Transcript Totara bank project - SEAT Home

2008 Energy Postgraduate Conference
Totara Bank project
Léa Sigot - Sylvain Lamige
Supervisor: Attilio Pigneri
Presentation overview
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Context
Totara Bank project outlines
Previous work
On-site visit
Thermal analysis of building design and siting options
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Objectives
Waitakere NOW Home®
Software review:
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Data collection:
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ALF3
Climate data
Techno-economic analysis of distributed generation options from
renewable energy sources and grid-interaction scenarios
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Objectives
Methodology
Software review:
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HOMER
ViPOR
Data collection
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Resource data
Technological data
Electrical demand data
Context of our research project within
our studies
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Our school:
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National Institute of Applied Sciences
(INSA) of Lyon
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Our department:
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Energy & Environment Engineering
Our research project:
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A five-month research project during our fourth year
Totara Bank project outlines
A sustainable land development, with:
Energy efficient houses, based on solar
principle design
 On-site electricity generation from
renewable sources
 Life in community
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On-site visit
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Site location
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Plot contours
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Common facilities
Location
8 km
south
of
Wairarapa
district
Masterton
Totara Bank site
 7 hectares
 8 lots
Common facilities
Common house
Road access
Pole connection
Coppicing trees
Previous work
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Land layout
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Whole solar access planning
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Optimized eaves shade angles
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Site thermal self-sufficiency
Previous work
Land layout
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7 hectares:
8
individual lots (1200-2100 m²)
 6 hectare common land
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Soil-adapted tree plantation
 An
optimized land layout
Previous work: Land layout
Previous work
Land layout
7 hectares:
8 individual lots (1200-2100 m²)
6 hectare common land

Soil-adapted tree plantation
 An
optimized land layout
Previous work: Land layout
Previous work
Land layout
7 hectares:
8 individual lots (1200-2100 m²)
6 hectare common land
Soil-adapted tree plantation
 An
optimized land layout
Previous work: Land layout
Previous work
Whole solar access planning

85 % solar energy capture
 Solar
obstruction contours
Previous work: Solar obstruction contours
Previous work
Optimized eaves shade angles
Avoid summer over-heating and
Traditional
winter under-heating
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Optimized
 Site-suited
angles
Previous work
Site thermal self-sufficiency
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Dwelling minimal thermal efficiency, based on:
 Building
Performance Index (BPI)
 Floor area
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On-site fuel resource:
 0.6
ha of coppicing trees
Thermal analysis of building design
and siting options

Objectives
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Waitakere NOW Home®
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Software review: ALF3
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Data collection
Thermal analysis of buildings
Objectives
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Model energy performance of a baseline
house
 How
much heating energy is required?
 Is the site resource sufficient?
 What can be done to improve the site thermal
self-sufficiency?
Thermal analysis of buildings: Data collection – Dwelling characteristics
Waitakere NOW Home®
Designed, built and monitored
by Beacon Pathway Ltd
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Efficient building best practices
Suit an average family
 Affordable
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Thermal analysis of buildings
Software review: ALF3
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Description
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Modelling capabilities
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Input requirements
Thermal analysis of buildings: Software review – ALF3
Description
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Annual Loss Factor Method
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Developed by the Building Research
Association of New Zealand (BRANZ)
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Determine the heating energy required
Thermal analysis of buildings: Software review – ALF3
Modelling capabilities
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Heating energy required [kWh/year]
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Building Performance Index (BPI)
[kWh/(DegreeDay.m²)]
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Design option comparison
Thermal analysis of buildings: Software review – ALF3
ALF flow chart
Thermal analysis of buildings: Software review – ALF3
Modelling capabilities
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Heating energy required [kWh/year]
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Building Performance Index (BPI)
[kWh/(DegreeDay.m²)]
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Design option comparison
Thermal analysis of buildings: Software review – ALF3
ALF sample design comparison
Thermal analysis of buildings: Software review – ALF3
Input requirements
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Dwelling characteristics
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Occupancy behaviour
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Climate region
Thermal analysis of buildings: Software review – ALF3
ALF flow chart
Thermal analysis of buildings: Software review – ALF3
Input requirements
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Dwelling characteristics
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Occupancy behaviour
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Climate region
Thermal analysis of buildings: Software review – ALF3
ALF flow chart
Thermal analysis of buildings: Software review – ALF3
Input requirements

