The Role of Simulation in Photovoltaics: From Solar Cells To Arrays. Ricardo Borges, Kurt Mueller, and Nelson Braga Synopsys, Inc.
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1 The Role of Simulation in Photovoltaics: From Solar Cells To Arrays Ricardo Borges, Kurt Mueller, and Nelson Braga Synopsys, Inc. 1
2 PV System Challenges Improving PV efficiency Optimizing for design performance and target reliability Reducing the effects of variation on system performance Predicting manufacturing yields Lowering production costs 2
3 Addressing Issues at All Stages Cell Module System Synopsys TCAD tools Synopsys Saber tools Design criteria Cell Level Maximize efficiency Optimize geometric and process parameters Design criteria Module Level Minimize effect of interconnects on performance Minimize impact of cell variation or degradation on module performance Design Criteria System Level Maximize system performance accounting for diurnal solar inclination and tracking of solar path (some systems have 1- or 2-axis tracking of the sun) Maximize system level efficiency delivered to the grid, including inverter system 3
4 What is TCAD? Process Simulation Device Simulation PDE for Pair Diffusion Current in Drift-Diffusion Model Potential distribution in flash memory LDMOS: doping, mesh 1D doping profile simulation PVD (Physical Vapor Deposition) Snapback of a UMOS Inductance Simulation Mechanical stress in intermetal dielectric Photogeneration in CIS AlGaAs VCSEL Full Chip H-Bridge EM Wave 4
5 Why Simulate Solar Cells? Continuous innovation makes cells more complex More process and geometrical variables 3D effects, complex light path, etc It s impractical to design new cells without simulation Too many experiments are needed to investigate design space Risks missing optimum design and market window Source: SERIS Early generation cell (Eff ~ 15-16%) New generation cell (Eff ~ 20%) 5
6 Solar Cell Simulation Flow Input Simulation Output Process Flow Recipe Process Simulation Device Geometry (doping profiles) Device Geometry Optical Data: n & k Optical Simulation External reflection Optical generation Electrical Data: SRH, Auger, BGN, Mobility Device Geometry (lifetime, doping profiles) Electrical Simulation IQE, EQE Dark & Light I-V 6
7 Example: 2D Cell Optimization Select parameters to be investigated Parameterize the TCAD model Run simulations Visualize the influence of each parameter W front S f d lfsf N lfsf N bulk d sub t bulk d bsf S b w back N lbsf d lbsf w tot 7
8 Example: Unit Cell Optimization Results w front w back w tot d sub N bulk d bsf N lbsf d lbsf N lfsf d lfsf t bulk S f S b j sc V oc FF eff Each array of points represents a separate simulated condition Unit cell pitch, base layer thickness, doping, and lifetime, and surface recombination velocity show major influence on cell response Design trade-offs can be investigated quantitatively 8
9 Application: Back-contact Silicon Cells Design problem: optimization of metal finger pitch to achieve good performance with low cost screen printing manufacturing Simulation correctly captures the measured behavior across a range of contact pitch and bulk resistivity Optimization of the structure results in 21.3% efficiency Surface Texturing Antireflection Coating (ARC) n+ FSF Base (n-type) n+ BSF p+ emitter Metal Contacts Passivation Pitch Source: F. Granek et al, Progress in Photovoltaics: Research and Applications, 17, Oct 2008, pp
10 Application: Multi-Junction Solar Cells GaAs/GaInP Dual-Junction Cell Excellent match between Sentaurus simulation and measurements in MJ cells Calibrated model allows researchers to explore more advanced structures: Bragg reflectors, additional junctions, etc Source: Philipps, S.S. et.al.. NUMERICAL SIMULATION AND MODELING OF III-V MULTI-JUNCTION SOLAR CELLS Proceedings of 23rd EUPVSEC,
11 Cells to Systems: Why simulate? Cells alone are physically interesting; Modules and Systems bring the power of the sun to the end user Once cell behavior is understood, need model capable of system-level simulation to: Minimize interconnect losses Evaluate effects of environmental variation: Light intensity and incidence angle Temperature variation Electrical environment Optimize power conversion 11
12 What is Saber? Multi-domain circuit simulation enabling full system Virtual Prototyping Multiple Domains Power Electronics Behavioral Models Control Algorithms Nominal Design Parameter Variation Production Tolerances Statistical Analyses Fault Analyses Optimizing System Performance and Reliability Worst- Case 12
13 Cells to Modules Design problem: active width optimization Given TCAD device design, physical parameters contributing to interconnect resistances can be extracted and a system-level model developed 13
14 Module Optimization Module Optimization: Variation of equivalent R Series & R Shunt From system cell level model, sweeps can be done to determine the effect of different cell widths on module performance Allows for optimization of Maximum Power Point at a module level as a function of luminance and cell width V V I Module (A) 14
15 Module Validation Accurate, physics-based models take TCAD results to system simulation for validating real-world measurements 15
16 Modules to Arrays and Systems Design problem: Thermal Effects on Module/Array performance and Maximum Power Point Analysis of faults on strings within the array Photovoltaic Module Performance Verification at Different Cell Temperatures Measurement of MPPT at Different Temperatures 16
17 System Integration & Optimization Simulation provides integrated test, validation and optimization environment for all aspects of the system: Environment Power Electronics Control System & Algorithms 17
18 Battery Charging System Simulation System highlights: Maximum Power Point Tracking through impedance matching using controlled DC/DC converter Dynamic thermal capable array model 18
19 Unit Cells to Systems Simulation Early validation of novel cell design Development of application-optimized cells, modules and arrays System level virtual prototyping for test & validation before anything physical is built 19
20 Predictable Success 20
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