Very
Large Photovoltaic Solar Power Plants (VLPVPPs) are a major revolutionary step
up not only for economies of scale, but also for %100 renewable power Global
Grid. Their designs and investments should be performed in an environmentally
friendly, fair, open to very large to small private investors, transparent and reducing
relative income inequality approaches. Their investments will easily be
possible with new investment models (%0 interest load, %100 private equity, open
investment for ordinary people, project developers, private companies etc. with
a constraint-based shareholder structuring). These revolutionary investment models
will play an important and game changing role. VLPVPPs’ early engineering and
investment analysis can be performed on many software. Therefore, validation
and verification efforts of those software in advance on the operational PVPPs
are essential. This research study aims to present a validation and
verification accomplishment of the Solar Star Projects (597 MWAC,
747,3 MWDC) (Solar Star I: 318 MWAC, 397,8 MWDC
& Solar Star II: 279 MWAC, 349,5 MWDC) in Antelope
Valley near Rosamond, Kern and Los Angeles counties, California, United States
with the PVWatts Version 5 model of the National Renewable Energy Laboratory
(NREL) System Advisor Model (SAM) Version 2017.9.5. The location and resource,
system design data and information of the Solar Star Projects (I & II) are presented
based on open source information and personal communications. The Solar Star
Projects SAM software models' are simulated on a personal computer (PC)
(Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHZ, 6,00 GB RAM) with the
internet connection. The results of eight simple simulations, one parametric simulation
and one stochastic simulation are compared with the actual generation data by
help of some statistical performance measures (e.g. annual model/actual: 100,0%,
annual model/actual: 100,1%, absolute maximum forecast error 39.276 MWh, mean
absolute error 11.554 MWh, geometric mean absolute error 8.924 MWh, mean square
error 2.662.330.229 MWh, root mean square error 51.597 MWh).
Primary Language | English |
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Journal Section | Research Article |
Authors | |
Publication Date | April 5, 2018 |
Submission Date | March 9, 2018 |
Acceptance Date | March 26, 2018 |
Published in Issue | Year 2018 |