* Solar Resource CUF Assessment
* Land Site Assessment
* PV Plant Operation Assessment
Akash
Founder, INDIS, LLC
B. Tech (IIT B), MS (USA), MBA (USA)
akash@indisllc.com / +91 9718112443 (India)
Salt Lake City, UT (USA) / New Delhi (India)
www.indisllc.com // www.akashcleantech.com
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INDIS – Service Offerings
* US-based; Established in 2008 in USA
* Active in USA & India (New Delhi)
• Solar Resource (CUF) Assessment
• Using Satellite-based data & GIS tools for
Land Siting & Land Identification
• Detailed Comparative Site Analysis
• Site Water Drainage Management
• Solar PV Plant – Complete Design Services
including Bill of Materials
• Solar PV Plant Operational Troubleshooting
• Solar DPRs & CER Assessment
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Questions that will be Answered in this Presentation
No.
Question
Refer to
Slide No.
1
Which is the most accurate Model to estimate avg. annual CUF?
Slide 14
2
What are the top 5 districts in Odisha with highest annual CUF?
Slide 17
3
What is the effect of local weather conditions (temp, wind, humidity) on relative
annual CUF & relative ranking of districts in Odisha?
Slide 18
4
What is the estimated avg. annual kWh per MW for the top 5 districts in Odisha?
Slide 20
5
What are the economic consequences of setting up a solar PV plant in the wrong
district or a district chosen using NASA/RETScreen/HOMER tools?
Slide 20
6
What is the most effective way to identify cheap, available land in a given state or
district for solar PV projects?
Slide 24
7
What are the potential cheap land areas that are most suitable for solar PV plants
in the top Odisha district?
Slide 26
8
What are the benefits of using Satellite-GIS tools for land identification?
Slide 27
9
What are the soiling & cleaning issues for a PV Plant?
Slide 30
10
What are the kWh generation issues & variations in a PV Plant?
Slide 31
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1. Solar Potential & Land Siting
[ Which District ? ]
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Land Siting - Setting up Solar PV
Plants in Odisha (Orissa)
• Odisha has 30 Districts Which district to
locate Solar PV power plant?
– Rank Districts in order of decreasing Annual CUF
– Choose those districts that maximize Annual CUF or
Annual kWh/kW
• Using tools like NASA, HOMER, RETScreen will
lead to incorrect conclusions
• Solar-CUF model developed by INDIS is more
accurate and has been successfully
demonstrated at 6 locations across India
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Validating INDIS Solar-CUF Model
• 365 days data available
for 6 Solar PV Sites
16.9%
• CUF for these 6 Solar PV
Power Plant sites varied
from 12% to 18%.
17.7%
12.3%
15.4%
• None of the existing
Models were predicting
such a wide variation in
CUF across India
14.8%
12.7%
INDIS Solar CUF Model
tested & validated on a
pan-India basis 6 sites
located across India
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Model
Validation
Month-wise Generation of Actual 1 MW Plant
Asansol
CUF = 12.3%
Derate Factors
Range of Value
PV module nameplate
DC rating
0.80 - 1.00
Inverter and Transformer
0.80 - 0.98
Mismatch
0.97 - 0.995
Diodes and connections
0.99 - 0.997
DC wiring
0.97 - 0.99
AC wiring
0.98 - 0.993
Soiling
0.80 - 0.995
System availability
0.80 - 0.995
Shading
0.80 - 1.00
Sun-tracking
0.95 - 1.00
Age
0.70 - 1.00
Grid Uptime
100+ PV Plants Worldwide
0.80 – 1.00
Overall Derating Factor = 0.70 – 0.83
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Measured Annual CUF in Asansol (West Bengal)
WBGEDCL / Asansol 1 MW Solar PV Project - Measured (Actual) CUF vs. Predicted
CUF from various Sources [PR = 0.75]
17%
16.0%
16%
15.0%
15.1%
Model 1 RETScreen
Model 2 HOMER
15.8%
15.5%
% CUF
15%
14%
13%
12.29%
12.39%
Measured /
Actual
INDIS CUF
Model
12%
11%
10%
NREL
Solar GIS *
3Tier *
Radiation
Map * * Estimates based on radiation map –
minor inaccuracies can be expected
• Most Models predict higher CUF for Asansol (West Bengal) Site that what is measured
• Most Models are over predicting by 18%
• Odisha has similar radiation / weather patterns like those in Asansol / West Bengal
• A lower Annual CUF is anticipated in Odisha But How Much Low?
