<div>In a world of ever-increasing demand for energy while preventing adverse effects of climate</div><div>change, renewable energy has been sought after as a sustainable solution. To this end,</div><div>the last couple of decades have seen an advancement in research and development of solar</div><div>photovoltaic (PV) technology by leaps and bounds. This has led to a steady improvement</div><div>in the cost-effectiveness of solar PV as compared to the traditional sources of energy, e.g.,</div><div>fossil fuels as well as contemporary renewable energy sources such as wind and hydropower.</div><div>To further decrease the levelized cost of energy (LCOE) of solar PV, new materials and</div><div>technologies are being investigated and subsequently deployed as residential, commercial, and</div><div>utility-scale systems. One such innovation is called bifacial PV, which allows collection of</div><div>light from the front as well as rear surfaces of a flat PV panel.</div><div><br></div><div>In this thesis, we present a detailed investigation of bifacial solar PV farms analyzed across</div><div>the globe. We define the problem, explore the challenges, and collaborate with researchers</div><div>from academia and the PV industry to find a novel solution.</div><div><br></div><div>First, we begin by developing a multi-module computational framework to numerically</div><div>model a utility-scale bifacial solar PV farm. This requires integrating optical, electrical,</div><div>thermal, and economic models in order to estimate the energy yield and LCOE of a bifacial</div><div>PV system. The first hurdle is to re-formulate the LCOE so that the economist and the</div><div>technologist can collaborate seamlessly. Thus, we re-parameterize the LCOE expression</div><div>and validate our economic model with economists at the National Renewable Energy Lab</div><div>(NREL).</div><div><br></div><div>Second, we extend the existing optical and electrical models created for stand-alone</div><div>bifacial PV panels to models that can simulate a large-scale bifacial solar PV farm. This</div><div>brings the challenge of mathematically modeling solar farms and light collection on the rows</div><div>of PV panels elevated from the ground by taking into account the mutual shading between</div><div>the rows, reflections from the ground, and elevation-dependent light absorption on the rear</div><div>surface of the PV panels from several neighboring rows. Next, we integrate temperaturedependent</div><div>efficiency models to take into account the effects of location-dependent ambient</div><div>temperature, wind speed, and technology-varying temperature coefficients of the solar PV</div><div>system in consideration.</div><div><br></div><div>Third, we complete the comprehensive modeling of bifacial solar PV farms by including</div><div>two types of single-axis tracking algorithms viz. sun-tracking and power tracking. Using these</div><div>algorithms, we explore the best tracking orientation of solar farms i.e., East-West tracking</div><div>vs. North-South tracking for locations around the world. We further find the best land type</div><div>suitable for installation of these E/W or N/S tracking bifacial solar PV farms.</div><div><br></div><div>Fourth, we reduce the computation time of numerical modeling by utilizing the advantages</div><div>of machine learning algorithms. We train neural networks using data from the alreadybuilt</div><div>models to emulate the numerical modeling of a solar farm. Amazingly, we find the</div><div>computation time reduces by orders of magnitude while accurately estimating the energy</div><div>yield and LCOE of PV farms.</div><div><br></div><div>Fifth, we derive, compare, and experimentally validate the thermodynamic efficiency</div><div>limits of photovoltaic-to-electrochemical energy conversion for the purpose of storing solar</div><div>energy for future needs.</div><div><br></div><div>Finally, we present some new ideas and guidelines for future extensions of this thesis as</div><div>well as new challenges and problems that need further exploration.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17131502 |
| Date | 20 December 2021 |
| Creators | Muhammed-Tahir Patel (11798318) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/SYSTEM-LEVEL_PERFORMANCE_AND_RELIABILITY_OF_SOLAR_PHOTOVOLTAIC_FARMS_LOOKING_AHEAD_AND_BACK/17131502 |
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