflect what‘exists’


GRUBER, T. R. 1993. A translation approach to portable ontology specifications, knowledgeacquisition. Special issue: Current Issues in Knowledge Modelling, 5:2, 199–220.

Conversion Point


A conversion point is a plant or device or distributionroute which converts a resource into another resourceor service, from extraction or capture through to enduser services.

Figure 1: Energy systems conversions (European Parliament, 2006, p5)

Conversion pointontology

Example

Conceptual scales

• This conceptualization is flexibleenough to represent a the use oftechnology to provide services atany level of description

• We can even imaginealternatives for the service ‘Washclothes’ such as ‘laundryservices’ which might requiremobility to collect and deliverclean clothes, and a bulk washingservice by a third party.

Energy SystemConversions

Interdependencies

Interdependencies in a landscape of conversionpoints can be identified by looking for the presence ofconversion points with similar attributes:

1. the same resources used: identifies resourceinterdependency,

2. the same policies: identifies governanceinterdependency,

3. the same technologies: identifies technologicalinterdependency (and potential for technologicalefficiency improvement),

4. the same geographical coordinates: identifieslocational interdependency.


ConversionPoints

Each conversion point is unique

Its success depends on: The technology and material used (and so its

efficiency and adaptability)

Demand (and so its utilization)

The type of natural resource processed (and soits carbon emissions and resource implications)and the institutions governing this

Its operational mode (and so its economics andadaptability)

Co-evolution of thesystem

Foxon (2010)

EPSRC methodology


Our methodology to deliver this research this is to

1. Create a map of current conversion points2. Create a base-line agent based model to demonstrate

the sustainability and adaptability of the current nationalutilities infrastructure

3. Run a stakeholder event – to prioritize scenarios andtechnologies

4. Develop the base-line model to include the futurescenarios

5. Run a stakeholder event – to review the results offutures model

Project events

project kick off and first stakeholder event tookplace on 10th October 2011

second stakeholder event was held on 30March 2012

final dissemination and stakeholder event 3Sep 2013

10 full-day team meetings

project portal -https://www.cranfieldccedportal.com/tucp/Pages/default.aspx


Agent based models

Base-line – demand driven, Yorkshire basedmodel

Version 2 – energy hub

Version 3 – mobility, water technologies,domestic power

Transition

Chappin & Dijkema, 2010

Data collection

National Regional Local Case study area

Have

Energy

Gas Have some

Electric

Coal Required

Oil

Water

Waste water

Transport

Roads

Petrol stations

EV points

Rivers

Canals

Refuse

Communications

Electricity

Water systemconversions Raw water

resource

Final water storage

Clarification

Flotation

Aeration&

Pre treatment

Flocculation&

Coagulation

AbstractionScreening

Coarse filter

ReservoirBoreholeSpring

River

Blending

Clarification

Sedimentation

Filtration

Distribution

WRAPReource

optimisation

Consumption

Networkmodelling &optimisation

Flow & pressuremonitoring &

reporting

SCADA& controlsystems

Sludge productionand / or

sewerage systemWastewaters

Processmodelling &optimisation

WWTP

Waste waterconversions

Transport- National

TransportYorkshire case studyarea

Combined infrastructuresdata plot

Emissions

Gross annual operational GHG emissions

Direct emissions from burning of fossil fuels (including natural gas CHP generated onsite) (tCO2) 18,125

Process and fugitive emissions (tCO2) 64,097

Transport: Company owned or leased vehicles (tCO2) 10,866

Total grid electricity used by company (including CHP electricity purchased) (tCO2) 321,065

Business travel (public transport and private vehicles used for company business) (tCO2) 820

Outsourced activities (if not included in Scope 1 or 2) Energy and other (tCO2) 5,633

Gross operational emissions (tCO2) 420,606

Net annual operational GHG emissions

Exported renewables (generated onsite and exported) (tCO2) -284

Green Tariff electricity purchased (tCO2) 0

Net operational emissions (tCO2) 420,323

Annual operation emissions derived from energy use (tCO2) 364,339

Annual operational GHG intensity ratio values

Operational GHG emissions per Ml of treated water (kg.CO2.Ml-1) 327

Operational GHG emissions per Ml of sewage treated (treated discharge) (kg.CO2.Ml-1) 378

Operational GHG emissions per Ml of sewage treated (water distribution input) (kg.CO2.Ml-1)730.6

The map is not the territory:challenges in Agent-BasedModelling of interdependentinfrastructures

Paul Graham Raven / John Machell / JobyBoxall

Pennine Water Group / UoS

Magritte’s advice for modellers...

