Solar PV is widely considered as a "green" technology. This paper, however, investigates the environmental impact of the production of solar modules made from thin-film silicon. We focus on novel applications of nano-crystalline Silicon materials (nc-Si) into current amorphous Silicon (a-Si) devices. Two nc-Si specific details concerning the environmental performance can be identified, when we want to compare to a-Si modules. First, in how far the extra (and thicker) silicon layer (s) affects upstream material requirements and energy use. Second, in how far depositing an extra silicon layer may increase emissions of greenhouse gases as additional emissions of Fluor gases (F-gases) are associated to this step. The much larger global warming potential of F-gases (17 200-22 800 times that of CO2) may lead to higher environmental burdens. To date, no study has yet analyzed the effect of F-gas usage on the environmental profile of thin-film silicon solar modules. We performed a life-cycle assessment (LCA) to investigate the current environmental usefulness of pursuing this novel micromorph concept. The switch to the new micromorph technology will result in a 60-85% increase in greenhouse gas emissions (per generated kWh solar electricity) in case of NF3 based clean processing, and 15-100% when SF6 is used. We conclude that F-gas usage has a substantial environmental impact on both module types, in particular the micromorph one. Also, micromorph module efficiencies need to be improved from the current 8-9% (stabilized efficiency) toward 12-16% (stab. eff.) in order to compensate for the increased environmental impacts. Copyright (C) 2010 John Wiley & Sons, Ltd.

The direct and indirect emissions associated with photovoltaic (PV) electricity generation are evaluated, focussing on greenhouse gas (GHG) emissions related to crystalline silicon (c-Si) solar module production. Electricity supply technologies used in the entire PV production chain are found to be most influential. Emissions associated with only the electricity-input in the production of PV vary as much as 0-200 g CO(2)-eq per kWh electricity generated by PV. This wide range results because of specific supply technologies one may assume to provide the electricity-input in PV production, i.e., whether coal-, gas-, wind-, or PV-power facilities in the "background" provide the electricity supply for powering the entire PV production chain. The heat input in the entire PV production chain, for which mainly the combustion of natural gas is assumed, adds another similar to 16 CO(2)-eq/kWh. The GHG emissions directly attributed to c-Si PV technology alone constitute only similar to 1-2 g CO(2)-eq/kWh. The difference in scale indicates the relevance of reporting "indirect" emissions due to energy input in PV production separately from "direct" emissions particular to PV technology. In this article, we also demonstrate the utilization of "direct" and "indirect" shares of emissions for the calculation of GHG emissions in simplified world electricity-and PV-market development scenarios. Results underscore very large GHG mitigation realized by solar PV toward increasingly significant PV market shares. Copyright (C) 2011 John Wiley & Sons, Ltd.

In a large number of energy models, the use of learning curves for estimating technological improvements has become popular. This is based on the assumption that technological development can be monitored by following cost development as a function of market size. However, recent data show that in some stages of photovoltaic technology (PV) production, the market price of PV modules stabilizes even though the cumulative capacity increases. This implies that no technological improvement takes place in these periods: the cost predicted by the learning curve in the PV study is lower than the market one. We propose that this bias results from ignoring the effects of input prices and scale effects, and that incorporating the input prices and scale effects into the learning curve theory is an important issue in making cost predictions more reliable. In this paper, a methodology is described to incorporate the scale and input-prices effect as the additional variables into the one factor learning curve, which leads to the definition of the multi-factor learning curve. This multi-factor learning curve is not only derived from economic theories, but also supported by an empirical study. The results clearly show that input prices and scale effects are to be included, and that, although market prices are stabilizing, learning is still taking place.

Progress ratios (PRs) are widely used in forecasting development of many technologies; they are derived from historical data represented in experience curves. Fitting the double logarithmic graphs is easily done with spreadsheet software like Microsoft Excel, by adding a trend line to the graph. However, it is unknown to many that these data are transformed to linear data before a fit is performed. This leads to erroneous results or a transformation bias in the PR, as we demonstrate using the experience curve for photovoltaic technology: logarithmic transformation leads to overestimates of progress ratios and underestimates of goodness of fit. Therefore, other graphing and analysis software is recommended.

