SYSTEM-LEVEL PERFORMANCE AND RELIABILITY OF SOLAR PHOTOVOLTAIC FARMS: LOOKING AHEAD AND BACK
Creators: name: Patel, Muhammed-Tahir
Publisher: name: Purdue University Graduate School
Year: 2021
Subjects: Electrical engineering not elsewhere classified
Other physical sciences not elsewhere classified

description: In a world of ever-increasing demand for energy while preventing adverse effects of climatechange, 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 solarphotovoltaic (PV) technology by leaps and bounds. This has led to a steady improvementin 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 andtechnologies are being investigated and subsequently deployed as residential, commercial, andutility-scale systems. One such innovation is called bifacial PV, which allows collection oflight from the front as well as rear surfaces of a flat PV panel.<br>In this thesis, we present a detailed investigation of bifacial solar PV farms analyzed acrossthe globe. We define the problem, explore the challenges, and collaborate with researchersfrom academia and the PV industry to find a novel solution.<br>First, we begin by developing a multi-module computational framework to numericallymodel 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 bifacialPV system. The first hurdle is to re-formulate the LCOE so that the economist and thetechnologist can collaborate seamlessly. Thus, we re-parameterize the LCOE expressionand validate our economic model with economists at the National Renewable Energy Lab(NREL).<br>Second, we extend the existing optical and electrical models created for stand-alonebifacial PV panels to models that can simulate a large-scale bifacial solar PV farm. Thisbrings the challenge of mathematically modeling solar farms and light collection on the rowsof PV panels elevated from the ground by taking into account the mutual shading betweenthe rows, reflections from the ground, and elevation-dependent light absorption on the rearsurface of the PV panels from several neighboring rows. Next, we integrate temperaturedependentefficiency models to take into account the effects of location-dependent ambienttemperature, wind speed, and technology-varying temperature coefficients of the solar PVsystem in consideration.<br>Third, we complete the comprehensive modeling of bifacial solar PV farms by includingtwo types of single-axis tracking algorithms viz. sun-tracking and power tracking. Using thesealgorithms, we explore the best tracking orientation of solar farms i.e., East-West trackingvs. North-South tracking for locations around the world. We further find the best land typesuitable for installation of these E/W or N/S tracking bifacial solar PV farms.<br>Fourth, we reduce the computation time of numerical modeling by utilizing the advantagesof machine learning algorithms. We train neural networks using data from the alreadybuiltmodels to emulate the numerical modeling of a solar farm. Amazingly, we find thecomputation time reduces by orders of magnitude while accurately estimating the energyyield and LCOE of PV farms.<br>Fifth, we derive, compare, and experimentally validate the thermodynamic efficiencylimits of photovoltaic-to-electrochemical energy conversion for the purpose of storing solarenergy for future needs.<br>Finally, we present some new ideas and guidelines for future extensions of this thesis aswell as new challenges and problems that need further exploration.