Solar Energy for Industrial Rooftops: An Economic and
Environmental Optimization
School of Mechanical and Manufacturing Engineering
Asia-Pacific Solar Research Conference
Solar Energy for Industrial Rooftops: An Economic and Environmental
Optimization
Bany Mousa Osama1, Taylor, Robert A1.
1University of New South Wales, Sydney, Australia
Energy systems/ Sustainable manufacturing
1
Solar Thermal
ST [2]Photovoltaic
PV [1]
2
Compact linear Fresnel
Collector [3]
PVT Collector [4]
Solar Energy for Industrial Rooftops: An Economic and
Environmental Optimization
Industrial Energy Demand [5,6,7]
3
MY APPLICATION
Heat: 85 EJElectrical: 44 EJ
Low: 25.5 EJMedium: 23 EJHigh: 36.5 EJ
Industrial Processes: 129 EJOther: 110 EJ
Research Question: What is the right mix of technologies for
industrial rooftops?
– Important Factors:
• Medium and high temperatures heat are needed.
• Environmental emissions reductions, (global variation in climate)
• Limited rooftop space
• Economics
Approach: Investigate global trade-offs to find an optimum solar
technology mix using transient performance and life cycle analysis.
– Note: The ratio can be optimized based on many objectives (and there’s no
prior literature to date on this topic)
Research Question and Approach
4
Aims
• The right mix of the solar technologies for industrialapplications– Technologies
• Solar thermal
• Photovoltaic
• Photovoltaic + solar thermal
– Objectives• Annual performance
• Embodied energy
• Embodied emissions
• Economic (levelized cost of energy)
• A comparison between locations that has differentirradiation and beam component.
5
Application - Process
• The annual performance of a sterilization
process that requires steam at 134°C outlet
temperature was modelled using TRNSYS.
• The collector output was maintained at 180°C
to drive a heat recovery steam generator
(HRSG).
6
7
System model (TRNSYS schematic diagram)
Side-by-Side PV and ST Systems
and Geographical Locations
• ST versus PV was varied from 0-1
• Ratio =𝑆𝑜𝑙𝑎𝑟 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟𝑎𝑟𝑒𝑎
𝑓𝑎𝑐𝑡𝑜𝑟𝑦 𝑎𝑟𝑒𝑎
• 0 presents a stand-alone PV system
1 presents the thermal-only system.
8
Environmental impacts and savings analysis
9
Location Installation
capital cost
ratio [%]
ST
including
Installatio
n ($/m2)
PV panels with installation
$/W
(IRENA
[8]
$/m2 LCOE $/kWhel [8]
Residen
tial
Commerc
ial
China 7.26 370 1.168 201 0.1059 0.084
Australia 43.89 496 1.567 270 0.1026 --
US 50 518 2.678 461 0.1545 0.112
Location Average
interest rate
Minimum
interest rate
Maximum
interest rate
China 5.1 4.4 6
Australia 5.9 5.2 7
US 3.3 3.2 3.5
5 years average interest rates for several
countries with interest variation
Location ST-
Embodied
GHGe
(kg/m2
aperture)
PV-
Embodied
GHGe
(kg/m2
aperture)
CT or ETS ($/tonne)
[9]
