Is Overproduction Costing You? - AWS Truepower, a UL · PDF fileIs Overproduction Costing You?...

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©2014 AWS Truepower, LLC

Is Overproduction Costing You? A review of the impacts of solar resource data on financing

and revenues for PV plants

Marie Schnitzer

Paul Thienpont

Vice President of Consulting Services

Meteorologist

Presented by:



Albany

New York (HQ)

Albany

New York (HQ)

Barcelona

Spain

Barcelona

Spain

Bangalore

India

Bangalore

India

Company Snapshot

Rio de Janeiro

Brazil

Rio de Janeiro

Brazil

Warsaw

Poland (*)

Warsaw

Poland (*)

Istanbul

Turkey (*)

Istanbul

Turkey (*)

Buenos Aires

Argentina (*)

Buenos Aires

Argentina (*)

Denver

Colorado

Denver

Colorado

• Renewable energy professionals providing technical consulting, software, data, and

advisory services for the wind and solar energy markets

• Established in 1983; over 30 years of renewable energy industry experience

• Assessed over 100,000 MW

• Project roles in over 80 countries

• Offices around the globe and *with partnerships in Argentina, Poland and Turkey



Key Messages

• Obtaining an accurate,

reliable solar resource

assessment

• Overproduction from

unreliable data sources can

result in lost revenue

• Overproduction can result in

lost returns



Financial Impacts

Power Purchase Agreements (PPA)

• Over/Under production

penalties

• PPA bid process

Financial Models

• Tax Equity Financing

• Debt to Equity ratios

• Debt Service Coverage Ratio

(DSCR)

Cost of Capital and Rate of Return



Investor Interests

Equity Investors

Long-Term Productions

Estimates

• P50 Estimates

Tax Equity Investors

Interests are greatest in

the beginning of the

project life-cycle

• P50 Estimates

Debt Lenders

Interested only in the

minimum ability to pay

back loan

• P90/99 Estimates



How do you quantify these risks?And how the variables impact your bottom line.



Study Approach

Solar Resource

• GHI, DNI, DHI

• POA

Energy

Revenue

Financial Model



Resource Assessment Methods

Irradiance Data

NASA SSE

National Solar Radiation Database

(NSRDB) - TMY 3

Satellite Derived Datasets

• Publicly available

• Commercially available (for

purchase)

Measured Reference Networks

• USCRN

• NREL

On-Site Measurements

Site Specific Typical Meteorological Year

AWS Truepower Analysis

• Onsite measurements

• Satellite derived datasets

• Measured reference networks

• Conversion to Plane of Array

(POA)



Study Approach

Solar Resource

Energy• Losses

• Uncertainty

Revenue

Financial Model



Energy Modeling

Satellite

Site Specific

TMY

Site Specific

(Advanced)

TMY

Reference

StationOnsite

Measurement

Energy

Model

Energy

Simulation

Model

Plant

Specifications

Environmental

Losses

Environmental

& System

Losses

Uncertainty

from all inputs

Annual Energy

Prediction P50, P90, P99



Sources of Energy Uncertainty

Annual

Degradation

(0.5 – 1 %)

Transposition

To Plane of Array(0.5 – 2%)

Energy Simulation, Plant

Losses

(3 – 5 %)

Solar Resource Uncertainty

(5 – 17%)



Energy Estimates and Probability of Exceedance

P90

P90

Probability of exceedance:

the level of confidence that a

plant’s actual energy production

will be at least a certain value

Based on project uncertainty

High Quality Resource DataHigh Quality Resource Data

Reduced uncertaintyReduced uncertainty

Increased value of P90 (99)Increased value of P90 (99)



Study Approach

Solar Resource

Energy

Revenue• PPA

• Over/Under

Financial Model



PPA Pricing

Time of Day Pricing

• Base Price based on Locational

Marginal Pricing (LMP)

• Time of Day Pricing (TOD) multipliers

are used to adjust base price based on

time of day and seasonal adjustments

Production Thresholds

• PPA’s typically include maximum annual

production restrictions

• Restrictions are commonly applied at 110%

of the estimated annual P50 plant production

• Net metering caps



Study Approach

Solar Resource

Energy

Revenue

Financial Model

• CapEx

• OpEx

• Debt Sizing



Financial Model

Capital Expenses:

• EPC (full wrap)

• Permitting

• Developer fees

• Land lease

• Other

Production Estimates:

• P50, P90, P99 Energy estimates

• PPA pricing

Operational Expenses:

• Operating costs

• General administration

Project Finance Structures

• Debt finance

− Debt Service Coverage Ratio

• Tax equity or cash equity finance

− Internal Rate of Return



Quantifying financial riskCase study results for three project locations



Case Studies: Methodology

Three sites collocated with United States Climate Reference Network (USCRN)

measured reference stations were selected for evaluation based on geographic location

and consistency of the observations.

