Urban Water Security Research Alliance Implications of - - PowerPoint PPT Presentation

Implications of Resource-Efficient Technology on Peak Water Demand and Water- Related Energy Demand

Rodney Stewart and Cara Beal

South East Queensland Residential End Use Project

20 June 2012

Urban Water Security Research Alliance

PRESENTATION SCOPE

• PART 1 -Water-related energy consumption

and savings

(Beal, C.D., Stewart, R.A., Bertone, E., (2012) Evaluating the energy and carbon reductions resulting from water- related stock efficiency. Buildings and Energy, under review.)

• PART 2 - Peak demand analysis of

SEQREUS

(Beal, C.D., Stewart, R.A., (2012) Identifying residential water end uses underpinning peak day and hour demand. Journal of Water Resources Planning and Management, under review.)

• PART 3 – General conclusions

PRESENTATION SCOPE PART 1 - Water-related energy consumption and savings

• Background - why do this work?
• Methods - for acquiring the water end use

data and energy data

• Results – energy and GHG emissions
• Results – intervention scenarios
• Conclusions – transferring findings into

practical policy outcomes

• The nexus of water and energy and GHG emissions well

recognised

• Adoption of water-efficient technologies is imperative in

reducing residential water-related energy demand

• Quantifying the energy savings from (hot) water-efficient

technologies has been largely based on modelled or assumed data (e.g. water use of fixtures/appliances, hot water system (HWS) type, % use of hot water)

• Therefore, aim is to determine water, energy and GHG

emission savings from resource-efficient household stock using:

• empirical water end use data
• detailed stock specifications and usage patterns

BACKGROUND – why do the study?

• The South-East Queensland Residential End-Use Study

(SEQREUS)

• Installation of smart meters and data loggers in 252

households in four regions of SEQ

• Produced a detailed registry of water end-uses (micro-

components) over 18 month period

• Information from household water stock – e.g. clothes

washing machines, hot water systems, shower flow rates etc.

• Data used to calculate energy demand from each water

appliance/fixture

• Savings determined from substituting with water/energy

efficient appliance/fixture

BACKGROUND – overview

STUDY AREAS

Sunshine Coast Regional Council (Noosa, Caloundra, Maroochy) Brisbane City Council Ipswich City Council Gold Coast

SEQREUS MIXED METHOD

Taken from aligned project CSIRO

• Energy demand determined using published* energy intensity

values (Wh/L) x known (measured) average water volume (L) of each end-use, for each home.

• Energy use values were converted into GHG emissions using

published GHG emission factors and methods presented in the Australian National Greenhouse Accounts report 2011

• A number of scenarios were devised to determine the impact
• n carbon emission reductions from various water and

energy-efficient technologies.

• Percentage savings from the base case scenario (worst case

scenario of no efficient strategies and electric HWS) were calculated when comparing to a range of sequentially applied water and energy efficiency intervention strategies.

METHODS –energy and GHG emission data

*Details: Beal, C.D., Stewart, R.A., Bertone, E (2012) Evaluating the energy and carbon reductions resulting from water-related stock efficiency. Buildings and Energy, submitted Apr 2012

• Clothes washers:

Table 1: Number of washing machines for each HWS, water connection and wash cycle category:

METHODS –energy and GHG emission data

Wash cycle temperature typical setting Hot water system type Electric cylinder Gas storage Solar (Electric Boosted) Single Dual Single Dual Single Dual Cold 23 86 2 8 8 22 Warm/Hot 8 23 2 7

‘Single’ refers to a single cold water tap connection to washing machine, where hot water is sourced from internal heating within the machine. ‘Dual’ refers to both a cold and hot water tap connection to washing machine, where the hot water is sourced from the external hot water service and not from internal heating.