A: The data you want, and thedata you get

Desired:

Maintenance costs

Opex costs

De- and recommissioning costs

Asset efficiency ratings

Utilisation %

Average asset ages

Manpower required

Volumes treated, delivered

Leakage

Network length

Energy consumed

Available (OFWAT):

(annual, by service)

(ditto)

(unavailable)

(unavailable)

(unavailable)

(unavailable)

Proxy: employment costs, annual

Volumes treated, delivered

Leakage

Network length

Proxy: energy costs, annual

B: Doing the math

I: Granularity - feeding finemodels with coarse data

Model:

● Short periodicity

● 100 - 1,000 households

Data:

● Annual aggregates

● >2x10^6 households

I: Granularity - feeding finemodels with coarse data

● No supplementary fields, so can’t “drill down” or slice through data for specificneeds

● Desired data must sometimes be inferred or extrapolated from proxy data

● Proxy data may itself be inferred or extrapolated… or even massaged, fudgedor faked.

II: Non-linearity - plottingfunctions from a single point

Calculated a value for energy consumed pervolume of sewage through system; how doesenergy consumption change with volume?

ENERGY

VOLUME

II: Non-linearity - plottingfunctions from a single point

How to get more points for the function plot?

● Can’t experiment; huge operational system

● Historical data insufficiently deep, lacking in context; can’t control for external factors

● Can’t compare with other systems; function is comparable, but context is not

(When dealing with multiple interdependent infrastructures, these problems arefurther subject to network effects and entanglement -- which is to say thatthe problems don’t add, they multiply.)

III: Heterogeneity: a tale of two (ormore) cities

Sources of volume into seweragesystem: domestic, industrial/municipal,agricultural, surface run-off, flood-typeevents / weather transients

Contextual factors for volume-to-sewers:asset type, asset mix, asset age; climate(eg rainfall, temperature, insolation,seasonal variance); geology, topography;land use (absorbance factors); populationdensity, housing density, household size...

Dr. Tonci Grubic, Dr Liz Varga& Stephen Varga

Transforming Utilities’Conversion Points(TUCP) - Agent BasedModel (03/09/2013)

Presentationoutline

• Overview of the final model

• Assumptions, user interface, demonstration

• Model experiments

• Experimental design

• Outcomes

Assumptions


Characteristic Definition

Household size Between one and six uniformly distributed.

Electricity (100% at the moment), gas

user (85% at the moment)

Energy sources used to meet energy

demand for cooking, hygiene, and washing.

Assumption, we get 11.2kWh per m3 which

generates 0.203 kg of CO2 per kWh (based

on carbonindependent.org)

Rain water harvesting (negligible or

0% at the moment), recovering waste

heat (0%), grey water recycling (0%),

solar thermal water heating (0.4%),

photo voltaic (2%), smart meter (4%),

EV (0% at the moment)

Generation technology used by the

household. Water and energy harvested

through these technologies is used in

meeting the household’s demand for

transportation, energy, and water.

Environmental priority, economic

attribute, location of living, charging

at work, roof area, photo voltaic area

Other household characteristics.

Assumptions - demand


Characteristic Definition

Miles demand The baseline demand is 50-200 miles per week/per car. This demand is corrected for the

household’s location of living (if urban then 30-60% below the baseline demand, if rural then

30-60% below the baseline demand). This demand is met either through private,

gasoline/diesel cars or EVs, or public transportation means, diesel buses, diesel and electric

trains. Demand for these utilities is then recorded.

Water demand Includes demand for drinking (250-300l per person/month), toilet flushing (1000-1500l per

person/month), hygiene (800-1200l per person/month), and washing (1200-1500l per

person/month). This data is based on published consumption figures (Waterwise).

Energy demand Includes demand for cooking (10-25kWh per person/month), hygiene (10-20kWh per

person/month), and washing (40-90kWh per person/month). This data is based on published

consumption figures (Palmer and Cooper, 2012; decc, 2012). Energy demand also includes

energy to charge an EV where a modern EV consumes 0.2-0.3 kWh per km.

