Minimizing the Optical Cell-to-module Losses for MWT-modules

Haedrich, Ingrid; Wiese, Martin; Thaidigsman, Benjamin; Eberlein, D.; Clement, Florian; Eitner, Ulrich; Preu, R.; Wirth, Harry

doi:10.1016/j.egypro.2013.07.289

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…There was an option in the calculator tool to select and design the geometry of the solar cells, and set their resistivity, along with the cost of the metal volume utilized in the grid and the cell dimensions. The current tool subsequently determined the surface area, volume, series resistance, shading, and cost of the metal employed to fabricate grids on the solar cells [28][29][30]. This modeling of the solar cell was utilized to generate the new material file for the CTM simulation.…”

Section: Modelling Of the Solar Cellmentioning

confidence: 99%

Cell-to-Module Simulation Analysis for Optimizing the Efficiency and Power of the Photovoltaic Module

et al. 2022

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A 60-cell photovoltaic (PV) module was analyzed by optimizing the interconnection parameters of the solar cells to enhance the efficiency and increase the power of the PV module setup. The cell-to-module (CTM) losses and gains varied substantially during the various simulation iterations. Optimization was performed to inspect and augment the gain and loss parameters for the 60-cell PV module. The power and efficiency of the module were improved by refining several parameters, such as number of busbars, size of the contact pads, interconnected ribbon width, thickness of the core, and distance between the solar cells and strings, to obtain the maximum efficiency of 21.09%; the CTM efficiency achieved was 94.19% for the proposed strategy related to the common interconnection setup of the ribbon-based system. The CTM efficiency was improved by optimizing the geometrical, optical, and electrical parameters precisely, the power enhancement was up to 325.3 W, and a CTM power of 99.1% was achieved from a standard PV module with rectangular ribbon interconnections.

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Section: Modelling Of the Solar Cellmentioning

confidence: 99%

Cell-to-Module Simulation Analysis for Optimizing the Efficiency and Power of the Photovoltaic Module

et al. 2022

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“…8,9) Various authors have discussed optical losses associated with the encapsulation=modularization. 5,10,11) The optical losses are mainly categorized into two components (Fig. 1): hemispherical reflection loss which occurs at various interfaces [air-glass (1), glass-encapsulant (2), and encapsulantcell (3)], as well as parasitic absorptance loss due to glass (4) and front encapsulant (5).…”

Section: Introductionmentioning

confidence: 99%

Cell-to-module optical loss/gain analysis for various photovoltaic module materials through systematic characterization

Saw

Khoo

Singh

et al. 2017

Jpn. J. Appl. Phys.

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Reducing levelized cost of electricity (LCOE) is important for solar photovoltaics to compete against other energy sources. Thus, the focus should not only be on improving the solar cell efficiency, but also on continuously reducing the losses (or achieving gain) in the cell-to-module process. This can be achieved by choosing the appropriate module material and design. This paper presents a detailed and systematic characterization of various photovoltaic (PV) module materials (encapsulants, tabbing ribbons, and backsheets) and an evaluation of their impact on the output power of silicon wafer-based PV modules. Various characterization tools/techniques, such as UV–vis (reflectance) measurement, external quantum efficiency (EQE) measurement and EQE line-scan are used. Based on the characterization results, we use module materials with the best-evaluated optical performance to build “optimized modules”. Compared to the standard mini-module, an optical gain of more than 5% is achievable for the “optimized module” with selected module materials.

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“…31,65,66 There is also promising work to reduce cell-to-module losses. [67][68][69] The price constraint on installed capacity depends strongly on the demand curve for PV. The demand curve in turn depends on a multitude of factors unrelated to PV module technology.8 To capture the uncertainty in these factors over a period of decades, we shift the demand curve to simulate increased and decreased demand at a given PV module price (see ESI † for details) and recalculate installations over time for each of our technology scenarios.…”

Section: View Article Onlinementioning

confidence: 99%

Economically sustainable scaling of photovoltaics to meet climate targets

Needleman

Poindexter

Kurchin

et al. 2016

Energy Environ. Sci.

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aTo meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today's technology falls short in two ways: profits are too small relative to upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost. Finally, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price. Broader contextTo reduce CO 2 emissions enough over the next fifteen years and avoid the worst effects of climate change will require dramatic increases in the deployment of renewable energy, photovoltaics (PV) in particular. Climate action plans call for 2-10 terawatts (TW) of PV by 2030. Current manufacturing capacity could supply enough for 1 TW of cumulative installations at the end of this period, implying that growth in manufacturing capacity is necessary. Industry roadmaps project up to 2.6 TW but largely fail to assess whether these targets are economically feasible with today's PV module technology. Addressing the question of what technological innovations, if any, would enable rapid manufacturing scale-up requires a conceptual advance in modeling methodology. We address this challenge by coupling three industry-validated models: a bottom-up cost model, an economically sustainable growth-rate calculator, and a constraining demand curve. This approach enables us to determine the sensitivity of PV industry growth to specific technological and economic variables, considering both their effect on the ratio of up-front factory costs to revenue and demand as a function of PV module price. Shifting the demand curve enables us to consider the effects of different policy decisions, like a carbon tax or deployment subsidies.

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Minimizing the Optical Cell-to-module Losses for MWT-modules

Cited by 7 publications

References 5 publications

Cell-to-Module Simulation Analysis for Optimizing the Efficiency and Power of the Photovoltaic Module

Cell-to-Module Simulation Analysis for Optimizing the Efficiency and Power of the Photovoltaic Module

Cell-to-module optical loss/gain analysis for various photovoltaic module materials through systematic characterization

Economically sustainable scaling of photovoltaics to meet climate targets

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