• Hot water systems:

Table 2: Energy intensity and GHG emission conversion factors used for calculating GHG

emissions savings for HWS

1 Kenway et al. (2008) except heat pump values; 2DCCEE (2011) assuming 100% supply from coal-fired

power station; 3DCCEE (2011) for natural gas; 4assumes insufficient insolation for 10% of the year due to cloud cover (i.e. 0.038 (DCCEE 2001) + 0.1×1.00), 5heat pump energy intensity based on coefficient of performance, 6assumed a 50% reduction in coal-fired electricity generation (Blum et al. 2010, Lund et al. 2004).

METHODS –energy and GHG emission data

HWS type Number in sample (% total) Energy intensity (kWh/kL)A GHG emission factor (kgCO2e/kWh) Electric 177 (65) 126.80 1.0B Gas Cylinder 22 (8) 171.23 0.197C Gas Instant 11 (4) 85.60 0.197C Solar (electric boosted) 56 (21) 59.19 0.138D Heat pump 5 (2) 22.09E 0.500F

*Details: Beal, C.D., Stewart, R.A., Bertone, E (2012) Evaluating the energy and carbon reductions resulting from water-related stock efficiency. Buildings and Energy, submitted Apr 2012

METHODS –intervention scenarios

*Details: Beal, C.D., Stewart, R.A., Bertone, E (2012) Evaluating the energy and carbon reductions resulting from water-related stock efficiency. Buildings and Energy, submitted Apr 2012

Scenario number Intervention scenario Assumptions S1 Conversion to energy-efficient solar HWS a) Solar panels with electric-boosted storage system; b) direct replacement of electric HWS; c) long term average solar radiation data taken from Brisbane airport and assuming same characteristics across SEQ d) 38 days or 10% of year with insufficient insolation. S2 Water-efficient shower heads a) Substitute high flow shower head with low flow shower head of flow rate at 0.09 L/s; b) co-efficient of 1.2 applied to compensate for increased duration due to lower flows. S3 S2 + Water- efficient clothes washer a) CW internally heats cold water; b) front load only; c) cold water connection

• nly; d) directly substituting dual connected front or top load CW.

S4 S3 + Tap aerators a) Tap flow rate fixed value of 0.08 L/s (Australian Government 2011) S5 S4 + Shower temperature reduced to average of 37 C˚ a) Original shower temperature set at 40˚C (Flower 2009); b) existing shower head efficiencies (e.g. low or high flow roses) remain. S6 S5 + Energy- efficient dishwashers (DW) a) > 3 star rated machines considered ‘energy-efficient’; b) two efficiency clusters generated from SEQREUS data: ≤ 3 star and >3 star rated.

Table 3

RESULTS – water-related energy & GHG emissions

Dish washer 1.2 kL/p/y (3%) Clothes washer 8.9 kL/p/y (19%) Taps 9.5 kL/p/y (20%) Shower 13.9 kL/p/y (30%) Bathtub 0.5 kL/p/y (1%) Toilet 7.5. kL/p/y (16%) Irrigation 2 kL/p/y (4%) Leaks 3.2 kL/p/y (7%)

Water end-use breakdown

Dish washer* 81.9 kWh/p/y (5.6%) Clothes washer 104 kWh/p/y (7.1%) Taps 464 kWh/p/y (31.8%) Shower 810 kWh/p/y (55.5%)

(a) Energy - Electric cylinder (EC)

Dish washer* 81.9 kg CO2e/p/y (5.6%) Clothes washer 104 kg CO2e/p/y (7.1%) Taps 464 kg CO2e/p/y (31.8%) Shower 810 kg CO2e/p/y (55.5%)

(b) Carbon - Electric cylinder (EC)

Electric cylinder (coal-fired power) HWS

Dish washer* 81.9 kWh/p/y (10.3%) Clothes washer 125 kWh/p/y (15.8%) Taps 234 kWh/p/y (29.5%) Shower 351 kWh/p/y (44.4%)

(g) Energy - Solar electric boosted (SEB)