Number of cars Based on data about household car availability for period 1985/86-2010 (Department for

Transport statistics). 0 cars/household - 25% of population, 1 car/household - 17% of

population, 2 cars/household - 49% of population, 3 cars and more/household - 9% of

population. The UK's average new car fuel consumption in 2010 was 5-7 litres (5.4 for diesel

vehicles and 6.4 for petrol) per 100km with 1l of petrol/diesel generating between 2.2kg and

3kg of CO2.

Assumption -infrastructure

Infrastructure Definition

Electric power

generation and

distribution

Technology mix used for generation of electricity is: coal (40%), gas (30%), nuclear (20%), and

wind (10%). Their levelised electricity generation costs (LEGC - the lifetime discounted cost of

ownership of using a generation asset converted into an equivalent unit cost of generation) and

carbon emissions are: coal (0.03-0.047 £/kWh, 900-1100 g/kWh), gas (0.032-0.039 £/kWh, 400-

600 g/kWh), nuclear (0.034-0.067 £/kWh, 5-10 g/kWh), and wind (0.051-0.091 £/kWh, 20-25

g/kWh). This data is based on Tidball et al. (2010) for costs and (paulchefurka.ca/Electric Cars and

CO2.html) for carbon emissions.

Distribution of electricity is not covered in our model, i.e. it is assumed that distribution is

without any losses.

Water

treatment,

distribution,

and sewerage

Energy required and carbon emissions: treatment (0.135kWh/m3, 0.327kgCO2/m3), distribution

(0.44kWh/m3, 0.126kgCO2/m3), and sewerage (0.07kWh/m3, 0.583kgCO2/m3). Total energy

consumption here is corrected for the amount of electricity generated from sludge processing

and other renewable sources (between 14% and 30%). This is based on a real world case study.

Transportation Diesel/gasoline used by public transport is 26miles/litre for a bus and 48miles/litre for a diesel

train (based on House of Commons Hansard written answers for 20 July 2005), electricity used by

an electric train is 0.108 kWh/passenger km (based on 'Baseline energy statement - energy

consumption and carbon dioxide emissions on the railway' from March 2007 - The voice of the

passenger railway).

Algorithms

User interface

Resultsdashboard

Electric vehiclesYvonne Hübner and Phil BlytheNewcastle University

Possible barriers to EVuptake

0%10%20%30%40%50%60%70%80%90%

100%

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ExtremelyimportantImportant

Switch EV Trails

Total number of journeys: 65,000Total number of charges: 17,000

260 answers to questionnaires58 participants attended 8 focus groups23 individual exit interviews

TUCP Assumptions

The key barriers to the uptake of electricvehicles are:

1) Cost of the vehicle

2) Limited driving range of the vehicle

3) Limited availability of public charging infrastructure

TUCP Model / Researchquestions

TUCP ResearchQuestions

1. How will incentives influence the uptake ofEVs?

2. How does advertisement / word of mouthinfluence the uptake of EVs?

3. How does an increased uptake of electricvehicles influence electricity demand?

Results: Impact of cost

Economic factor Uptake of EVs

10% 23

50% 7

100% 0

Results: Impact of EVrange

% of journeys that can be met by EV Uptake ofEVs

40% 2

90% 7

100% 13

% of journeys that can be met by EV Uptake ofEVs

40% 5

90% 11

100% 8

Work place charging posts: 10%

Work place charging posts: 100%

The real world

National strategy

• £300m for the Plug-In Car Grant

• Favourable tax regime, with plug-in vehicles receivingVehicle Excise Duty and Company Car Tax exemptions, aswell as Enhanced Capital Allowances

• £30m available to match-fund eight pilot projects installingand trialling recharging infrastructure in the UK to install upto 8,500 charge points

North East of England:• 12 quick charge points• 700 standard points• 390 home charge points

• For over 90% of the time driving in the North East, the EVis within 5km of a charging point.

The real world (2)

Year All ultra-low carbon

cars registered in the

UK

New car

registrations in

the UK

2010 272 1,996,300

2011 1,209 1,907,400

2012 2,621 2,010,800

45.1% 40.1%

5.4% 9.4%

Petrol Diesel Alternativefuels andhybrids

Don't know

Responses to the question "What typeof fuel will you be most likely to choose

when you buy your next car?"

(AA Populus Panel)

(DfT Statistics)

Results: Impact of ‘wordof mouth’

0.0%5.0%

10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%

Yes, as myprimary car

Yes, but as asecond car only

No

Would you consider buying an EV?

