ieee power energy society 2012 presentation - validation of forecasting technologies

New Energy Horizons Opportunities and Challenges

Validation of Solar PV Power

Forecasting Methods for High

Penetration Grid Integration

IEEE Power and Energy Society General Meeting

July 26, 2012

James Bing, NEO Virtus Engineering, Inc.

Obadiah Bartholomy, Sacramento Municipal Utility District

Pramod Krishnani, Belectric North America Inc.


Project Sponsors and Partners

CPUC

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CSI Solar RD&D Program www.calsolarresearch.ca.gov

CSI RD&D Program Manager

http://www.gosolarcalifornia.org/

http://www.calsolarresearch.ca.gov/


TOPICS

• PROJECT BACKGROUND • TECHNICAL BACKGROUND

• EXPERIMENTAL DESIGN

• PRELIMINARY RESULTS

• ONGOING WORK

• CONCLUSIONS

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PROJECT BACKGROUND

• California Public Utilities

Commission High Penetration PV

Program

• Sacramento Municipal Utility District

100MW Feed-in-Tariff

• Parallel Research Effort (DOE FOA:

Improving the Accuracy of Solar Forecasting)

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CPUC High Penetration PV Program

• CPUC established CSI RD&D Program in 2007 – Allocated $50 million for research, development,

demonstration and deployment (RD&D) projects to further the overall goals of the CSI Program

– Adopted the “CSI RD&D Plan” • CSI RD&D Plan established:

– Goals and objectives – Allocation guidelines for project funding – Criteria for solicitation, selection and project funding

• Three Target Areas Established for Program Funding: – Grid-Integration: 50-65% – Production Technologies: 10‐25% – Business Development and Deployment: 10‐20%

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Sacramento Municipal Utility

District 100MW Feed-in-Tariff

• SMUD max summer load ~3GW

• Feed in Tariff = 100MW or ~3% of peak load

• ~36 MW of hidden “behind the meter” PV

• SMUD capacity expected to grow, goal of 125 MW net metered PV by 2016

• Power integration issues are very (grid) site dependant: Forecast capability addresses/informs planning, automation and curtailment/mitagation issues

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NDFD Grid, Primary & Secondary Sites

SMUD Service Territory

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Sacramento Municipal Utility District

Multi-forecaster Benchmarking

•SMUD will be using irradiance network and FiT systems to benchmark forecast performance for 4 forecasters in addition to Neo Virtus beginning in August

•Forecasters will provide hourly forecasts, uncertainty and 5 minute variability on hour ahead up to 5 days in advance for each irradiance sensor and FiT system output

•Sandia Labs will quantify forecast accuracy for various weather conditions and timeframes

•Goal is to understand broadly state of the art in forecast accuracy and ability to trust forecast performance for different timeframes

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TOPICS

• PROJECT BACKGROUND

• TECHNICAL BACKGROUND • EXPERIMENTAL DESIGN


• ONGOING WORK

• CONCLUSIONS

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TECHNICAL BACKGROUND

• Irradiance Fundamentals

• PV Power Simulation Fundamentals

• Current Solar Forecasting Methods

• NEO Virtus Day Ahead Forecasting

of AC Power

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Irradiance Fundamentals

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• Direct Normal Irradiance (DNI) • Diffuse Horizontal Irradiance (DHI) • Global Horizontal Irradiance (GHI): GHI = DNI cos ϴ +DHI • 1000W/m2 GHI ≈ Full Scale or 1pu (nominal at sea level)


Irradiance Fundamentals

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• Plane of Array Irradiance (POA) = Incident Irradiance on PV array • POA can be calculated with a knowledge of:

– Direct Norman Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI) and – PV system Azimuth, Tilt, Lat/Lon, Shading Obstruction, Date & Time


PV Power Simulation Fundamentals

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Current Solar Forecasting Methods

Technology Time Horizon Coverage

Satellite 12hr to 7 days Global

Mesoscale

NWP models

12hrs to months Global/

Regional

Aggregated

Ground Sensors

1hrs to 3hrs Regional

SkyImager 30min to 3hrs 2 to 10km radius

Array Scale

Sensors

1 to 30 minutes Array Size

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NEO Virtus Method for Day Ahead

Forecasting AC Power

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Collect Forecast Cloud-cover (NEO uses NDFD data):

Cloud fraction (%)

Collect PV Array Data:

Azimuth, Tilt, Lat/Lon, Shading, Capacity, Inverter, etc.