Dish washer* 81.9 kg CO2e/p/y (45.3%) Clothes washer 17 kg CO2e/p/y (9.4%) Taps 34 kg CO2e/p/y (18.8%) Shower 48 kg CO2e/p/y (26.5%)

(h) Carbon - Solar electric boosted (SEB)

Solar (electric boosted) HWS

RESULTS – energy intensity

• Clothes washer configuration important:

Warm/hot wash sourced from HWS = h energy use than internally heated water

• DW energy intensity h, but low water demand reduces overall use demand

Water internally heated by machine Hot water sourced from HWS Electric cylinder

RESULTS - Impacts of scenarios

S1 Conversion to energy-efficient solar HWS S2 Water-efficient shower heads S3 S2 + Water-efficient clothes washer (single, cold, front) S4 S3 + Tap aerators S5 S4 + Shower temperature reduced to average of 37 C˚ S6 S5 + Energy-efficient dishwashers (DW)

Electric cylinder - Energy Solar power (EB) - Energy

Cumulative reduction as each scenario applied

RESULTS - Impacts of scenarios

S1 Conversion to energy-efficient solar HWS S2 Water-efficient shower heads S3 S2 + Water-efficient clothes washer (single, cold, front) S4 S3 + Tap aerators S5 S4 + Shower temperature reduced to average of 37 C˚ S6 S5 + Energy-efficient dishwashers (DW)

Electric cylinder - Energy Solar power (EB) - Energy Solar power (EB) – GHG Emissions

RESULTS – Individual savings

Scenario Water reduction (%) Energy reduction (%) Solar HWS (EB)

• 46

Water-efficient shower head 37 63 Water-efficient clothes washer 27 87 Tap aerators 27 38 Shower temp reduced to 37C

• 13

Energy-efficient dish washer

• 28

Table 4. % individual savings (person/year)

 

In Qld it is mandatory to install water and energy-efficient fixtures in new buildings – ‘Queensland Development Code’

• Using disaggregated water end-use (micro-component) data +

detailed information on household stock can improve the resolution and accuracy of modelling water-related energy

• Water, energy and GHG emission savings can be achieved by:
• replacement of coal-fired power source to heat water with an

alternative (solar / gas);

• reduction of hot water use with low flow shower heads

water-efficient (high star rated) clothes washers, tap aerators,

• reduced reliance on HWS with stock that internally heats water

(dishwasher & clothes washers

• Results suggest that mandating water-efficient fixtures in building

codes and offering rebates for water-efficient appliances can be effective in reducing water-related energy

CONCLUSIONS – Policy implications

• Low flow shower head the most efficient and economical scenario

from a water and energy perspective, but…..

• Margin for savings will be dependent on existing penetration of

technology in homes (e.g. majority of homes in SEQ already have LF heads)

• Other strategies/interventions to reduce the high volume shower end

use may need to be considered (e.g. shower alarms/ feedback on specific end uses to customers)

• Retrofitting cheaper water-efficient technologies (low flow shower

heads, tap aerators) are still an effective means of reducing both water and energy consumption, regardless of hot water system type.

• Future work in this area is needed to expand the current research
• utcomes, such as the consideration of all thermal losses from

heating systems and the use of empirical energy end-use data.

CONCLUSIONS – Policy implications

• PART 2 - Peak demand analysis of

SEQREUS

• Background - Importance of knowing peak

demand and peaking factors

• Methods
• Results – peak end uses & peaking factors
• Conclusions – application of results

BACKGROUND

• A residential water supply system is a significant

infrastructure asset

• Optimal planning and design of this infrastructure is

critical.

• Accurate and up-to-date peak demand data is essential

to ensure that future mains water supply networks reflect current usage patterns and are designed efficiently from an engineering, environmental and economic perspective.

BACKGROUND

• A residential water supply system is a significant

infrastructure asset

• Optimal planning and design of this infrastructure is

critical.

• Accurate and up-to-date peak demand data is essential

to ensure that future mains water supply networks reflect current usage patterns and are designed efficiently from an engineering, environmental and economic perspective.