Dwelling characteristics
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Occupancy behaviour
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Climate region
Thermal analysis of buildings: Software review – ALF3
ALF flow chart
Thermal analysis of buildings: Data collection – Climate
Climate data
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Meteorological station location
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Long-term average monthly temperature
 ALF-value
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calculation
Long-term average monthly sunshine
hours
 AGF-value
calculation
Thermal analysis of buildings: Data collection – Climate
Thermal analysis of buildings: Data collection – Climate
Climate data
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Meteorological station location
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Long-term average monthly temperature
 ALF-value

calculation
Long-term average monthly sunshine
hours
 AGF-value
calculation
Thermal analysis of buildings: Data collection – Climate
Long-term monthly temperatures for Totara Bank (1995-2007)
Monthly average temperature [°C]
25
20
15
Minima
Maxima
Average
10
5
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Month of year
Aug
Sep
Oct
Nov
Dec
Thermal analysis of buildings: Data collection – Climate
Monthly average temperature for
Waitakere NOW Home® & Totara Bank
Central Auckland
Average Waitakere
Waingawa & Masterton
Average Totara Bank
Monthly average
temperature [°C]
25
20
15
10
5
Average difference: 3.2 °C
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Month of year
Sep
Oct
Nov Dec
Thermal analysis of buildings: Data collection – Climate
Climate data

Meteorological station location

Long-term average monthly temperature
 ALF-value

calculation
Long-term average monthly sunshine
hours
 AGF-value
calculation
Thermal analysis of buildings: Data collection – Climate
Long-term average monthly sunshine hours for Totara Bank
(1995-2007)
Average
Minima
Maxima
Monthly average sunshine [h]
300
250
200
150
100
50
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Month of year
Aug
Sep
Oct
Nov
Dec
Thermal analysis of buildings: Data collection – Climate
Long-term average monthly sunshine hours (1995-2007)
Monthly average sunshine [h]
Waitakere
Totarabank
250
200
150
100
50
0
Jan
Feb
Mar
Apr
May
Jun
Month of year
Jul
Aug
Sep
Oct
Nov
Dec
Thermal analysis of buildings: Data collection – Climate
Climate data

Meteorological station location

Long-term average monthly temperature
 ALF-value

calculation
Long-term average monthly sunshine
hours
 AGF-value
calculation
Thermal analysis of buildings: Data collection – Climate
ALF-value [1000.°C.h]
ALF-values comparison between "ALF3" and "Calculated" for
Totara Bank
45
40
35
30
25
20
15
10
5
0
ALF3
Calculated
16 °C 18 °C 20 °C 16 °C 18 °C 20 °C 16 °C 18 °C 20 °C 16 °C 18 °C 20 °C Heating level
Schedule
Evening 1
Schedule 2
Morning/evening
Schedule
All day3
Schedule
24 h 4
Heating
schedule
Thermal analysis of building design
and siting options
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Model template for Waitakere NOW Home®
in Totara Bank site
Heating energy required for the whole
Totara Bank site
Part provided by the coppicing area
To be continued…
Techno-economic analysis of distributed
generation options from renewable energy
sources and grid-interaction scenarios
Objectives
 Methodology
 Software review:

