To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. The low cost and ease of use of the SVD will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.

References

References
1.
Lave
,
M.
,
Reno
,
M. J.
, and
Broderick
,
R. J.
,
2015
, “
Characterizing Local High-Frequency Solar Variability and Its Impact to Distribution Studies
,”
Sol. Energy
,
118
, pp.
327
337
.
2.
Srikrishnan
,
V.
,
2015
, “
Estimation of Direct Normal Irradiance With Multi-Pyranometer Arrays and Artificial Neural Networks
,” Master's thesis, The Pennsylvania State University, State College, PA.
3.
Mancilla-David
,
F.
,
Riganti-Fulginei
,
F.
,
Laudani
,
A.
, and
Salvini
,
A.
,
2014
, “
A Neural Network-Based Low-Cost Solar Irradiance Sensor
,”
IEEE Trans. Instrum. Meas.
,
63
(
3
), pp.
583
591
.
4.
Cruz-Colon
,
J.
,
Martinez-Mitjans
,
L.
, and
Ortiz-Rivera
,
E.
,
2012
, “
Design of a Low Cost Irradiance Meter Using a Photovoltaic Panel
,”
38th IEEE Photovoltaic Specialists Conference
(
PVSC
), Austin, TX, June 3–8, pp.
002911
002915
.
5.
Parry
,
D.
,
2013
, “
NRL Develops Low Cost, High Efficiency Solar Sensor
,”
U.S. Naval Research Laboratory
, Washington, DC.
6.
Sengupta
,
M.
, and
Andreas
,
A.
,
2010
, “
Oahu Solar Measurement Grid (1-Year Archive): 1-Second Solar Irradiance; Oahu, Hawaii (Data)
,” National Renewable Energy Laboratory, Golden, CO, Technical Report No. DA-5500-56506.
7.
Dangelmaier
,
L.
,
2012
, “
HECO Companies Experience With Distributed PV
,” PJM/EPRI/NREL Inverter Based Generation Interconnection Workshop Proceedings.
8.
Sacramento Municipal Utility District
,
2014
, “
High Penetration Photovoltaic Initiative
,”
Sacramento Municipal Utility District
, Sacramento, CA.
9.
EPRI
,
2016
, “
Distributed PV Monitoring and Feeder Analysis
,”
Electric Power Research Institute
,
Palo Alto, CA
.
10.
Kipp and Zonen
,
2015
, “
CMP21 Instruction Manual
,”
Kipp and Zonen
, Delft, The Netherlands.
11.
The Eppley Laboratory
,
2013
, “
Standard Precision Pyranometer, Model SPP
,”
The Eppley Laboratory
, Newport, RI.
12.
Reno
,
M. J.
, and
Hansen
,
C. W.
,
2016
, “
Identification of Periods of Clear Sky Irradiance in Time Series of {GHI} Measurements
,”
Renewable Energy
,
90
, pp.
520
531
.
13.
Ineichen
,
P.
, and
Perez
,
R.
,
2002
, “
A New Airmass Independent Formulation for the Linke Turbidity Coefficient
,”
Sol. Energy
,
73
(
3
), pp.
151
157
.
14.
SNL
,
2014
, “
PVLib Toolbox: Ineichen Clear Sky Model
,”
Sandia National Laboratory
, Albuquerque, NM.
15.
Lave
,
M.
, and
Kleissl
,
J.
,
2013
, “
Cloud Speed Impact on Solar Variability Scaling: Application to the Wavelet Variability Model
,”
Sol. Energy
,
91
, pp.
11
21
.
16.
Lave
,
M.
,
2012
, “
Comparison of Errors in Solar Power Plant Variability Simulation Methods
,” Sandia National Laboratories, Albuquerque, NM, Technical Report No. SAND2015-0156.
17.
Gevorgian
,
V.
, and
Booth
,
S.
,
2013
, “
Review of PREPA Technical Requirements for Interconnecting Wind and Solar Generation
,” National Renewable Energy Laboratory, National Renewable Energy Laboratory, Golden, CO, Technical Report No.
NREL
/TP-5D00-57089.
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