* solar resource cuf assessment * land site assessment * pv

1Business Proprietary

* Solar Resource CUF Assessment

* Land Site Assessment

* PV Plant Operation Assessment

AkashFounder, INDIS, LLC

B. Tech (IIT B), MS (USA), MBA (USA)

[email protected] / +91 9718112443 (India)

Salt Lake City, UT (USA) / New Delhi (India)

www.indisllc.com // www.akashcleantech.com


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


Questions that will be Answered in this Presentation

Slide 27What are the benefits of using Satellite-GIS tools for land identification?8

Slide 30What are the soiling & cleaning issues for a PV Plant?9

Slide 26What are the potential cheap land areas that are most suitable for solar PV plants

in the top Odisha district?

7

Slide 31What are the kWh generation issues & variations in a PV Plant?10

Slide 24What is the most effective way to identify cheap, available land in a given state or

district for solar PV projects?

6

Slide 20What are the economic consequences of setting up a solar PV plant in the wrong

district or a district chosen using NASA/RETScreen/HOMER tools?

5

Slide 20What is the estimated avg. annual kWh per MW for the top 5 districts in Odisha?4

Slide 18What is the effect of local weather conditions (temp, wind, humidity) on relative

annual CUF & relative ranking of districts in Odisha?

3

Slide 17What are the top 5 districts in Odisha with highest annual CUF?2

Slide 14Which is the most accurate Model to estimate avg. annual CUF?1

Refer to

Slide No.

QuestionNo.


1. Solar Potential & Land Siting[ Which District ? ]


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


INDIS Solar CUF Model

tested & validated on a

pan-India basis � 6 sites

located across India

12.3%

16.9%

17.7%

15.4%

12.7%

14.8%

• 365 days data available for 6 Solar PV Sites

• CUF for these 6 Solar PV Power Plant sites varied from 12% to 18%.

• None of the existing Models were predicting such a wide variation in CUF across India

Validating INDIS Solar-CUF Model


0.70 - 1.00Age

0.80 – 1.00Grid Uptime

0.95 - 1.00Sun-tracking

0.80 - 1.00Shading

0.80 - 0.995System availability

0.80 - 0.995Soiling

0.98 - 0.993AC wiring

0.97 - 0.99DC wiring

0.99 - 0.997Diodes and connections

0.97 - 0.995Mismatch

0.80 - 0.98Inverter and Transformer

0.80 - 1.00

PV module nameplate

DC rating

Range of ValueDerate Factors

Overall Derating Factor = 0.70 – 0.83

100+ PV Plants Worldwide

CUF = 12.3%

Asansol

Month-wise Generation of Actual 1 MW Plant

Model

Validation


Measured Annual CUF in Asansol (West Bengal)

• 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?

http://www.cercind.gov.in/2011/Whats-New/PERFORMANCE%20OF%20SOLAR%20POWER%20PLANTS.pdf

WBGEDCL / Asansol 1 MW Solar PV Project - Measured (Actual) CUF vs. Predicted

CUF from various Sources [PR = 0.75]

15.0% 15.1%

16.0%15.8%

15.5%

12.39%12.29%

10%

11%

12%

13%

14%

15%

16%

17%

Measured /

Actual

INDIS CUF

Model

Model 1 -

RETScreen

Model 2 -

HOMER

NREL

Radiation

Map *

Solar GIS * 3Tier *

% CUF

* Estimates based on radiation map –

minor inaccuracies can be expected


Actual (Measured) Data from Operational Solar PV Power Plants

1,740,480*

(total for both)

212Crystalline

Silicon

2*Amritsar,

Punjab

Azure Power #2

(Dec ‘10 – Jun ‘11)

*2nd MW added in Nov

2b

730,500242Crystalline

Silicon

1Asansol,

West

Bengal

West Bengal

WBGEDCL # 2

(Sept ‘10 – Apr ‘11)

1b

12.74%3,348,446365Crystalline

Silicon

3Kolar,

Karnataka

Karnataka Power Corp

Ltd (KPCL)

