Solar Variability and Forecasting Using Remote Sensing
Shahrouz Alimohammadi
University of California San Diego, Center For Energy Research
salimoha@ucsd.edu, June 4th, Script Institute of Oceanography
Point Irradiance vs. Plant Power
• PV plant power output is the relevant quantity for grid impact studies, but point irradiance
measurements are much more common.
• PV plant power output is the relevant quantity for grid impact studies, but point irradiance
measurements are much more common.
Sola Irradiance (GHI) [W m-2]
Meteorological conditions

The penetration of solar irradiance into
grids is one of the most important research
subjects for the Department Of Energy
(DOE).

One of the primary obstacles to overcome
in solar power as a reliable source is how
meteorological conditions affect the
amount of energy available to the PV
module.

The key elements in solar variability is
focusing on meteorological conditions
such as properties of clouds, the
movement of cloud, and the position of
the Sun in relation to the clouds.
point
sensor
Time (4 min)
September, 2012
Sun
Mon
Tues
Thurs
Wed
Sat
Fri
1
1200
800
400
0
1200
Solar PV power is variable.
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
800
400
0
1200
GHI [W m-2]
550 MW
800
400
0
1200
800
400
0
1200
800
400
0
1200
30
800
400
0
Traditional generation sources have very little variability.
Traditional Generation
(coal, natural gas, nuclear)
Caused by
clouds
PV
Irradiance [W m -2]
GHI
GHIskc
600
500
400
300
200
100
0
Deviation [W m-2]
Caused by sun
movement
700
400
200
0
-200
-400
08:00
12:00
16:00
GHI-GHIskc
08:00
12:00
16:00
Space and Time Scales of Resolution
Satellite
Tool:
1 hour
Time
1 day
“Clouds exist”
± 2 miles, ± 10%
Ground Station
“Clouds there”
± 100 ft, ± 3%
10 min
FPL
1 min
10 sec
1 sec
Distance
Point
Yards / meters
km / mile
10 miles
100 miles
Solar Irradiance Data For Solar Variability
Modeling
Solar Irradiance Data
Ground Station
Data
Satellite
Data
L. Nonnenmacher
et al. 2014
SUNY model
R. Perez 2010
NREL Hawaii
State of California Solar Irradiance Map
SUNY model: Solar Anywhere is the company that sells satellite solar
irradiance data using GOES satellites. R. Perez et al.2010
Cloud Segmentation and Irradiance Model
Linear Model
Satellite to
Irradiance
Model
L. Nonnenmacher et al. 2014b
Forecast the
Cloud Motion
Identification of cloud
region that is
propelled to the
region of interest
Cloud Classification
Clear (no cloud)
Thin cloud
Satellite Irradiance (GHI) map
No data
Thick cloud
Pair Sensor Correlation
Data from sensors
Correlation of ramp time series as a function of station
separation distance for pairs of sites aligned along (blue) and
across (red) the trade wind direction. L. Hinkelman 2013
Correlation is a function of Cloud Speed
20
• Correlations between any
two sites can be modeled
as: [M. Lave et al. 2013]
A value []
𝜌 𝑑𝑖,𝑗 , 𝑡 =
15
1 𝑑𝑖,𝑗
exp(−
)
𝐴 𝑡
10
5
0
𝐴 value dependence on
cloud size and speed
5
25
𝐴 value linear dependence
on cloud speed
M. Lave et al. 2013
NAM: North America Model
10
15
20
cloud speed [m/s]
To test the dependence of the 𝐴 value on cloud speed and size,
created a simple cloud simulator.
distance [m]
 Various cloud fields were created, each with a different cloud size
 Cloud fields were advected at different velocities
 A virtual sensor network was used to determine A values for each cloud field/cloud
size combination
Direction of Cloud Motion
0
500
1000
1000
2000
3000
4000
5000
distance [m]
6000
7000
8000
9000
10000
Along-wind and Cross-wind
Elements of Solar Variability
Key elements of solar variability modeling are
• d, between the two locations,
• ∆𝑡, time scale t between two measurements,
• V, cloud speed and direction.
R. Perez et al. 2015
Frozen Cloud Estimation
Naive model: 𝑘 𝑇 𝑥, 𝑦, 𝑡 = 𝑘 𝑇 𝑥𝑜 + 𝑣𝑥 𝑑𝑡, 𝑦𝑜 + 𝑣𝑦 𝑑𝑡, 𝑡 − 𝑑𝑡
Frozen Cloud Estimation
𝑘2𝑡 = 𝑘1𝑡−Δ𝑡 ,
∆𝑥
∆𝑡 =
𝑣
This is a Naive estimation because
it doesn’t consider the changes in
cloud shape and velocity.
Naive Model
Ordinary Kriging Formulation
Noise + Naive
Noise
Kriging Model
Summery
• Different Solar Irradiance Sensors (satellite and ground station)
• Satellite irradiance model (SUNY model and Nonnenmacher model)
• Frozen Cloud Estimation (Naive Model)
• Gaussian Process Model (Naive Model + Noise Model)
References
• [1] Hinkelman L. Differences between along-wind and cross-wind solar irradiance variability on small spatial scales, Solar Energy
Journal (2013), pp. 192 - 203.
•
[2] Chaojun, G. and Yang, D. and Jirutitijaroen, P. and Walsh, W.M. and Reindl, T. Spatial load forecasting with communication
failure using time-forward kriging,IEEE Transactions on Power Systems (2014).
•
[3] Chi Wai Chow, Juan Luis Bosch, and Jan Kleissl, Evaluation of hoursahead solar forecasting using satellite imagery and Numerical
Weather Prediction, technical report , UC San Diego (2014)
•
[4] Booker, A. J., Dennis Jr, J. E., Frank, P. D., Serafini, D. B., Torczon, V., Trosset, M. W. A rigorous framework for optimization of
expensive functions by surrogates. Structural optimization (1999), 17(1), 1-13.
•
[5] Lave, M. and Kleissl, J. (2013). Cloud speed impact on solar variability scaling Application to the wavelet variability model. Solar
Energy 91(0): pp. 11-21. [5] Cressie, N. A. Wikle, C. Statistics for Spatio-Temporal Data. United States: Wiley. (2011).
•
[6] Jones, D. R. A taxonomy of global optimization methods based on response surfaces. Journal of global optimization, (2001).
21(4), 345-383.
•
[7] Thomas E. Hoff, Richard Perez, Quantifying PV power Output Variability, Solar Energy, Volume 84, Issue 10, October 2010, Pages
1782- 1793, ISSN 0038-092X, http://dx.doi.org/10.1016/j.solener.2010.07.003.
• [8] Richard Perez ,Tom Hoff, Mathieu David, Sergey Kivalov, Philippe Lauret, Marc Perez, Jan Kleissl, Spatial and temporal variability
of solar energy. Solar Energy (2015) 12
• [9] Lukas Nonnenmacher, Amanpreet Kaur, Carlos F.M. Coimbra, Verifi- cation of the SUNY direct normal irradiance model with
ground measurements. Solar Energy, Volume 99, Issue null, Pages 246-258
• [10] Lukas Nonnenmacher, Carlos F.M. Coimbra, Streamline-based method for intra-day solar forecasting through remote sensing,
Solar Energy, Volume 108, Issue null, Pages 447-459
Questions ?