SYSTEM-LEVEL PERFORMANCE AND RELIABILITY OF SOLAR PHOTOVOLTAIC FARMS: LOOKING AHEAD AND BACK
thesisposted on 20.12.2021, 14:17 by Muhammed-Tahir PatelMuhammed-Tahir Patel
In a world of ever-increasing demand for energy while preventing adverse effects of climate
change, renewable energy has been sought after as a sustainable solution. To this end,
the last couple of decades have seen an advancement in research and development of solar
photovoltaic (PV) technology by leaps and bounds. This has led to a steady improvement
in the cost-effectiveness of solar PV as compared to the traditional sources of energy, e.g.,
fossil fuels as well as contemporary renewable energy sources such as wind and hydropower.
To further decrease the levelized cost of energy (LCOE) of solar PV, new materials and
technologies are being investigated and subsequently deployed as residential, commercial, and
utility-scale systems. One such innovation is called bifacial PV, which allows collection of
light from the front as well as rear surfaces of a flat PV panel.
In this thesis, we present a detailed investigation of bifacial solar PV farms analyzed across
the globe. We define the problem, explore the challenges, and collaborate with researchers
from academia and the PV industry to find a novel solution.
First, we begin by developing a multi-module computational framework to numerically
model a utility-scale bifacial solar PV farm. This requires integrating optical, electrical,
thermal, and economic models in order to estimate the energy yield and LCOE of a bifacial
PV system. The first hurdle is to re-formulate the LCOE so that the economist and the
technologist can collaborate seamlessly. Thus, we re-parameterize the LCOE expression
and validate our economic model with economists at the National Renewable Energy Lab
Second, we extend the existing optical and electrical models created for stand-alone
bifacial PV panels to models that can simulate a large-scale bifacial solar PV farm. This
brings the challenge of mathematically modeling solar farms and light collection on the rows
of PV panels elevated from the ground by taking into account the mutual shading between
the rows, reflections from the ground, and elevation-dependent light absorption on the rear
surface of the PV panels from several neighboring rows. Next, we integrate temperaturedependent
efficiency models to take into account the effects of location-dependent ambient
temperature, wind speed, and technology-varying temperature coefficients of the solar PV
system in consideration.
Third, we complete the comprehensive modeling of bifacial solar PV farms by including
two types of single-axis tracking algorithms viz. sun-tracking and power tracking. Using these
algorithms, we explore the best tracking orientation of solar farms i.e., East-West tracking
vs. North-South tracking for locations around the world. We further find the best land type
suitable for installation of these E/W or N/S tracking bifacial solar PV farms.
Fourth, we reduce the computation time of numerical modeling by utilizing the advantages
of machine learning algorithms. We train neural networks using data from the alreadybuilt
models to emulate the numerical modeling of a solar farm. Amazingly, we find the
computation time reduces by orders of magnitude while accurately estimating the energy
yield and LCOE of PV farms.
Fifth, we derive, compare, and experimentally validate the thermodynamic efficiency
limits of photovoltaic-to-electrochemical energy conversion for the purpose of storing solar
energy for future needs.
Finally, we present some new ideas and guidelines for future extensions of this thesis as
well as new challenges and problems that need further exploration.