(Jan ‘10 – Dec ‘10)

6

14.83%3,897,680365Crystalline

Silicon

3Belgaum,

Karnataka

Karnataka Power Corp

Ltd (KPCL)

(Jan ‘10 – Dec ‘10)

5

17.69%7,473,378352Crystalline

Silicon, Thin

Film, CPV

5Khimsar,

Rajasthan

Reliance Industries

(July ‘10 – June ‘11)

4

15.39%1,347,840365a-Si Thin

Films

1Chandrapur,

Maharashtra

Mahagenco

(May ‘10 – Apr ‘11)

3

16.92%1,571,610

365

Crystalline

Silicon

1Amritsar,

Punjab

Azure Power #1

(Dec ‘09 – Nov ‘10)

2a

12.29%1,130,700

365

Crystalline

Silicon

1Asansol,

West

Bengal

West Bengal

WBGEDCL # 1

(Sept ‘09 – Aug ‘10)

1a

Actual

CUF %

Actual

Generation in

Units during

the Period

Days in

Operation

PV

Technology

Used

Nominal

Capacity

(MW)

Project Site

(nearest city)

Project DeveloperNo.

Source: MNRE &

CERChttp://www.mnre.gov.in/pdf/Grid-Solar-Demo-Performance.pdf

http://www.cercind.gov.in/2011/Whats-New/PERFORMANCE%20OF%20SOLAR%20POWER%20PLANTS.pdf


INDIS Solar CUF Model (Prediction) vs. Measured

Grid / Inverter Outage

issues explains the

lower actual CUF

Grid / Inverter Outage

issues explains the

lower actual CUF

Comments

1.1%

1.2%

-0.3%

0.5%

-0.9%

0.1%

Difference

CUF %

8.39%

8.36%

-1.97%

3.49%

-5.12%

0.78%

As % of

Measured CUF

Error Margin

13.81%12.74%Kolar6. KPCL

16.07%14.83%Belagum5. KPCL

17.34%17.69%Khimsar4. Reliance

15.93%15.39%Chandrapur3. Mahagenco

16.05%16.92%Amritsar2. Azure

12.39%12.29%Asansol1. WBGEDCL

INDIS ModelMeasuredLocationDeveloper

CUF %CUF %SiteProject

Avg. Error: 2.32%

Std Dev. In Error: 5.50%

% CUF = 100 x Total kWh Generated Annually

365 * 24 * MW * 1000


NREL Map is predicting >21% Avg. Annual

CUF for most of Orissa (at PR = 1.00)

For Asansol, at PR =0.75, NREL Map

estimates Avg. Annual CUF of ~16%

Other Solar Irradiation Data Sources: NREL Irradiation Map

Orissa

West

Bengal

* Orissa & West Bengal have very

similar solar irradiation profile

Source: NREL GHI India Radiation Map (2009)

NREL GHI Irradiation Map of Eastern India

Asansol


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%

Other Solar Irradiation Data Sources: Private Party Providers [ SolarGIS ]

Source: India Solar Handbook, Bridge to India

West

Bengal

Orissa

SolarGIS Irradiation

Map of Eastern India

Orissa & West Bengal have very

similar solar radiation profile


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%

Other Solar Irradiation Data Sources: Private Party Providers [ 3Tier ]

Orissa

West

Bengal

Source: http://www.3tier.com/static/ttcms/us/documents/publications/validations/solar_india_validation.pdf

3Tier Irradiation Map of Eastern India

Orissa & West Bengal have very

similar solar irradiation profile


INDIS Solar CUF Model more accurate than most other available models

INDIS Solar CUF Model vs. Other 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

WBGEDCL / Asansol 1 MW Solar PV Project - Measured (Actual) CUF vs. Predicted

CUF from various Sources [PR = 0.75**]