Advertisementfactor

Uptake of EVs

0 5

5 7

10 9

20 6

I saw the EV actually a couple ofdays ago because our Chief Exec’s

decided to drive one and I wasactually surprised at the size of it.

It looks a lot chunkier than Iwould have expected.

Results: Environmentalpriority

Environmental priority Uptake of EVs

10% 14

50% 49

100% 122

“Utopia” Uptake of EVs

998

Results: Electricity demand

Utopia Scenario Results

Number of EVs 998

Number of PVs 960

Number of smart meters 997

Gas total 3,341,865 m3

Electric total 105,013 MWh

Gasoline total 9,270 m3

Carbon total 67,685 t

Agenda

13.30 introduction to the project 13:40 the conversion point ontology 13:50 overview of the final model, including presentations from partners on data and

practices in industry

14:30 ** coffee/tea and networking 14:50 model experiments, outcomes 15:20 discussion 15:50 next steps, research outputs 16:00 finish


Modelexperiments

Experiments are used to study theperformance of processes and systems

Experimentaldesign

• The general approach to planning and conductingthe experiment is called the strategy ofexperimentation

• In planning for the experimentation we followedseven step approach proposed by Coleman andMontgomery (1993) :

(1) Recognition of and statement of the problem(2) Choice of factors and levels(3) Selection of the response variable(s)(4) Choice of experimental design(5) Conduction of the experiment(6) Data analysis, and(7) Conclusions and recommendations

Modelexperiments (1)

• What conditions may lead to large-scaleadoption of household generationtechnologies?

• The intention of experiments is to explorecombination of factors which wouldmaximise number of technologies in thesystem

1. Recognition of and statementof the problem

2. Choice of factors and levels

Factor Low Level High Level

(A) Environmental

priority

30% 70%

(B) Location of

living

80% urban 90% urban

(C) Charging at work 10% 50%

(D) EV battery range 40% 80%

(E) Economic factor 90% 50%

(F) Reduction of

gasoline cars by 2050

50% 100%

(G) Advertisement 1% of total 5% of total

(H) UK railway

journeys

60% (electric)/40%

(diesel)

80% (electric)/20%

(diesel)



• Average number of technologies adopted in themodel and average number of technologies perhousehold

• We also monitored the following system variables:(1) total water consumption (m3)(2) total gas consumption (m3)(3) total gasoline consumption (m3)(4) total electricity consumption (MWh)(5) total carbon (tonnes), and(6) total cost (millions £)

• We selected fractional factorial design ( )

3. Selection of the response variable(s)& 4. Choice of experimental design


5. Conduction of the experiments& 6. Data analysis

A B C D E F G H

Technology

uptake70%

No

effect

No

effect

No

effect50%

No

effect

No

effect

No

effect

Water

consumption70%

No

effect

No

effect

No

effect50%

No

effect

No

effect

No

effect

Gas

consumption

No

effect

No

effect

No

effect

No

effect

No

effect

No

effect

No

effect

No

effect

Gasoline

consumption

No

effect90%

No

effect

No

effect

No

effect100%

No

effect

No

effect

Electricity

consumption70% 90% 10% 40%

No

effect50%

No

effect

60%/40

%

Carbon

emissions70% 90%

No

effect40%

No

effect50% 1%

60%/40

%

Cost70% 90% 10% 40% 50% 50% 1%

60%/40

%


7. Conclusions and recommendations

Factor Proposed Current

(A) Environmental

priority

70% 30%

(B) Location of

living

90% urban 80% urban

(C) Charging at work 10% 10%

(D) EV battery range 40% 40%

(E) Economic factor 50% 90%

(F) Reduction of

gasoline cars by 2050

50%/100% 20%

(G) Advertisement 1% of total 1% of total

(H) UK railway journeys 60% (electric)/40%

(diesel)

60% (electric)/40%

(diesel)



System response Current

Proposed with 50%

of gasoline cars in

2050

Proposed with 0% of

gasoline cars in 2050

Average over 10 runs Average over 10 runs Average over 10 runs

Total water (m3) 6062586.325 6004711.721 5893023.294

Total gas (m3) 3861532.476 3871474.414 3811934.219

Total gasoline (m3) 19984.2592 14706.653 11237.41723

Total electricity (MWh) 115053.3929 117380.8058 116687.4104

Total carbon (tonnes) 78933.9617 80272.78875 79649.29109

Total cost (in millions

£) 4.982 5.081 5.041

Average number of

technologies in the

model 10.1 69 68.2

Average number of

technologies per

household 0.0101 0.069 0.0682


• It seems that:

