Solar Power Forecasting: Current State of the … · Solar Power Forecasting: Current State of the...
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Solar Power Forecasting: Current State of the Technology and Market Demand Jeff Lerner, Vaisala Inc. 21 st Mid-C Seminar July 19, 2017 Wenatchee, WA
Transcript of Solar Power Forecasting: Current State of the … · Solar Power Forecasting: Current State of the...
Solar Power Forecasting: Current State ofthe Technology and Market Demand
Jeff Lerner, Vaisala Inc.
21st Mid-C Seminar
July 19, 2017
Wenatchee, WA
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Solar Power Forecasting
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State of the current technologies
Methods for different horizons
Validation
Market Demand
Current status
Growth areas and drivers
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Vaisala : What we do
Founded in 1937 by Professor VilhoVäisälä
World leader in environmentalmeasurement
Headquarters in Helsinki, Finland;offices in Seattle, Boulder and Boston
Acquired 3TIER in December 2013
Digital Services division has extensiveexperience in
Wind and Solar Power Forecasting
Hydro Streamflow forecasting
Solar and Wind Resource Assessment
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Current State of Solar - Global
* IEA 2016 Snapshot of Global Photovoltaic Markets
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Current State of Solar - USA
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Current State of Solar - USA
* Solar Energy Industry Association (seia.org)
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Forecast Horizons DictateTechnologyObservations
Numerical Weather Modeling
Madden-Julian Oscillation El Nino Southern Oscillation
Pacific Decadal Oscillation
Ac
cu
rac
y
Hours WeeksDays Months Years
• Transmissionscheduling
• Assetallocation
• Day aheadmarket
Medium-range
• Assetoptimization
• Futuresmarket
• Hedging
Long-range
• Firm capacity• Volumetric
risk
AssessmentShort-range
• Spinning orfirming
reserves• Load
following• Spot market
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Where Solar Forecasting Differs From Wind
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1. Horizons Dictate The Technologies!
2. Solar Celestial variables obvious butcan’t be understated
a. Solar zenith and azimuth angles
b. Clear-sky irradiance modeling
c. Eclipses!
3. Non-PV storage technologyconsiderations
a. Tower of Power, ParabolicTroughs and Stirling engines arevery different from one another
b. Heat capacity of storage mediumhas to be modeled properly
4. Tracking Technologies – single- anddouble axis trackers
Fixed Tilt PV Plant
Single-axis Tracking PV Plant
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Example technologies used today
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1. 0-1 houra. Sky Imagers
b. Radiometers
c. Statistical, machine learning methods
2. 1-6 hoursa. Satellite advective methods
b. Rapid Refresh NWP models
3. 6-hours to 10 daysa. NWP models
b. Analogue methods
4. 10-30 daysa. Ensemble forecast models
b. Physical and Statistical climate models
c. Climatology
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Forecast Technology Example : 0-1 hour
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Sky Imager Application
Fractional cloud observations
Image is processed andconverted to cloud cover fraction
Cloud cover is forecast out to 30minutes
Depending on cloud height andspeed, forecast may extend toone hour before leaving field ofview
Photo credits to Pekko Tuominen & Lasse Kauppinen
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How does the sky camera work?
Imageprocessing
Cloud coverage is 30.7%
Segmentation results
Cloud coverForecast
30s – 5min
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Forecast Technology Example : 1-6 hour
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NOAA High ResolutionRapid Refresh (HRRR)
• 3-km grid spacing
• Hourly updates
• Cloud resolving
• Assimilating satelliteand RADAR data
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Identify, configure,optimize, and operate aseries (ensemble) ofnumerical weatherprediction systems
Statistical post-processingand power conversionapplication
Forecast Technology Example : 6-hr - 10 days
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Numerical Weather Prediction (NWP):
ECMWF IFS (0.25° grid)– Cloud cover (Total, High, Med, Low), Cloud base height– Downward solar (global, direct)– 3h precipitation, 925mb relative humidity– 100m, 10m wind– 2m temperature, dew-point– Mean sea-level pressure
Celestial:
Solar Position (solar zenith, azimuth angles)
Ineichen-Perez Clear Sky Irradiance Model (GHI and DNI)
Forecast Technology Example : 6-hourto 10 days
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Forecast System Flow Diagram
› Use of multiple NWP models,rather than just single best.
› Reduced emphasis on oneparticular statistical modelingapproach, since severalmachine learning methods are inour toolbox and a hierarchicalsetup improves robustness andperformance.
› Generalized power conversionapproach, since direct powerprediction is often superior topower curve application.
› NWP now integrated withstatistical model inputs, reducingneed for blending with day/weekahead power forecasts.
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Solar Forecast Technology : 10-30 days
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1. Ensemble models
• CFS month aheadforecast
2. Use of ClimatePredictors
ENSO forecastswith Analogues
Essential these forecasts have historical context (i.e., reanalysis)!
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Solar Forecast Verification
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Forecast Tool – Historical QuickView
Regional CAISO SP-15 Solar Forecast
Public
3TIER Blend
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Mid-AtlanticValidation(Site)
Solar Time Series VerificationCAISO NP-15 Validation(Region)
IndividualSolar Plant
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Error = Forecast -Observation
MAPE typically on theorder of 4-8% for allhours when normalizedby plant capacity
MAPE typically around30% normalized byobserved power
Solar Forecast Verification
Metric Public (PIRP)3TIERBlend
Bias+ 132 MW(+3.3%)
- 41 MW (-1.0%)
MAE 262 MW (6.5%)202 MW(5.0%)
Peak > 3500MW
“Clear”
POD 77% 83%
CSI(ETS)
65% (38.5%)73%
(50.9%)
Peak < 3000MW
“Cloudy”
POD 14% 83%
CSI(ETS)
13% (9.9%)67%
(59.9%)
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Forecast Improvement Over Time –Wind Example
Halving the error in a decade! Can we do the same for solar?
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Solar Forecast Market Demand
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Market Demand : Current Status
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1. Site-specific forecasting demand -
a. Renewables penetration high (e.g., HI,NC, NJ, CA and regional BAs)
b. Large plants (e.g., SW USA)
c. Cost of not having a forecast is too high(e.g., CAISO EIM)
2. Regional (ISO-level) forecasting demand
a. Renewables penetration high (e.g., CA)
b. Local congestion (UTC and FTRs) – e.g.,ERCOT
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Market Demand : Growth Areas and Drivers
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1. Site-specific
a. Incentivized by market rules or requirements(CAISO EIM, PJM)
b. Required by PPAs – check the box (SW USA)
c. Grid stability and balancing (HI, FL, NC)
2. Regional / ISO-level
a. High Frequency markets (CAISO EIM, MISO)
b. Localized congestion (e.g., ERCOT, PJM, CAISO)
c. Localized high penetration of RE (e.g., HI, FL, NC)
d. Demand Response / Smart Grid
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Forecasting Market Demand : Future Drivers
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The view from 22,000 miles!
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