• Three resource methods were examined for each location:

– Actual resource data (measured)

– Satellite Derived TMY

– NSRDB TMY3

• Uncertainty and Probability of Exceedance (P-tables) were assessed for each

method

• Energy was simulated in PVsyst using the AWS Truepower standard approach for

each location



Case Study: Methodology

Plant Design:

• 12.5 MWDC

• 10 MWAC

• DC/AC Ratio: 1.25

• Generic 300 W polycrystalline module

• Generic 500 kW inverter

• Row tilt optimized for each location using PVsyst

• Modeled without near shading

• Plant loss assumptions were applied consistently for each project location and resource

analysis



Solar and Met Resource Variation

Merced, CA

Millbrook, NY

Tucson, AZ

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

GHI DNI DHI POA Temp

Dif

fere

nce

Fro

m A

ctu

al (%

)

Satellite

TMY3

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%


Dif

fere

nce

Fro

m A

ctu

al (%

)

Satellite

TMY3

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%


Dif

fere

nce

Fro

m A

ctu

al (%

)

Satellite

TMY3



Energy Variation

Merced, CA

Satellite

Tucson, AZ

Satellite

Millbrook, NY

TMY3

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

Gross Energy Long-Term Net Energy

Dif

fere

nce

Fro

m A

ctu

al

(%)

Merced, CA

TMY3

Tucson, AZ

TMY3

Millbrook, NY

Satellite



Case Study: PPA Revenue Assumptions

Financial model used LMP data to calculate baseline PPA pricing and TOD

based off of the Day Ahead Market (DAM) prices

PPA Pricing Assumptions:

• Base price: $66/MWh

• TOD and seasonal multipliers

• PPA overproduction: Assumed 75% decreased payment structure beyond

110% of the guaranteed energy production level (P50 Production)

Energy > Annual P50 Production = 0.75 x (Base Price x TOD Multiplier)



Case Study: PPA Revenue

• TOD revenue is based of long-term energy 8760 results for each resource file utilizing the

TOD and seasonal multipliers to the baseline PPA price

• Lost revenue is based off of using 110% of the estimated long-term net energy P50

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

Financed With Satellite TMY Financed with NSRDB TMY3

Dif

fere

nce

in

Re

ve

nu

e F

rom

Act

ua

l(%

)

TOD Revenue Lost Revenue to Cap



Case Study: Debt Finance Structure Model Assumptions

Financial Model Assumptions:

• CAPEX: $2.2/WAC installed capacity

• OPEX: $25/kWAC/year installed capacity

Debt sizing:

• Debt ratio sized using a DSCR of 1.0 and P99 production estimates

Financial Models:

• Three independent models were created:

1. Actual resource data

2. Satellite derived TMY

3. NSRDB TMY3

Analysis:

• Satellite and TMY3 model financing assumptions were stressed using the actual

resource data



Case Study: Debt Finance Structure Key Metrics

Debt Ratio:

• The amount of acquired debt

Projected IRR:

• The estimated IRR for the cash equity investor when financing with each resource

dataset

Actual IRR:

• The realized IRR for the cash equity investor when running the actual resource

data file through the financial model for the Satellite TMY and NSRDB TMY3



Case Study Results: Debt Financing – Merced, CA

-7.0%

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

Financed With Satellite TMY Financed With NSRDB TMY3D

iffe

ren

ce F

rom

Gro

un

d C

orr

ect

ed

(%

)

Debt Ratio Projected IRR Actual IRR

The project

should have

financed using

3% more debt

The forecasted

IRR was 2%

lower than

optimal results

As a result the

actual IRR was

0.5% lower than

optimal results



Case Study: Tax Equity Financial Model Assumptions

Financial Model Assumptions:

• CAPEX: $2.2/WAC installed capacity

• OPEX: $25/kWAC/year installed capacity

Tax Equity Investment:

• Tax equity investment sized assuming an 8.0% IRR

Financial Models:

• Three independent models were created:

1. Actual resource data

2. Satellite derived TMY

3. NSRDB TMY3

Analysis:

• Satellite and TMY3 model financing assumptions were stressed using the actual

resource data.



Case Study: Tax Equity Finance Structure Key Metrics

Tax Equity Contribution:

• The difference in tax equity contribution

Projected IRR:

• The estimated IRR for the cash equity investor when financing with each resource

dataset

Actual IRR:

• The realized IRR for the cash equity investor when running the actual resource

data file through the financial model for the Satellite TMY and NSRDB TMY3



Case Study Results: Tax Equity Financing – Merced, CA

The project should

have financed using

0.3% more Tax

Equity Contribution

The forecasted IRR

was 0.9% lower than

optimal results

As a result the

actual IRR was

0.5% lower than

optimal results

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

Financed With Satellite TMY Financed With NSRDB TMY3

Dif

fere

nce

Fro

m O

pti

ma

l (%

)

Tax Equity Contribution Projected IRR Actual IRR



Conclusions

Site Specific Resource yields more

accurate P50 analysis and improved

uncertainty bounds

• Many sources of unreliable data

resulting in higher uncertainty

• Overproduction results in lost

revenue

• Overproduction results in lost

returns for both debt, tax equity

and cash equity finance



Thank You

+1 518-213-0044

[email protected]

awstruepower.com

Marie Schnitzer

Vice President of

Consulting Services

Paul Thienpont

Meteorologist

Is Overproduction Costing You? - AWS Truepower, a UL · PDF fileIs Overproduction Costing You?...

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