• promoting and creating conditions which wouldlead to higher uptake of household generationtechnologies may not be good

• proliferation of charging stations and electric trainjourneys, increase in EV battery range, andremoval of gasoline cars, would lead to highercosts, and significantly higher electricityconsumption and carbon emissions

• Unless we make changes in part of the systemresponsible for electricity generation – 2nd round ofexperiments



• To what extent may adoption of householdgeneration technologies affect interdependentinfrastructures?

• The purpose of experiments is to explorecombination of factors which would minimise theeffects of adopting household generationtechnologies on the six system variables

1. Recognition of and statementof the problem



Factor Low Level High Level Centre

point

Axial points

(A) Reduction of gasoline

cars by 2050

50% 100% 75% 63%, 69%, 88%,

and 97%

(B) Reduction of gas users

by 2050

30% 60% 45% 38%, 44%, 53%,

and 57%

(C) Reduction in coal

generated electricity by

2050

30% 60% 45% 38%, 44%, 53%,

and 57%

(D) Increase in wind


2050

5% 10% 7.5% 6%, 8%, and 9%

(E) Increase in nuclear


2050

3% 6% 4.5% 4%, and 5%



Factor Effect

(F)Steady decrease in cost of

generation technologies

By 2050 almost half of households would be able to afford

generation technologies, whereas today this is possible only for

less than 10% in case of EV or PV technologies.

(G) Steady increase in the

number of households with

environmental attribute

By 2050 number of households with environmental attribute

would more than double, from around 30% today to 70%,

meaning there will be more people who are prepared to actively

participate in reducing their environmental effects.

(H) Steady increase in the EV

battery range

By 2050 the battery range would double relative to its

contemporary value.

(I) Steady proliferation of EV

charging stations

From almost negligible number of charging stations today, by

2050 we expect there to be at least 10% of charging stations

relative to the population.

(J) Steady increase in the

number of journeys by electric

trains

By 2050 there will be at least 20% more journeys by electric

trains than today (around 60%).

(K) Steady increase in the

number of urban dwellers

By 2050 there will be at least 10% more people living in urban

areas than today (around 80%).


• Average number of technologies adopted in the modeland average number of technologies per household

• We also monitored the following system variables: (1)total water consumption (m3), (2) total gasconsumption (m3), (3) total gasoline consumption(m3), (4) total electricity consumption (MWh), (5) totalcarbon (tonnes), and (6) total cost (millions £)

• We selected full factorial design plus centre and axialpoints runs with two blocks (low and high adoptionrate)

