By: Ramy Essam - Uni Kassel€¦ · By: Ramy Essam [email protected] June 18, 2014 Ramy...
Transcript of By: Ramy Essam - Uni Kassel€¦ · By: Ramy Essam [email protected] June 18, 2014 Ramy...
By: Ramy Essam [email protected]
Ramy Essam-APV June 18, 2014 1
Under Supervision of:
Prof. Dr. Mohammed Fawzy Elrefaie
Prof. Dr. Dirk Dahlhaus
1. Introduction & Objective
2. Methodology & Procedure
3. Modeling
4. Simulation & Results
5. Conclusion
6. Future Recommendations
7. Summary
8. Questions & Answers
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Outline
Source: www.ise.fraunhofer.de
Definition:
Agro-photovoltaic (APV) is the concept of combining power generated from PV and to enhance Agriculture productivity simultaneously
Aim of work:
The PV integration on farm land concept is used to increase land productivity and economic profitability with minimal negative interactions and positive optimal interactions
Objective of the study:
Evaluation of technical and economical feasibility under Egyptian climate conditions
1. Introduction & Objective
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Identify the Problem & Search for
Solutions
1. Define Technical Model Variables
2. Classification of Plants
Run the Simulation
Interpretation of results & Conclusion
2. Methodology & Procedure
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• History of APV applications
• Literature review
• Lessons learned: Fraunhofer ISE/In-house research
• Literature experimental shading studies
• Radiance Software
Sources: 1- Fraunhofer ISE; 2- M. Guggenmos; 3- www.revolutionenergymaker.com; 4- University of Montpellier
Prof. A. Goetzberger (early 80s) published preliminary results of research. Putting it into practice:
Bavaria (since 2010):
Manfred Guggenmos: practical experiments for vegetables under PV.
Northern Italy (2011): three APV-prototypes have been installed, but no scientific support to date.
South of France (2009): University of Montpellier installed APV testing facility.
2.1. History of APV
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2. Methodology & Procedure
Source: www.ise.fraunhofer.de
Plant growth conditions are a subject to change with APV implementation
Evaluation according to ecological indicator values of plants.
Ecological indicators
Interpretation against field of reference
3. Modeling
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3.1. Modeling- Agriculture Aspects
Figure (1): Biomass Yield as a Function of Relative Light Availability (PAR)
Plants react differently on shading
Response to shading of crops in arid regions
3. Modeling
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3.1. Modeling- Agriculture Aspects
0
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140
20 30 40 50 60 70 80 90 100 110
Bio
mas
s yi
eld
[%
]
Photosynthetic Active Radiation (PAR) [%]
PLUS
ZERO
MINUS
Figure (2): Classification of Egypt’s most relevant economic plants in agriculture
PLUS category: Shading tolerant, crops are benefited from shade
ZERO category: No significant effect on yield
MINUS category: Shading sensitive, crops are badly influenced by shade
3. Modeling
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3.1. Modeling- Agriculture Aspects
Figure (3): APV System Technology
1 = PV module
2 = foundation of intermediate supports
3 = foundation at the edge of the field
α = surface azimuth angle
b = module width
d = row spacing
d’ = distance between supports
L= modules length
h = clear height underneath the panels
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3. Modeling 3.1. Modeling- Technical Aspects
Figure (5): Simulation of Irradiance on Ground
Inclination Angle (15°, 25°)
Height of Installation (2m, 4m & 6m)
Orientation Angle (0°, 45°)
Row Spacing Distance (1.5-6.5m)
4. Simulation & Results
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Figure (4): Side View of an APV Module System Structure
4.1. Technical Variables
Figure (6): Global Horizontal Irradiation underneath Different Installation Heights.
Increasing height shows higher uniformity of solar irradiation distribution on ground.
Installation height of 4 m is to be considered for the upcoming calculations.
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4. Simulation & Results 4.2. Technical Results
4.2.1. Global Horizontal Irradiation vs Height
Figure (7): Global Horizontal Irradiation underneath Different Orientation of Arrays.
Optimal module orientation towards South results in heterogeneous distribution of radiation on ground level.
Orientation towards 45° South-west provides homogeneously distributed Irradiation.