• Find Land Site in Odisha with Highest Annual CUF Which Model to Use?
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http://www.cercind.gov.in/2011/Whats-New/PERFORMANCE%20OF%20SOLAR%20POWER%20PLANTS.pdf
8
Actual (Measured) Data from Operational Solar PV Power Plants
No.
Project Developer
Project Site
(nearest city)
1a
West Bengal
WBGEDCL # 1
(Sept ‘09 – Aug ‘10)
Asansol,
West
Bengal
1
1b
West Bengal
WBGEDCL # 2
(Sept ‘10 – Apr ‘11)
Asansol,
West
Bengal
1
Crystalline
Silicon
2a
Azure Power #1
(Dec ‘09 – Nov ‘10)
Amritsar,
Punjab
1
Crystalline
Silicon
2b
Azure Power #2
(Dec ‘10 – Jun ‘11)
*2nd MW added in Nov
Amritsar,
Punjab
2*
Crystalline
Silicon
212
1,740,480*
(total for both)
3
Mahagenco
(May ‘10 – Apr ‘11)
Chandrapur,
Maharashtra
1
a-Si Thin
Films
365
1,347,840
15.39%
4
Reliance Industries
(July ‘10 – June ‘11)
Khimsar,
Rajasthan
5
Crystalline
Silicon, Thin
Film, CPV
352
7,473,378
17.69%
5
Karnataka Power Corp
Ltd (KPCL)
(Jan ‘10 – Dec ‘10)
Belgaum,
Karnataka
3
Crystalline
Silicon
365
3,897,680
14.83%
6
Karnataka Power Corp
Ltd (KPCL)
(Jan ‘10 – Dec ‘10)
Kolar,
Karnataka
3
Crystalline
Silicon
365
3,348,446
12.74%
Source: MNRE &
CERC
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Nominal
Capacity
(MW)
PV
Technology
Used
Crystalline
Silicon
Days in
Operation
Actual
Generation in
Units during
the Period
Actual
CUF %
1,130,700
12.29%
365
242
730,500
1,571,610
16.92%
365
http://www.mnre.gov.in/pdf/Grid-Solar-Demo-Performance.pdf
http://www.cercind.gov.in/2011/Whats-New/PERFORMANCE%20OF%20SOLAR%20POWER%20PLANTS.pdf9
INDIS Solar CUF Model (Prediction) vs. Measured
Error Margin
Project
Site
CUF %
CUF %
CUF %
Comments
Developer
Location
Measured
INDIS Model
Difference
As % of
Measured CUF
1. WBGEDCL
Asansol
12.29%
12.39%
0.1%
0.78%
2. Azure
Amritsar
16.92%
16.05%
-0.9%
-5.12%
3. Mahagenco
Chandrapur
15.39%
15.93%
0.5%
3.49%
4. Reliance
Khimsar
17.69%
17.34%
-0.3%
-1.97%
5. KPCL
Belagum
14.83%
16.07%
1.2%
8.36%
Grid / Inverter Outage
issues explains the
lower actual CUF
6. KPCL
Kolar
12.74%
13.81%
1.1%
8.39%
Grid / Inverter Outage
issues explains the
lower actual CUF
% CUF = 100 x Total kWh Generated Annually
365 * 24 * MW * 1000
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Avg. Error: 2.32%
Std Dev. In Error: 5.50%
10
Other Solar Irradiation Data Sources: NREL Irradiation Map
NREL Map is predicting >21% Avg. Annual
CUF for most of Orissa (at PR = 1.00)
* Orissa & West Bengal have very
similar solar irradiation profile
NREL GHI Irradiation Map of Eastern India
Asansol
For Asansol, at PR =0.75, NREL Map
estimates Avg. Annual CUF of ~16%
West
Bengal
Orissa
Source: NREL GHI India Radiation Map (2009)
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Other Solar Irradiation Data Sources: Private Party Providers [ SolarGIS ]
SolarGIS Irradiation
Map of Eastern India
West
Bengal
Orissa
Orissa & West Bengal have very
similar solar radiation profile
SolarGIS Data is
predicting >21% Avg.
Annual CUF for most of
Orissa (at PR = 1.00)
For Asansol, at PR =0.75,
SolarGIS Data estimates
Avg. Annual CUF of ~16%
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Source: India Solar Handbook, Bridge to India
12
Other Solar Irradiation Data Sources: Private Party Providers [ 3Tier ]
3Tier Irradiation Map of Eastern India
Orissa & West Bengal have very
similar solar irradiation profile
West
Bengal
3 Tier Data is
predicting >21% Avg.