Natural
gas
emission
kgCO2eq/
GJth
China 803 556 9 [Beijing ETS] 61.84
Australia 892 595 17.95 [10] 69.72
US 539 461 15.10 66.74
Environmental impacts of solar technologies
Global average annual Natural gas price variation
Solar technologies estimated costs (including Installation)
Objective Functions
1. 𝑆𝐹 =𝐿𝑜𝑎𝑑 𝐺𝐽𝑡ℎ −𝐴𝑢𝑥(𝐺𝐽𝑡ℎ)
𝐿𝑜𝑎𝑑 (𝐺𝐽𝑡ℎ)
2. 𝐸𝐸 𝑃𝐵𝑇 =𝐸𝐸(𝐺𝐽𝑡ℎ)
𝑆𝐹∗𝐿𝑜𝑎𝑑 𝐺𝐽𝑡ℎ/𝑦𝑒𝑎𝑟;
• 𝐸𝐸 = 2.62 𝑆𝑇𝑎𝑟𝑒𝑎 + 2.66 𝑃𝑉𝑎𝑟𝑒𝑎; 𝐸𝐸𝑆𝑇 = 2.62 𝐺𝐽𝑡ℎ/𝑚2, 𝐸𝐸𝑃𝑉 = 2.66 𝐺𝐽𝑡ℎ/𝑚
2
3. 𝐺𝐻𝐺𝑒𝑃𝐵𝑇 =𝐸𝑚𝑏𝑜𝑑𝑖𝑒𝑑 𝐺𝐻𝐺𝑒(𝑘𝑔𝐶𝑂2𝑒𝑞)
𝑆𝐹∗𝐿𝑜𝑎𝑑 𝐺𝐽𝑡ℎ/𝑦𝑒𝑎𝑟 ∗𝑁𝐺𝑒[𝑘𝑔𝐶𝑜2𝑒𝑞
𝐺𝐽𝑡ℎ]
• 𝐸𝐺𝐻𝐺𝑒 = 𝑃𝑉𝐶𝑂2𝑒𝑞𝑘𝑔𝐶𝑂2𝑒𝑞
𝑚2 ∗ 𝑃𝑉𝑎𝑟𝑒𝑎 + 𝑆𝑇𝐶𝑂2𝑒𝑞𝑘𝑔𝐶𝑂2𝑒𝑞
𝑚2 ∗ 𝑆𝑇𝑎𝑟𝑒𝑎
4. 𝐿𝐶𝑂𝐸($/(𝑘𝑊ℎ𝑡ℎ) =𝑆𝑦𝑠𝑡𝑒𝑚 𝑐𝑜𝑠𝑡
𝑡=1𝐿𝑇 𝑆𝐹∗𝐿𝑜𝑎𝑑 𝐺𝐽𝑡ℎ / 1+𝑖
𝑡
5. 𝐿𝐶𝐶𝐿𝐶𝑂𝐸($/(𝑘𝑊ℎ𝑡ℎ) =𝐿𝐶𝐶
𝑡=1𝐿𝑇 0.0036∗𝑆𝐹∗𝐿𝑜𝑎𝑑 𝐺𝐽𝑡ℎ / 1+𝑖
𝑡
• 𝑆𝑦𝑠𝑡𝑒𝑚 𝐶𝑜𝑠𝑡 = 𝑃𝑉𝑎𝑟𝑒𝑎𝐶𝑜𝑠𝑡𝑃𝑉 + 𝑆𝑇𝑎𝑟𝑒𝑎 ∗ 𝐶𝑜𝑠𝑡𝑆𝑇
• 𝐿𝐶𝐶 = 𝐸𝐸 𝐺𝐽𝑡ℎ ∗ 𝑁𝐺$
𝐺𝐽𝑡ℎ+ 𝐸𝐺𝐻𝐺𝑒 𝐾𝑔𝐶𝑜2𝑒𝑞 ∗ 𝐶𝑇
$
𝐾𝑔𝐶𝑂2𝑒𝑞+
𝑠𝑦𝑠𝑡𝑒𝑚 𝐶𝑜𝑠𝑡 ($)
10
11
Optimization Flow Chart
TRNSYS
Environment
Genopt
Environment
Industrial
Load
Weather
Data
Key
Inputs
System Energy
Simulation
Design Parameters
Output Parameters
Technical Function
Cost Function
Environmnetal Function
Select Optimization algorithm
Stopping Criteria
Satisfied?
End
Yes
No
Update d
esign v
ariables
Optimization Results
12
Optimum solar technology mix in various locations
based on the performance objective function
Optimum solar technology mix in various locations based on the embodied objective
functions: (A) Embodied energy function; (B) Embodied emission function
Optimization Results
13Optimum solar technology mix in various locations based on cost functions:
(A) Levelized cost of energy; (B) Life cycle levelized cost of energy
14
Optimization Results – Cost variation
Economical objective function optimum distribution with ST to PV cost variation
15
Worldwide Profiling
16
System model (flow diagram)
FU: Heat output per unit area
17
18
Multi-objective Optimization Results
Fig 7. Optimum solar mix using several criteria Pareto frontier in Alice Springs/ Australia, Andir/ China and
Santiago/ Chile A: Economical – environmental criteria B: Economical – Technical criteria C: Environmental –
Technical criteria D: Economical – Technical – Environmental criteria
19
Worldwide Profiling
20
Multi-objective Optimization Results
Optimization summary- Ratio of ST area to mixed solar system area
1. https://au.pinterest.com/pin/555209460286792472/
2. http://www.newmexicosolarandwind.com/Solar%20Thermal.htm
3. http://products.newformenergy.ie/photovoltaic-thermal-pvt.php
4. Zhang, X., et al., Review of R&D progress and practical application of the solar photovoltaic/thermal
(PV/T) technologies. Renewable and Sustainable Energy Reviews, 2012. 16(1): p. 599-617.
5. U.S. Energy Information Administration. International 126 Energy Outlook 2016. Available from:
http://www.eia.gov/forecasts/ieo/industrial.cfm.