15.0% 15.1%

16.0% 15.8%15.5%

12.39%12.29%

10%

11%

12%

13%

14%

15%

16%

17%

Measured /

Actual

INDIS CUF

Model

Model 1 -

RETScreen

Model 2 -

HOMER

NREL

Radiation

Map *

Solar GIS * 3Tier *

% CUF

* Estimates based on radiation map –

minor inaccuracies can be expected

** PR = Performance Ratio = PV System Derating Factor


Orissa at a Glance

* Total 30 Districts

NASA tool, RETScreen, HOMER, and INDIS

Solar-CUF Model were run on all 30 districts


Using Conventional Tools to Rank Top 5

Districts for Solar PV Output or Annual CUF

21.88%20.75%20.73%20.75%Bolangir5

21.88%20.75%N/A20.75%Kalahandi4

21.88%20.75%N/A20.75%Sonepur3

21.98%20.85%N/A20.84%Nuapada2

22.00%20.86%20.83%20.86%Malkangiri1

NASA Tool

With Tilt

HOMER

Horizontal Surface

RETScreen

Horizontal Surface

NASA Tool

Horizontal Surface

District in

Odisha

Rank

Annual Average CUF at Performance Ratio = 1.00 for Horizontal Surface &

Tilted Surface (without any Temperature Derating)

• 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


Top 5 Districts for Solar PV Output or Annual CUF

Using Solar-CUF Model Developed by INDIS

-3.71%

-3.08%

-1.22%

-1.22%

(baseline reference)

% Reduction in

CUF Relative to the

Top Rank

Loss of Rs 93 Lac in

NPV (25 yr)


NPV (25 yr)


NPV (25 yr)


NPV (25 yr)

- Baseline Reference -

Impact on Revenue for

a 5 MW Plant at Rs.

7.50 / kWh bid price

17.76%

[at PR=0.81, CUF = 14.39%]

17.87%

[at PR=0.81, CUF = 14.47%]

18.22%

[at PR=0.81, CUF = 14.76%]

18.22%

[at PR=0.81, CUF = 14.76%]

18.44%

[at PR=0.81, CUF = 14.94%]

Average Annual CUF for an Actual Solar PV

Plant at Performance Ratio = 1.00 (without

any Temperature Derating)

Nuapada

Gajapati

Sundergarh

Puri

Malkangiri

District

Location

5

4

3

2

1

Rank

• 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


Top 5 Districts for Solar PV Output or Annual CUF

Using INDIS Solar CUF Model after Temp Correction

Loss of Rs 1.2 Crore in NPV (25 yr)

Loss of Rs 1.1 Crore in NPV (25 yr)

Loss of Rs 86 Lac in NPV (25 yr)

Loss of Rs 55 Lac in NPV (25 yr)

- Baseline -

Impact on Revenue for a 5 MW

Plant at Rs. 7.50 / kWh bid

-5.0%

-4.5%

-3.5%

-2.3%

(baseline reference)

% Reduction in Avg. Annual CUF

relative to the Top Rank (after

Temp-Deration)

Rank 5: Gajapati

Rank 4: Nuapada

Rank 3: Puri

Rank 2: Sundergarh

Rank 1. Malkangiri

District

Location

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)

• % 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


1

2

5

3

4

Top 5 Districts for Solar PV

• 1. Malkangiri

• 2. Sundargarh

• 3. Puri

• 4. Nuapada

• 5. Gajapati

Next Step: Identify Land in Malkangiri District


Economic Consequences of Solar PV Plant

at Different District Locations

194 KW / MWRs 14.31 Lac per Year1.254991Bolangir7

194 KW / MWRs 14.31 Lac per Year1.254991Sonepur6

192 KW / MWRs 14.18 Lac per Year1.25720Gajapati5

Rs 17.44 Lac per Year





Penalty Per Year (up

to 2017) Due to

Generation Shortfall

(Bid Price: Rs

7.5/kWh) *

246 KW / MW

185 KW / MW

173 KW / MW

158 KW / MW

132 KW / MW

Additional KW per MW *that has to be Installed to

Generate the Minimum

Generation Target of

1.4976 MU/MW/Yr &

Avoid any Penalty

1.201963

1.264565

1.277086

1.294025

1.323485

At PR = 0.81, Estimated

Avg. Annual

MU/MW/Year

[Minimum Specified in

Odisha Tender: 1.4976

MU/MW/Yr] *

Kalahandi

Nuapada

Puri

Sundergarh

Malkangiri

District

13

4

3

2

1

Rank

*

Economic Consequences of Choosing the Wrong District

(using NASA/RETScreen/HOMER tools):