Annual CUF for most of
Orissa (at PR = 1.00)
For Asansol, at PR =0.75,
3Tier Data estimates Avg.
Annual CUF of ~16%
Orissa
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Source: http://www.3tier.com/static/ttcms/us/documents/publications/validations/solar_india_validation.pdf
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INDIS Solar CUF Model vs. Other Models
WBGEDCL / Asansol 1 MW Solar PV Project - Measured (Actual) CUF vs. Predicted
CUF from various Sources [PR = 0.75**]
17%
16.0%
16%
15.0%
15.1%
Model 1 RETScreen
Model 2 HOMER
15.8%
15.5%
% CUF
15%
14%
13%
12.29%
12.39%
Measured /
Actual
INDIS CUF
Model
12%
11%
10%
NREL
Solar GIS *
3Tier *
Radiation
* Estimates based on radiation map –
Map * minor inaccuracies can be expected
** PR = Performance Ratio = PV System Derating Factor
INDIS Solar CUF Model more accurate than most other available models
• Orissa & West Bengal have very similar solar irradiation profile
• INDIS Solar CUF Model can be used to accurately estimate Average
Annual CUF for all 30 Districts in Odisha
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Orissa at a Glance
* Total 30 Districts
NASA tool, RETScreen, HOMER, and INDIS
Solar-CUF Model were run on all 30 districts
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Using Conventional Tools to Rank Top 5
Districts for Solar PV Output or Annual CUF
Rank
District in
Odisha
Annual Average CUF at Performance Ratio = 1.00 for Horizontal Surface &
Tilted Surface (without any Temperature Derating)
NASA Tool
Horizontal Surface
RETScreen
Horizontal Surface
HOMER
Horizontal Surface
NASA Tool
With Tilt
1
Malkangiri
20.86%
20.83%
20.86%
22.00%
2
Nuapada
20.84%
N/A
20.85%
21.98%
3
Sonepur
20.75%
N/A
20.75%
21.88%
4
Kalahandi
20.75%
N/A
20.75%
21.88%
5
Bolangir
20.75%
20.73%
20.75%
21.88%
• NASA Tool / RET Screen / HOMER all yield the same top 5 districts
• The % difference in Avg. Annual CUF between the top 5 districts is only 0.55%; top 2
districts are virtually identical (<0.09%) (i.e. within the measurement error)
• A Project Developer might decide to put a power plant in any of these five districts given
the very narrow range of CUF values
• However, doing this will be a costly mistake since the estimated CUF values will be lower
by as much as 4% (or Rs. 1 crore in NPV Loss in Nuapada) or 20% lower (in Kalahandi),
according to the INDIS Solar CUF Model
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Top 5 Districts for Solar PV Output or Annual CUF
Using Solar-CUF Model Developed by INDIS
Rank
District
Location
Average Annual CUF for an Actual Solar PV
Plant at Performance Ratio = 1.00 (without
any Temperature Derating)
% Reduction in
CUF Relative to the
Top Rank
Impact on Revenue for
a 5 MW Plant at Rs.
7.50 / kWh bid price
1
Malkangiri
18.44%
[at PR=0.81, CUF = 14.94%]
(baseline reference)
- Baseline Reference -
2
Puri
18.22%
[at PR=0.81, CUF = 14.76%]
-1.22%
Loss of Rs 30 Lac in
NPV (25 yr)
3
Sundergarh
18.22%
[at PR=0.81, CUF = 14.76%]
-1.22%
Loss of Rs 30 Lac in
NPV (25 yr)
4
Gajapati
17.87%
[at PR=0.81, CUF = 14.47%]
-3.08%
Loss of Rs 78 Lac in
NPV (25 yr)
5
Nuapada
17.76%
[at PR=0.81, CUF = 14.39%]
-3.71%
Loss of Rs 93 Lac in
NPV (25 yr)
• Other tools like NASA, RETScreen or Homer are predicting ~22% [@ PR=1] & 17.8% [@ PR=0.81]
• 16% lower CUF predicted using INDIS Solar-CUF Model compared to other Models
• 3 new districts become a contender for the top 5 spots in the INDIS CUF Model
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Top 5 Districts for Solar PV Output or Annual CUF
Using INDIS Solar CUF Model after Temp Correction
Relative Rankings Change between Top 5 Districts after factoring in the Local Temperature effect
(this was calculated by factoring in typical Tmax, Tmin, Humidity, and Wind Speed trends for each District)
District
Location
% Reduction in Avg. Annual CUF
relative to the Top Rank (after
Temp-Deration)
Impact on Revenue for a 5 MW
Plant at Rs. 7.50 / kWh bid
(baseline reference)
- Baseline -
Rank 2: Sundergarh
-2.3%
Loss of Rs 55 Lac in NPV (25 yr)
Rank 3: Puri
-3.5%
Loss of Rs 86 Lac in NPV (25 yr)
Rank 4: Nuapada
-4.5%
Loss of Rs 1.1 Crore in NPV (25 yr)
Rank 5: Gajapati
-5.0%
Loss of Rs 1.2 Crore in NPV (25 yr)
Rank 1. Malkangiri
• % Difference in CUF between the Districts increase when local temp trend is factored
• Sundergarh moves to 2nd rank, replacing Puri to the 3rd rank, when local weather conditions are
factored into the derating calculations