Conclusion: Homogeneity of radiation is very important for crop cultivation ( simultaneous ripening, etc.) therefore, modules should have to be installed:
High
Not towards South
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4. Simulation & Results 4.2. Technical Results
4.2.2. Global Horizontal Irradiation vs Orientation angle
Figure (8): Photovoltaic Electric Yield
𝑷𝑽𝑬𝒓𝒆𝒍 𝐝; 𝜶 =𝐆𝐭
𝐝;𝜶 ∗𝐃 (𝐝𝐞𝐠𝐫𝐞𝐞 𝐨𝐟 𝐬𝐮𝐫𝐟𝐚𝐜𝐞 𝐜𝐨𝐯𝐞𝐫𝐚𝐠𝐞)
𝐆𝐭 𝟏.𝟓;𝟎 ∗𝐃 (𝐎𝐏𝐓𝐈𝐌𝐀𝐋 𝐝𝐞𝐠𝐫𝐞𝐞 𝐨𝐟 𝐬𝐮𝐫𝐟𝐚𝐜𝐞 𝐜𝐨𝐯𝐞𝐫𝐚𝐠𝐞)
∗ 𝟏𝟎𝟎
Orientation of array in an APV system towards 45° south-west. Electricity yield decreases by less than 5 % due to this suboptimal orientation.
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4. Simulation & Results 4.2. Technical Results
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PV
E [
%]
Row Spacing Distance [m]
Summer-South Winter-South
Summer-Southwest Winter-Southwest
4.2.3. Photovoltaic Electric Yield
Figure (9): Photosynthetically Active Radiation on Ground between modules
𝑷𝑨𝑹𝒓𝒆𝒍 𝒅 =𝐆𝐡𝐨𝐫
𝐝;𝛂;𝐮𝐧𝐝𝐞𝐫 𝒎𝒐𝒅𝒖𝒍𝒆
𝐆𝐡𝐨𝐫 𝐮𝐧𝐬𝐡𝐚𝐝𝐞𝐝 𝒂𝒓𝒆𝒂
∗ 𝟏𝟎𝟎
Photosynthetic active radiation in winter South-west oriented is higher than in South oriented.
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4. Simulation & Results 4.2. Technical Results
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1 2 3 4 5 6 7
PA
R [
%]
Row Spacing Distance [m]
Summer-South Winter-South
Summer-Southwest Winter-Southwest
4.2.4. Photosynthetic Active Radiation
Figure (10): Biomass Yield of APV south and 45°-Southwest oriented modules
45° South-west facing system and 25° inclination angle is a good regime to measure the effect of changing row spacing distance on the three categorized crops.
Slight differences, but only for winter crops.
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4. Simulation & Results 4.2. Technical Results
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1 2 3 4 5 6 7
BM
E [
%]
Row Spacing Distance [m]
PLUS-Summer
PLUS-Winter
ZERO-Summer
ZERO-Winter
MINUS-Summer
MINUS-Winter0
20
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1 2 3 4 5 6 7B
ME
[%
]
Row Spacing Distance [m]
PLUS-Summer
PLUS-Winter
ZERO-Summer
ZERO-Winter
MINUS-Summer
MINUS-Winter
Standard Orientation: South Standard Orientation: 45° South-west
4.2.5. Biomass Yield
Figure (11): Land Equivalent Ratio
𝐋𝐄𝐑 =𝑩𝑴𝑬𝐀𝐏𝐕
𝑩𝑴𝑬𝒎𝒐𝒏𝒐+
𝑷𝑽𝑬𝐀𝐏𝐕
𝑷𝑽𝑬𝒎𝒐𝒏𝒐
It is a quantitative approach for determining productivity of APV, it measures the total output per unit area.
LER for summer crops of PLUS category increased by 80 % productivity at optimum row spacing of 2.9 m.
LER for the other two categories are in the range of 30 to 60 % higher in productivity compared to mono-cultivation.