6. Institute, Environmental and Energy Study. solar thermal energy for industrial uses. 2011; Available from:
http://www.eesi.org/files/solar_thermal_120111.pdf.
7. Vannoni, Battisti, and Drigo, Potential for Solar Heating Industrial Processes. Report within IEA SHC
Task33/IV. 2008: Rome,Italy.
8. I. Staffell, A review of domestic heat pump coefficient of performance (2009)
References
2121
Cost Analysis
22
𝑋𝑒𝑞_𝑃𝐵𝑇 = 1 −𝑥
𝑆𝐹
𝑥 =𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 ∗ 𝑖 %
𝑇𝑜𝑡𝑎𝑙 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 ($)
23
Cost Analysis
𝑋𝑒𝑞_𝐼𝑅𝑅 =𝐴𝑛𝑛𝑢𝑎𝑙 𝑠𝑎𝑣𝑖𝑛𝑔𝑠
𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡
Initial Work-1st Year
Solar thermal circuit flow diagram
2424
Model validation/Tolerance
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
0.00E+00
2.00E+07
4.00E+07
6.00E+07
8.00E+07
1.00E+08
1.20E+08
1.40E+08
1.60E+08
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Tole
ran
ce %
En
erg
y (
J)
Gains Losses Tolerance
2525
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65
Eff
icie
ncy
'
∆T/G
FPC
TVP
ETC
PTC
MUSIC
MCT
G =1000 W/m2
instantaneous efficiency comparison
Ƞ = 𝐹’ 𝜏𝛼 ∗ 𝐼𝐴𝑀 – c1 ( Tavg−Ta
Gt) – c2 (
(Tavg−Ta)2
Gt) ; (
𝐺𝑏
𝐺𝑡)
2626
Annual efficiency comparison
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 50 100 150 200 250 300 350 400 450 500An
nu
al
effi
cien
cy
Tout when Tin = 20
TVP
ETC
PTC
MUSIC
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 10 20 30 40 50 60 70 80 90
Inci
den
t a
ng
le m
od
ifie
r
Angle (degree)
TVP IAM PTC transverse IAM ETC transverse IAM MUSIC transverse IAM
Incident angle modifier
2727
Parametric Study
The annual performance of a sterilization process that requires steam at
134°C outlet temperature was compared using TRNSYS software :
The collector output was maintained at 180°C to drive a heat recovery
steam generator (HRSG).
1) Four different solar collectors for.
2) Six different transient load profiles.
3) Two tank configurations
4) Two controller methods.
Finally, all (4x6x2x2 = 96) systems were compared for two Australian
locations, Sydney and Alice Springs (96x2 = 192).
2828
2nd year: Beam split PVT system
Beam splitting strategy- from
• Technology purpose
• PV performance
• Thermal collector performance
• Operating temperature
• Available literature (four filter designs)
Wavelength
range (nm)
Ratio- ST
absorbed
radiation
PV cell
efficiency
rise
Crisostomo et al. 732-1067 71.5% 9%
Sabry et al. 450-920 41% 6%
Liu et al. 590-1082 51% 12.5%
Kandilli 400-800 46% 10%
2929
Beam splitting PVT filters
Filter bandwidth effect on ‘uncoupled’ PV/T splitting collector output (bars, which
correspond to the left y-axis) and solar contribution (lines, with correspond to the
right y-axis) for two PV cell types (White background- Alice Springs, black
background- Santiago)
DNI ratio and system contribution for
different beam split technologies
3030
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Req
uir
ed e
ner
gy
(G
J/m
2)
Case study [N°]
Equipment maufacturing
Overhead operations
Cell,module processing
subtrate and encapsulation
Cell material
Frame
Module Assembly
Cell Production
Wafer Process
CZ Process
MG-Si
Si Feedstock
Mono Si Poly Si a-Si
Embodied energy requirements
for solar silicon PV panels processes
3131
Some of the industrial processes required temperature range
3232
Industry Process Temperature (°C)
Food & Beverage Evaporating
Sterilisation
Drying
Cooking
40 – 130
100 – 140
40 – 200
70 – 120
Tinned food Sterilization 110 – 120
Textile Colouring 40 – 130
Paper Bleaching
Drying
130 – 150
95 – 200
Metal Drying 60 – 200
Bricks curing Curing 60 – 140
Plastics Separation
Drying
200 – 220
50 – 150
Chemical Soups
Distillation
Cooking
Compression
200 – 260
100 – 200
85 – 110
110 – 170
Boundaries-System Level
3333
PV manufacturing and inventory data
3434
3
5
Methods/Approaches