• Difference between 1st Rank (Malkangiri) and 2nd Rank (Puri) widens – Loss of Rs 55 Lac in NPV
(25 yr) vs. Rs 30 Lac
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2
4
3
5
1
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Top 5 Districts for Solar PV
•
1. Malkangiri
•
2. Sundargarh
•
3. Puri
•
4. Nuapada
•
5. Gajapati
Next Step: Identify Land in Malkangiri District
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Economic Consequences of Solar PV Plant
at Different District Locations
Rank
District
*
At PR = 0.81, Estimated
Avg. Annual
MU/MW/Year
[Minimum Specified in
Odisha Tender: 1.4976
MU/MW/Yr] *
Penalty Per Year (up
to 2017) Due to
Generation Shortfall
(Bid Price: Rs
7.5/kWh) *
Additional KW per MW *
that has to be Installed to
Generate the Minimum
Generation Target of
1.4976 MU/MW/Yr &
Avoid any Penalty
1
Malkangiri
1.323485
Rs 10.27 Lac per Year
132 KW / MW
2
Sundergarh
1.294025
Rs 12.01 Lac per Year
158 KW / MW
3
Puri
1.277086
Rs 13.01 Lac per Year
173 KW / MW
4
Nuapada
1.264565
Rs 13.75 Lac per Year
185 KW / MW
5
Gajapati
1.25720
Rs 14.18 Lac per Year
192 KW / MW
6
Sonepur
1.254991
Rs 14.31 Lac per Year
194 KW / MW
7
Bolangir
1.254991
Rs 14.31 Lac per Year
194 KW / MW
13
Kalahandi
1.201963
Rs 17.44 Lac per Year
246 KW / MW
* Based on INDIS Solar CUF Model
Economic Consequences of Choosing the Wrong District
(using NASA/RETScreen/HOMER tools):
Nuapada is ranked #2 in NASA, RETScreen, and HOMER.
1. Loss in Tariff-based Revenue; and/or
But, as per INDIS Solar CUF Model, it is ranked #4 and will
2. Generation Shortfall Penalty; and/or
generate 4.5% less kWh per MW or Rs 1.1 crore loss in
NPV (25 yr) + yearly penalty of Rs. 13.75 lac until 2017.
3. Additional KW per MW to be installed (Higher CapEx)
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Conclusion
• INDIS Solar CUF Model is relatively more accurate than other models
highly relevant for States which have a good Monsoon effect (Kerala,
Tamil Nadu, Karnataka, A.P, M.P, U.P, Maharashtra, Goa, Chattisgarh,
Orissa, Jharkhand, Bihar, Himachal Pradesh, Punjab, Haryana,
Uttarakhand)
• Can be used to accurately rank the top districts to locate a Solar
Project
• Variations in annual kWh generation / CUF within a District can be
expected due to localized weather patterns – A more accurate
estimation of CUF is required after the exact Latitude & Longitude of
the actual Site is known
• Economic consequences of choosing a wrong site location: Tariff
revenue + Penalty + CapEx can be >Rs 1 crore
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2. Land Identification / Availability
[ Where is the Land Available Within the Top Ranked District ? ]
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Table I: List of Individual Land-related Attributes
(included in GIS Solution)
List of Individual Site-related Attributes in GIS Analysis
1
Solar Radiation Data (kWh / m2 / day)
List of Individual Site-related Attributes in GIS Analysis
7
Pollution Level Map (broad level)
Aerosols and/or SO4
2
Land Topography
Slope
Any major industrial activity
8
Soil Map (broad level)
9
Drought & Aridity Patterns (broad level)
10
Roads & Accessibility
Elevation
Aspect, Orientation, Shape
Contours
3
Local Land Use Patterns
Forest
National Highways
Village
State Highways
Wasteland
Local Roads
Water Bodies
11
Railway Lines
12
Water Bodies & Accessibility
13
Administrative Boundary
Economic Activity (Agriculture / Industry)
4
Grid & Substation Maps
5
Local Climate Patterns
Temperature / Wind
State & District
Rainfall / Humidity
6
Seismic Zone Map
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Use Satellite Data to Identify, Quantify & Rank Various Land Options by
Analyzing Over 20 Different Factors
Satellite Data
Soil Map
Contour
Shadow Analysis
Soiling Tendency
Road
Slope
Grid
Zone 3
Aspect
Irradiation Data
Water Drainage Land Use Pattern
CUT
Pollution
Water Access
FILL
Zone 3
Zone 2
Site
Forest Fires
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Seismic Zones
Rainfall Patterns
Cut-Fill Analysis
Power Evacuation
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Using Satellite – GIS to Identify Best Land Sites Over a Very Large Area
INDIS has developed a GIS-based Land risk assessment & Site selection tool to help identity the most optimal Solar Land Sites
within a State or a District. This tool can also be used for Site Identification, Site Planning, Site Leveling, Power Evacuation, and
Water Management.