3. Selection of the response variable(s)& 4. Choice of experimental design


5. Conduction of the experiments& 6. Data analysis

(A) Reduction

of gasoline cars

by 2050

(B) Reduction of

gas users by

2050

(C) Reduction in

coal generated

electricity by

2050

(D) Increase in

wind generated

electricity by

2050

(E) Increase in

nuclear

generated

electricity by

2050

Water

consumptionNo effect No effect No effect No effect No effect

Gas

consumptionNo effect 60% No effect No effect No effect

Gasoline

consumption100% No effect No effect No effect No effect

Electricity

consumption50% 30% No effect No effect 3%

Carbon

emissions50% 30% 60% 10% No effect

Cost 50% 30% No effect 5% 3%

Technology

uptakeNo effect No effect No effect No effect No effect



Factor Proposed Benchmark

(A) Reduction of gasoline

cars by 2050

50% 30%

(B) Reduction of gas

users by 2050

30% 10% increase

(C) Reduction in coal


2050

60% 45%

(D) Increase in wind


2050

5%/10% 5%/10%

(E) Increase in nuclear


2050

3% 3%



System response

Proposed with

low adoption

rate and wind

increase of 5%

Proposed with

high adoption

rate and wind

increase of 5%

Proposed with

low adoption

rate and wind

increase of

10%

Proposed with

high adoption

rate and wind

increase of

10%

Total water (m3) 6.060E+06 5.978E+06 6.079E+06 5.965E+06

Total gas (m3) 3.324E+06 3.255E+06 3.319E+06 3.264E+06

Total gasoline (m3) 1.617E+04 1.592E+04 1.588E+04 1.601E+04

Total electricity (MWh) 1.228E+05 1.236E+05 1.236E+05 1.235E+05

Total carbon (tonnes) 7.227E+04 7.246E+04 7.125E+04 7.103E+04

Total cost (in millions £) 5.411E+00 5.435E+00 5.537E+00 5.556E+00

Average number of

technologies per

household 0.22 0.58 0.20 0.66



System

response

Benchmark

with low

adoption rate

and wind

increase of 5%

Benchmark

with high

adoption rate

and wind

increase of 5%

Benchmark

with low

adoption rate

and wind

increase of 10%

Benchmark

with high

adoption rate

and wind

increase of 10% Worst - Best

Total water

(m3) 6.033E+06 6.002E+06 6.082E+06 6.029E+06 117,035.30

Total gas (m3) 4.090E+06 4.126E+06 4.158E+06 4.160E+06 904,411.72

Total gasoline

(m3) 1.742E+04 1.774E+04 1.787E+04 1.741E+04 1,991.05

Total electricity

(MWh) 1.130E+05 1.146E+05 1.133E+05 1.152E+05 10,542.23

Total carbon

(tonnes) 7.122E+04 7.206E+04 7.039E+04 7.118E+04 2,071.48

Total cost (in

millions £) 4.977E+00 5.050E+00 5.097E+00 5.171E+00 0.58

Average no. of

technologies

per household 0.20 0.56 0.18 0.58 0.48


• It seems that:

• the highest adoption of household generationtechnologies are achieved by proposed (0.66) andbenchmark (0.58) configurations with high adoptionrates which is expected though

• the worst results, in terms of electricity consumptions,carbon emissions, and costs, are achieved by theproposed configurations

• when it comes to electricity consumption, carbonemissions, and cost, benchmark configurations with lowtechnology adoption level (0.18 and 0.2) produce thebest results



• Translated in language of our five control factors, this meansthe following:

• In terms of electricity consumption, carbon emissions, andcost, modest level of reduction (30%) in gasoline cars by2050 is better than more significant reductions, e.g. 50% or100%.

• It also seems that increase in gas users is better than theirreduction.

• All this results from conditions with very modest level ofuptake of household generation technologies and not toosevere reductions in coal generated electricity compared tothe proposed configurations



• In the context of current technology mix used in the UK forthe generation of electricity and plans to change that mix inthe future, large-scale adoption of household generationtechnologies can only make things worse

• If we are to see some tangible benefits from large-scaleadoption of these technologies, the technology mix wouldhave to be changed radically (investing in higher levels ofwind and nuclear generated electricity)

• Reductions in electricity consumption, carbon emissions,and costs may be achieved with proliferation of gas usersand further efficiency improvements in gasoline cars andgas boilers


Discussion – Colin Taylor

Boundaries/scope

Data

Algorithms

Visualization

Further testing:

geographical?

scale?


Outputs and impact

Conference (peer-reviewed) papers: Varga, L., Camci, F., Boxall, J., Toossi, Machell, J., Blythe, P.T., Taylor, C. Transforming Critical

Infrastructure: Matching the Complexity of the Environment to Policy ECCS 2012

Varga, L., Attitudes & Behaviours in Utility Systems, ARCC CN – Birmingham 2012 -

Varga, L., Grubic, G., Greening, P., Varga, S. Critical Infrastructure and Social SystemsCoevolution, Complexity Interface 2012

Toossi, A., Camci, F., Varga, L., IEEE International Conference on Industrial Technology (ICIT2013) - Developing an AHP based decision model for energy systems policy making

Varga, L., ISGNI 2013 – Sydney Australia 2013 Action in 8 domains for an 8 factor improvement:infrastructure with agency

Gonzalez, J. de Durana, J. Barambones, O., Kremers, E. Varga, L., Agent based modelling of localenergy networks as instances of Complex Infrastructure Systems, ECCS’13

Raven PG, Machell J, Boxall JB, “Choice infrastructure: non-engineering strategies for behaviourchange around domestic water and sewerage use”; 7th International Conference on SewerProcesses & Networks, Uni. Of Sheffield, 28-30/8/2013