 HOMER
 ViPOR
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Data collection:
 Resource
data
 Technological data
 Electrical demand data
Electrical generation system analysis
Objectives
Techno-economic
analysis of distributed generation
options from renewable energy sources
and grid-interaction scenarios
 What
type of generation mix?
 Trade-of between electrical requirements and onsite generation costs
 What electrical self-sufficiency level can be
achieved?
Electrical generation system analysis
Methodology
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Using HOMER:
 Determine
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the optimal generation system
Using ViPOR:
 Draw
the local network map
Electrical generation system analysis
Software review: HOMER
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Description
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Modelling capabilities
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Input requirements
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Already done projects
Electrical generation system analysis: Software review – HOMER
Description
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Developed by the American
National Renewable Energy
Laboratory (NREL)
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Hybrid Optimization Model for
Energy Renewables
Electrical generation system analysis: Software review – HOMER
Modelling capabilities
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Generation system simulation
 Energy
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Optimisation
 Ranked
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balance
by cost
Sensitivity analysis
Electrical generation system analysis: Software review – HOMER
Input requirements
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Resources
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Technologies
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Electrical demand
Electrical generation system analysis: Software review – HOMER
Already done projects
Sicud project:
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Small village in Philippines
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Wind, solar and fuel resource
 Methodology
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Impact of wind speed and fuel price
 Usefulness
of sensitivity analysis
Electrical generation system analysis: Software review – ViPOR
Software review: ViPOR
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Description
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Modelling capabilities

Input requirements
Electrical generation system analysis: Software review – ViPOR
Description
Developed by the American
National Renewable Energy Laboratory
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The Village Power Optimization Model for
Renewable
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In association with HOMER
Electrical generation system analysis: Software review – ViPOR
Modelling capabilities
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Intern grid design
 Wire
length
 Load kind
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Grid-interaction scenarios
 Electricity
price
Electrical generation system analysis: Software review – ViPOR
Input requirements
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Spatial inputs
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Non-spatial inputs
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose
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Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose

Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
Average wind speed: 3,2 m/s
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose

Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
Long-term average wind speed frequency distribution curve
(1995-2007)
No. of hours per year of each wind speed
band
2500
2000
1500
1000
500
0
0,5
1,5
2,5
3,5
4,5
5,5
6,5
7,5
8,5
Wind speed (m/s)
55 % of wind speed < 3 m/s
9,5 10,5 11,5 12,5 13,5 > 14
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose

Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
East Taratahi Aws wind rose over a thirteen-year period (1995-2007)
North
330
320
310
360
3506%
340
10
20
5%
40
4%
300
30
50
Wind speed (m/s)
60
3%
> 12,5
290
70
2%
280
10,5 - 12,5
8,5 - 10,5
80
1%
6,5 - 8,5
West
270
0%
90
260
East
4,5 - 6,5
2,5 - 4,5
100
0,5 - 2,5
250
110
240
230
220
210
120
200
190
170
180
Drawn using hourly wind speed records from
East Taratahi Aws between 1995 and 2007
South
160
130
140
150
□
Variable direction (≈ 0,5 m/s): 7,5 %
□ Calm (<0,5 m/s): 2,65 %
Average wind speed: 3,2 m/s
Direction: wind blowing from
Electrical generation system analysis: Data collection – Resource
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose

Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
Electrical generation system analysis: Data collection
Resource data
Available data
 Hourly wind speed and direction
 Hourly global solar radiation
Profile development
 Wind resource:
 Long-term
average hourly profile
 Wind speed frequency distribution curve
 Wind rose

Solar resource
 Long-term
average hourly profile
 Daily profile per month
Electrical generation system analysis: Data collection – Resource
Long-term daily hourly average global solar radiation (1995-2007) for each
month
0,80
Solar radiation (kW/m²)
0,70
January
February
0,60
March
April
May
0,50
June
July
August
September
0,40
0,30
October
November
December
0,20
0,10
0,00
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of day (h)
Electrical generation system analysis: Data collection
Next steps

Technological data
 Database
 Cost

estimation
Electrical demand data
 Hourly
electrical demand record
2008 Energy Postgraduate Conference
Totara Bank project
Léa Sigot - Sylvain Lamige
Supervisor: Attilio Pigneri