• Residential water consumption patterns typically

vary on both a daily basis, where an average day peak hour demand will occur, and on annual basis, where a peak day demand will occur.

• Annual peak hour demand that can be many

multiples of the average day peak hour consumption.

• Peaking factors – e.g. ratio of peak day : average

day demand PEAK DEMAND FACTORS

• Identify the water end uses driving peak demand
• Determine the peak day diurnal demand patterns and

relative frequency of peaking factors at an end use level

• f resolution.
• Determine ratios of peak to average day demand and

average day peak hour with peak day peak hour demand and compare with ratios reported in the literature.

• Explore how water-efficient technology can reduce

peak demand.

STUDY OBJECTIVES

• Timeline of total daily water consumption – SMIP database
• Can identify peak demand days
• Calculate peak day (PD) to average day (AD) ratios
• Estimate peaking factors – used in planning and design of water distribution

infrastructure e.g. pipe diameter sizing

Average and Peak Demand Analysis

Average and Peak Demand Analysis

PD/AD 1.2 PD/AD 1.7 PD/AD 1.3 PD/AD 1.5

Winter 2010 Summer 2010-11 Winter 2011

• Internal end

uses – e.g. CW & Shower, drive “small” peaks (peaking factors <1.5)

• External end

uses – e.g. irrigation, drive large peaks (factors > 1.5)

Peaking factor distributions

0.5 1 1.5 2 2.5 100 200 300 400 500 600 700 800 1 50 99 148 197 246 295 344 393 442 491 540 Peaking factor (PD / study AD) Total daily household consumption (L/hh/d) Number of days of data Average consumption (left) Median average consumption (left) Peaking factor (right)

Future peak demand /peaking factors

• Water efficient technology will reduce peak day demand
• Change in water use behaviour, ongoing water restrictions e.g. shift

in irrigation practices, will likely reduce peak hour demand

• Historical peaking factors not a true reflection of current and/or

future peaking factors : implications for network distribution modelling and infrastructure optimisation

0.0 2.5 5.0 7.5 10.0 12.5 15.0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Average daily diurnal consumption (L/p/h/d) Time (hours) 50 Least Efficient Homes 50 Most Efficient Homes

Carragher, BJ., Stewart, RA., Beal, CD.,(2012) Quantifying the influence of residential water appliance efficiency on average day diurnal demand patterns at an end use level: A precursor to optimised water service infrastructure planning. Resour Conserv Recy (2012), 62, 81-90

CONCLUSIONS

• Peak Day (PD) to Average Day (AD) ratios between 1-1.5 were

primarily driven by greater clothes washer and shower use.

• As the PD:AD ratio increased above 1.5, demand was driven largely

by external water usage (i.e. lawn and garden irrigation).

• Hourly peak demand was over 10 times the average demand for

irrigation (peaking factor average of 13.3), with shower and clothes washer average peaking factors up to 2.4 and 3.3, respectively.

• Comparison between data from this study and historically-based,

but currently used, peaking factors used for network distribution modelling suggests that the degree and frequency of high peaking factors are lower now, likely due to the high penetration of water- efficient technology and growing water conservation awareness by consumers.

GENERAL CONCLUSIONS

• Results demonstrate that significant reductions

in both peak water demand and energy demand use can be achieved by various water efficient fixtures and appliances.

• Knowledge of the end uses that are influencing

peak water and energy demand, can:

• 1) facilitate the optimisation of infrastructure

design and sizing and inform the decision process for the subsequent deferral of these expensive assets; and

• 2) underpin future sustainability codes for

new buildings.

Acknowledgements

• Edoardo Bertone, Byron Carragher from Smart Water

Research Centre

• Kim Keogh and eResearch team for SMIP development
• Don Begbie and the team at UWSRA 

Urban Water Security Research Alliance THANK YOU www.urbanwateralliance.org.au

References

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