1. Loss in Tariff-based Revenue; and/or

2. Generation Shortfall Penalty; and/or

3. Additional KW per MW to be installed (Higher CapEx)

* Based on INDIS Solar CUF Model

Nuapada is ranked #2 in NASA, RETScreen, and HOMER.

But, as per INDIS Solar CUF Model, it is ranked #4 and will

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.


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


2. Land Identification / Availability[ Where is the Land Available Within the Top Ranked District ? ]


Table I: List of Individual Land-related Attributes

(included in GIS Solution)

Solar Radiation Data (kWh / m2 / day)1

Seismic Zone Map 6

Rainfall / Humidity

Temperature / Wind

Local Climate Patterns5

Grid & Substation Maps 4

Economic Activity (Agriculture / Industry)

Water Bodies

Wasteland

Village

Forest

Local Land Use Patterns3

Contours

Aspect, Orientation, Shape

Elevation

Slope

Land Topography2

List of Individual Site-related Attributes in GIS Analysis

State & District

Administrative Boundary13

Water Bodies & Accessibility12

Railway Lines11

Local Roads

State Highways

National Highways

Roads & Accessibility10

Drought & Aridity Patterns (broad level)9

Soil Map (broad level)8

Any major industrial activity

Aerosols and/or SO4

Pollution Level Map (broad level)7

List of Individual Site-related Attributes in GIS Analysis


ContourSatellite Data Shadow Analysis Slope Aspect

Soil Map

Irradiation Data

Soiling Tendency

Pollution

Forest Fires

Land Use Pattern Water AccessRoad Grid Water Drainage

Use Satellite Data to Identify, Quantify & Rank Various Land Options by

Analyzing Over 20 Different Factors

Zone 2

Zone 3

Zone 3

Site

Seismic Zones Rainfall Patterns

CUTFILL

Cut-Fill Analysis Power Evacuation


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.

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)

Goa - Solar Site Favorability Index Map

Resolution: 30m

Scale: 1 cm = 400 m

* Business Proprietary

Zoom

in

Beta Version

Favorability Index

Higher (Red) is Better

Goa State MapSite Identified for 20 MW PV Plant

Using Satellite – GIS to Identify Best Land Sites Over a Very Large Area

Example Case Study in Goa


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

2. Land Identification / Availability

in Malkangiri District in Odisha

• 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


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.


3. PV Plant Operational Issues[ Maximizing PV Plant Generation ? ]


PV Plant Operational Issues

• Module Mismatch [Product / Performance]

• System Layout (# of Series vs. Parallel)

• Shading

• Soiling

• Cleaning

• Inverter tripping / Grid Outage / Electrical

Issues


PV Plant Operational Issues:

Soiling & Cleaning

Brand New Panel Before Cleaning Bad Cleaning

Good Cleaning Even After Good Cleaning

* Generation Losses

Due to Soiling

3% to 15%

* Location Specific

* Cleaning regime

dependent


PV Plant Operational Issues:

Monthly Generation Patterns

Comparative Generation for Feb Month of 3 PV Plants in Rajasthan

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

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

Norm

alized Generation (kWh / kWh on

1st day of the Month)

PV Plant #1

PV Plant #2

PV Plant #3


Solar PV Plant Design

• Complete Design Services

– DC Side / AC Side / Module / Inverter

• Bill of Materials

• Upcoming: Cleaning Solutions


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: [email protected] / [email protected]

Phone: +91 9718112443

New Delhi (India)

THANK YOU

www.indisllc.com // www.akashcleantech.com

* solar resource cuf assessment * land site assessment * pv

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