Raven PG, Machell J, Boxall JB, “Worlds that tell stories, stories that tell worlds: diegesis andmimesis in science fiction and technoscience”, Science in Public 2013, Nottingham, 22-23/7/2013

Outputs and impact (2)

Academic papers: Robinson, A.P. Blythe, P.T., Bell, M.C., Hübner, Y., Hill, G.A. (2013), Analysis of electric vehicle driver recharging

demand profiles and subsequent impacts on the carbon content of electric vehicle trips, Energy Policy, 61, 337–348 http://www.sciencedirect.com/science/article/pii/S0301421513004266

Toossi, A., Camci, F., Varga, L. (2013). IEEE Developing an AHP based decision model for energy systems policymaking, IEEE International Conference on Industrial Technology (ICIT),1456-1460

Neaimeh, M., Hill, G.A., Hübner, Y., Blythe, P.T. (2013), Routing systems to extend the driving range of electricvehicles, IET Intelligent Transport Systems, July, 1–10

Varga, L., Grubic, T., Greening, P., Varga, S. Camci, F. (2013), Characterising Conversion Points and ComplexInfrastructure Systems: Creating a system representation for agent based modelling, Complexity, in revision

Varga, L., Camci, F., Boxall, J., Toossi, Machell, J., Blythe, P.T., Taylor, C. (2013), Transforming CriticalInfrastructure: Matching the Complexity of the Environment to Policy, International Journal of E-PlanningResearch (IJEPR), submitted

Bale, C.S.E., Varga, L., Foxon, T. (2013). Energy & Complexity: New Ways Forward, Applied Energy, submitted Karaca, F. Camci, F. “Soiling Reduction Analysis of Cultural Heritage through Adoption of Passenger Electric Cars”,

Journal of Cultural Heritage, submitted Grubic, T., Varga, L., Varga, S. (2013), Conditions and implications of large-scale adoption of household generation

technologies, Energy Policy, ready for submission Raven PG, Machell J, Boxall JB, “Choice infrastructure: non-engineering strategies for behaviour change around

domestic water and sewerage use”; Water Science & Technology submitted Grubic, T., Varga, L., Varga, S. (2013), How critical are household generation technologies for the UK national

infrastructures?, Technological Forecasting & Social Change, ready for submission Machell J, Raven PG, Boxall J, (2013), <as-yet-untitled paper proposing a system of electricity and water

conversion points>, under preparation Y. Hübner, P.T. Blythe, T. Grubic, F. Camci, J. Boxall, J. Machell, F. Karaca, P. Raven, L. Varga, Estimating the

demand for electric vehicles using the TUCP approach, under preparation Y. Hübner, O. Heidrich, G.A. Hill, P.T. Blythe, R.J. Dawson, Transport, EV and climate change mitigation strategies

in UK cities, under preparation


Practitioner papers and other dissemination:

Varga, L., Camci, F., Boxall, J. Toossi, A., Machell, J. March 2012 Conversion Points:An introduction, Stakeholder briefing note.

Varga L, Grubic T (2013) Transforming critical infrastructure. International Innovation,(Jul) 35-37.

Raven PG, “An introduction to Infrastructure Fiction”, Superflux, 27 June 2013<http://www.superflux.in/blog/infrastructure-fiction>

Raven PG, “An Introduction to Infrastructure Fiction”, Improve Reality 2013,Lighthouse, Brighton; 5/9/2013 [pending]

Raven PG, “Infrastructure fiction and cyborg anthropology”, #Stacktivismunconference, Lime Wharf, Hackney; 13/7/2013

Raven PG, special guest speaker/facilitator at Fractal'13 (public-access design fictionconference), Medellín, Colombia; 17-18/5/2013



Future funding

ECCS 2013 – satellite workshop IUS'13

Integrated Utility Services – Complex Models

ICIF – Jul 2013 – 3 years

International Centre for Infrastructure Futures

ARCC-CN – Assets sandpit project

Workshop at Cranfield and draft proposal

EU-InnovatE – 3.5 years starting 2014

European FP7 project under the Sustainable Lifestyles and the GreenEconomy in Europe theme

Relationships:

University of Bilbao, Vitoria, Spain

Public/partner web–site:https://www.cranfieldccedportal.com/tucp/Pages/default.aspx


Transforming Utilities’ ConversionPoints – TUCPFinal DisseminationThanks

03.09.2013

transforming utilities’ conversion points – tucp final ......the uk's average new car fuel...

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