Simulate:

• Sun Position Model: zenith & azimuth angles (º)

• Irradiance Transmittance Model: DNI & DHI (W/m^2)

• PV Array Geometry & Shading Model: POA (W/m^2)

• Photovoltaic Conversion Model: DC Power (Wdc)

• Inverter & Losses Model: AC Power (Wac)

CLOUDCOVER

FORECAST+

PV ARRAY SPECS

IRRADIANCE

TRANSMITTANCE

MODEL

SOLAR

GEOMETRY

MODEL

PV ARRAY

GEOMETERY+

SHADING

PV CONVERSION

MODEL

INVERTER+

LOSSES MODEL

AC POWER

OUT


TOPICS


• TECHNICAL BACKGROUND

• EXPERIMENTAL DESIGN • PRELIMINARY RESULTS

• ONGOING WORK

• CONCLUSIONS

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EXPERIMENTAL DESIGN

Build & deploy irradiance monitoring network

Monitor utility scale PV system power production

Validate irradiance forecast performance territory-wide

0-3 hour ahead

Day ahead

Validate PV power production forecast for SMUD

Feed-In-Tariff (FIT) PV Systems

0-3 hour ahead

Day ahead

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Primary & Secondary Irradiance

Monitoring Station Specifications

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• Irradiance Monitoring Network

– 5 primary monitoring stations • GHI, DHI, DNI

• Ambient temperature

• 1 minute averages

– 66 secondary monitoring stations • GHI

• Ambient temperature

• 1 minute averages


Secondary Station Design & Fabrication

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• Irradiance Monitoring

Network – 5km grid Sacramento

– NDFD Lambert conformal

projection

– Installed on SMUD utility

poles

– Automated data retrieval

via cellular modem

– 14 month continuous data


Primary & Secondary Monitoring Stations

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Day Ahead Irradiance & PV Power

Forecast Validation

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1-minute, 5km resolution GHI & Temp database

NEO Forecast vs. Measured irradiance validation

NEO Forecast vs. Measured PV

power production validation


Solar Forecasting Error Analysis

Relative (percent) Error*:

RMSE/Capacity

MAE/Capacity

MBE

Data were filter for zenith angle >90 (no night

time data were used).

No plant availability information was

provided: we assumed 100% availability. * Hoff, Perez, Kleissl, Renne, Stein: “Reporting of Relative Irradiance Prediction Dispersion Error”

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TOPICS




• PRELIMINARY RESULTS • ONGOING WORK

• Territory-wide measured irradiance data

animation

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PRELIMINARY RESULTS

• NEO 20-36 Hour (Day Ahead) Forecasting

Model PV Production 5/1/12 to 7/2/12

• R2 = 0.9398

• RMSE/Capacity = 8.61%

• MAE/Capacity = 5.57%

• MBE = -2.39%

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NEO’s Day Ahead Forecast vs. Measured One SMUD FIT PV System 5/1/12 to 7/2/12

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R² = 0.9398

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Fore

cast

ac

pro

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ctio

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% c

apac

ity)

Measured ac production (% capacity)

SMUD FIT PV Array


NEO’s Day Ahead Forecast vs. Measured PV Production (one system)

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6/3/2012 6/4/2012 6/5/2012 6/6/2012 6/7/2012 6/8/2012

BP01 measured ac (avg 1 min)

BP01 forecast ac


Unforeseen Issues, Lessons Learned

• Shadows cast by overhead lines and crossbars causing “artifacts” in daily data

– Sandia & SMUD are working out algorithm to filter data

• Data logger flash memory failures

– Code was changed and manufacturer replaced failed units

• Secondary sensors cannot be cleaned economically

– Precision Spectral Pyrranometers which can be cleaned and maintained are used for system-wide calibration

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Overhead Wire Shading Anomalies

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Tem

pe

ratu

re ( C

)

Irra

dia

nce

(W

/m2)

Time (Pacific Standard Time)

Global Horizontal Radiation 6/25/11 SMUD/NEO Virtus Secondary Station #64

DAS# 64 POLE# UD122802 LAT: 38.59662 LON: -121.48561 Temp (⁰C)

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Irra

dia

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(W

/m2)

Time (Pacific Standard Time)

Global Horizontal Radiation 07/09/11 SMUD/NEO Virtus Secondary Station #64

DAS# 64 POLE# UD122802 LAT: 38.59662 LON: -121.48561 Temp (⁰C)


Global Network Calibration

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• Eppley Precision Spectral Pyranometer (PSP)


TOPICS





• ONGOING WORK • CONCLUSIONS

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ONGOING WORK

Development of 0-3 hour ahead forecasts

using sensor network

Territory-wide GHI

Feed in Tariff PV Systems Production

Filtering of signal noise caused by cross arm

and wire shading

Global calibration of sensor network

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TOPICS





• ONGOING WORK

• CONCLUSIONS

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CONCLUSIONS

Solar forecasting technology is in the earliest stages of development

Error metrics, time horizons and benchmarks are being developed

Numerous forecasting technologies are under development

Performance validation efforts for individual forecasting technologies are being conducted

There are front runners but currently no clear winners in this technology race

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QUESTIONS?

James M. Bing, PE

[email protected]

Obadiah Bartholomy

[email protected]

Pramod Krishnani

[email protected]

NEO Virtus Engineering, Inc.

mailto:[email protected]









ieee power energy society 2012 presentation - validation of forecasting technologies

Technology

irradiance network

incident irradiance

irradiance sensor

pv array poa

pv array geometery

challenges cpuc

pv system azimuth

ac power wac ac power