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4. Simulation & Results 4.2. Technical Results
0,8
1
1,2
1,4
1,6
1,8
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1 2 3 4 5 6 7
LE
R
Row Spacing Distance [m]
PLUS-Summer PLUS-Winter
ZERO-Summer ZERO-Winter
4.2.6. Land Equivalent Ratio
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4. Simulation & Results 4.3. Technical & Economical Input Parameters
Technology Parameters Unit PVoff APVoff APVon
Operating Lifetime [a] 25 25 25
Surface Area [ha] 0.5 0.5 0.5
Investment Cost * [€/kWp] 1,210 1,700 1,700
Module Width [m] 1 1 1
Row Spacing Modules [m] 1.5 2.5 2.5
Rated Capacity of Module [W/m2] 2054 2054 2054
Installed Capacity ** [kWp] 676.5 410 410
Power Generation Yield *** [kWh/m2.a] 1,7901 1,7001 1,7001
PV System Power Generation [kWh/a] 1,210,935 697,205 697,205
Financial Parameters
Debt Percentage [%] 90 90 90
Equity Percentage [%] 10 10 10
Loan Repayment Time 5 [a] 10 10 10
Weighted Average Cost of Capital (WACC) [%] 7.23 7.79 7.51
Cost of Equity [%] 31.75 37.38 34.56
Beta Factor [-] 2 2.5 2.25
Annual Current Output Reduction [%] 0.21 0.21 0.21
Price of Electricity [€/kWh] 0.146 0.146 0.0697
Annual Operating Expenses
(as a percentage of investment)
[%] 21 21 21
Leased Land [€/a] 0 0 0
Agricultural Income [€/a] 0 6888 6888
Table (1): Input Technical and Financial Parameters for Economic Feasibility
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4. Simulation & Results 4.4. Economical Results
Results Unit PVoff APVoff APVon
Lifetime [a] 25 25 25
Total Investment Cost 10 [€] 818,565 697,000 697,000
Total Power Generation 11 [kWh] 29,557,833 17,018,146 17,018,146
Revenue on Sale of
Electricity
[€] 4,138,096 2,382,540 1,174,252
Maintenance Cost 12 [€] 409,282 348,500 348,500
Net Income Electricity [€] 2,910,249 1,337,040 128,752
Net Income Agriculture [€] 0 32,836 32,836
Total Net Revenue [€] 2,910,249 1,369,876 161,588
Present Value (PV) [€] 1,716,588 904,040 383,961
Weighted Average Cost of
Capital (WACC)
[%] 7.23 7.79 7.51
Net Present Value (NPV) [€] 898,023 207,040 -313,038
Internal Rate of Return
(IRR)
[%] 18.22 10.92 1.37
Electricity Generation Costs
(LCOE)
[€/kWh] 0.07 0.114 0.111
Table (2): Results of the Investment Analysis of an APV System in comparison with a Conventional PV System
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4. Simulation & Results 4.5. Sensitivity Analysis-Investment Cost
Figure (12): The influence of Investment cost on the NPV and IRR for APV off-grid scenario.
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*10
00
€/h
a]
Investment cost [€/kWp]
NPV
IRR
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4. Simulation & Results 4.6. Sensitivity Analysis-Electricity Cost
Figure (13): The influence of Electricity cost on the NPV and IRR for APV on-grid scenario.
0
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-300
-200
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IRR
[%
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NP
V [
*10
00
€/h
a]
Electricity cost [€/kWh]
NPV
IRR
APV off-grid Technology is Technically and Economically fesible.
APV off-grid scenario can compete with PV scenario by decreasing the investment cost and for APV on-grid by increasing the price of electricity (Feed-in Tariff).
In the off-grid scenario, reducing the investment cost due to relying on local material of construction. Result in APV-off grid technology could still compete with a PV system.
Coexisting of PV and plant cultivation is “theoretically” feasible in Egypt and “practically” proven elsewhere (e.g. France and Italy)
(In Egypt: only if economic framework will be established)
5. Conclusion
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P = Surface area ∗ PAPV,ha
APV-Potential in Egypt equals 123-246 GWp.
APV-Potential in Minya equals 14-29 MWp.
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6. Future Recommendations 6.1. APV Potential-Case Study in Minya
Potential Restriction 𝐏𝐀𝐏𝐕,𝐡𝐚 Surface Area P
[kWp/ha] [ha] [MWp]
Theoretical Agriculture Land 820 420 344
Technical Plants of Categories
PLUS and ZERO
820 350 287
Technical Assumption:
Sustainability of 5-10 %
of the area
820 17.5-35 14-29
Table (3): Assessment of Theoretical and Technical Potential of APV in Minya
Sekem and Fraunhofer ISE intend to kick-off a pilot project in Egypt
Recommended to Calculate:
Amount of irrigation water savings .
GHG emissions due to the replacement of APV-electricity to diesel generators in off-grid regions.
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6. Future Recommendations 6.2. Recommendation for Action
Source: Green Valley Farm, SEKEM.
Some results of the present study based only on theoretical assumptions, their validation is still pending. The three dominant uncertainties that should be examined are:
Data basis for assessing the shade tolerance of crops
Yield models of the three plant categories
Boundary conditions of the economic analysis
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6. Future Recommendations 6.3. Uncertainties and Future Work
Source: Green Valley Farm, SEKEM
Finally, the use of APV still appears to be a significant and purposeful approach to enhance the productivity of the same land area between agriculture and energy sector.
7. Summary
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Source: www.google.de/images
Thank you very
much for your attention…
8. Questions & Answers
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