Example Case Study in Goa
Goa - Solar Site Favorability Index Map
Site Identified for 20 MW PV Plant
Goa State Map
Beta Version
Zoom
in
Resolution: 30m
Scale: 1 cm = 400 m
Factors*: Road; Grid; Water; Local Land
Use Pattern; Land Topography (Slope,
Aspect, Contour); Soil Properties; Soiling
Probability, Local Climate (Temp, Rainfall,
Wind); Local Pollution Levels (industries,
aerosol, SO4); Radiation (optional)
Favorability Index
Higher (Red) is Better
* Business Proprietary
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2. Land Identification / Availability
in Malkangiri District in Odisha
Malkangiri District
Top District (1st Rank)
Potential Cheap Land
Areas for Solar PV Projects
Figures & Areas not to Scale
• Analysis based on preliminary analysis of Satellite-Derived Data
• Positive Impact on CapEx, OpEx & Tariff-based Revenue: NPV savings of Rs.
25 lac per MW – Rs 50 lac per MW is estimated by using this detailed approach
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Other Advantages of
INDIS Satellite-GIS Tool
• (i) Speed of execution – using satellite data one can
quickly scan a whole State to identify the potential sites
of interest for a solar project based on land usage;
• (ii) First mover advantage – by being the first buyer of
the land before anyone else gets to the local Village, you
will probably get land at cheaper rates;
• (iii) Cheaper land rates – by analyzing land use patterns
(using satellite data), we can identify land areas that are
non-agricultural or far from agricultural sites, i.e. of less
economical value – and therefore lower rates.
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3. PV Plant Operational Issues
[ Maximizing PV Plant Generation ? ]
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PV Plant Operational Issues
•
•
•
•
•
•
Module Mismatch [Product / Performance]
System Layout (# of Series vs. Parallel)
Shading
Soiling
Cleaning
Inverter tripping / Grid Outage / Electrical
Issues
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PV Plant Operational Issues:
Soiling & Cleaning
Brand New Panel
Before Cleaning
Bad Cleaning
* Generation Losses
Due to Soiling
3% to 15%
* Location Specific
* Cleaning regime
dependent
Good Cleaning
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Even After Good Cleaning
30
PV Plant Operational Issues:
Monthly Generation Patterns
Comparative Generation for Feb Month of 3 PV Plants in Rajasthan
Normalized Generation (kWh / kWh on
1st day of the Month)
1.3
1.2
1.1
1
0.9
PV Plant #1
PV Plant #2
PV Plant #3
0.8
0.7
0.6
0 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 26 27 28 29 30 31
Day of the Month
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Solar PV Plant Design
• Complete Design Services
– DC Side / AC Side / Module / Inverter
• Bill of Materials
• Upcoming: Cleaning Solutions
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THANK YOU
Contact us regarding
* INDIS Solar CUF Model for kWh Generation Estimation &
* INDIS GIS Tool for Site Identification & Land Assessment
for existing projects or future Solar PV Projects
* Solar PV Plant Design Services
Akash
Email: akash@indisllc.com / akashslc@yahoo.com
Phone: +91 9718112443
New Delhi (India)
www.indisllc.com // www.akashcleantech.com
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