Measured and modelled JV characteristics of crystalline silicon cells below one sun intensity have been investigated. First, the JV characteristics were measured between 3 and 1000W/m2 at 6 light levels for 41 industrially produced mono- and multi-crystalline cells from 8 manufacturers, and at 29 intensity levels for a single multi-crystalline silicon between 0.01 and 1000W/m2. Based on this experimental data, the accuracy of the following four modelling approaches was evaluated: (1) empirical fill factor expressions, (2) a purely empirical function, (3) the one-diode model and (4) the two-diode model. Results show that the fill factor expressions and the empirical function fail at low light intensities, but a new empirical equation that gives accurate fits could be derived. The accuracy of both diode models are very high. However, the accuracy depends considerably on the used diode model parameter sets. While comparing different methods to determine diode model parameter sets, the two-diode model is found to be preferred in principle: particularly its capability in accurately modelling VOC and efficiency with one and the same parameter set makes the two-diode model superior. The simulated energy yields of the 41 commercial cells as a function of irradiance intensity suggest unbiased shunt resistances larger than about 10kΩcm2 may help to avoid low energy yields of cells used under predominantly low light intensities. Such cells with diode currents not larger than about 10−9A/cm2 are excellent candidates for Product Integrated PV (PIPV) appliances.

The use of polymer materials for photovoltaic applications is expected to have several advantages over current crystalline silicon technology. In this paper, we perform an environmental and economic assessment of polymer-based thin film modules with a glass substrate and modules with a flexible substrate and we compare our results with literature data for multicrystalline (mc-) silicon photovoltaics and other types of PV The functional unit of this study is '25 years of electricity production by P V systems with a power of I watt-peak (W-p)'. Because the lifetime of polymer photovoltaics is at present much lower than of mc-silicon photovoltaics, we first compared the PV cells per watt-peak and next determined the minimum required lifetime of polymer PV to arrive at the same environmental impacts as mc-silicon PV We found that per watt-peak of output power, the environmental impacts compared to mc-silicon are 20-60% lower for polymer PV systems with glass substrate and 80-95% lower for polymer PV with PET as substrate (flexible modules). Also in comparison with thin film CuInSe and thin film silicon, the impacts of polymer modules, per watt-peak, appeared to be lower. The costs per watt-peak of polymer PV modules with glass substrate are approximately 20% higher compared to mc-silicon photovoltaics. However, taking into account uncertainties, this might be an overestimation. For flexible modules, no cost data were available. If the efficiency and lifetime of polymer PV modules increases, both glass-based and flexible polymer PV could become an environment friendly and cheap alternative to mc-silicon PV Copyright (C) 2009 John Wiley & Sons, Ltd.

Based on the assumption that following cost development as a function of market size can monitor technological development, the use of learning curves for estimating technological improvements has become popular in a large number of energy models. However, recent data show that in some stages of photovoltaic technology (PV) production, the market price of PV modules stabilizes even though the cumulative capacity increases. This would imply that no technological learning takes place in these periods: the cost predicted by the learning curve in the PV study is lower than the market one. We argue in this contribution that this bias results from ignoring the effects of input prices and scale effects, and that incorporating the input prices and scale effects into the learning curve theory is an important issue in making cost predictions more reliable. We describe a methodology to incorporate the scale and input-prices effect as the additional variables into the one-factor learning curve, which leads to the definition of a the multi-factor learning curve. This curve is not only derived from economic theories, but also supported by an empirical study for PV technology. The results clearly show that input prices and scale effects are to be included, and that, although market prices are stabilizing, learning is still taking place.

A solar powered wireless computer mouse (SPM) was chosen to serve as a case study for the evaluation and optimization of industrial design processes of photovoltaic (PV) powered consumer systems. As the design process requires expert knowledge in various technical fields, we assessed and compared the following: appropriate selection of integrated PV type, battery capacity and type, possible electronic circuitries for PV-battery coupling, and material properties concerning mechanical incorporation of PV into the encasing. Besides technical requirements, ergonomic aspects and design aesthetics with respect to good “sun-harvesting” properties influenced the design process. This is particularly important as simulations show users can positively influence energy balances by “sun-bathing” the PV mouse. A total of 15 SPM prototypes were manufactured and tested by actual users. Although user satisfaction proved the SPM concept to be feasible, future research still needs to address user acceptance related to product dimensions and user willingness to pro-actively “sun-bath” PV powered products in greater detail.

A sustainable recycling of photovoltaic (PV) thin film modules gains in importance due to the considerable growing of the PV market and the increasing scarcity of the resources for semiconductor materials. The paper presents the development of two strategies for thin film PV recycling based on (wet) mechanical processing for broken modules, and combined thermal and mechanical methods for end-of-life modules. The feasibility of the processing steps was demonstrated in laboratory scale as well as in semi-technical scale using the example of CdTe and CIS modules. Pre-concentrated valuables In and Te from wet mechanical processing can be purified to the appropriate grade for the production of new modules.An advantage of the wet mechanical processing in comparison to the conventional procedure might be the usage of no or a small amount of chemicals during the several steps.Some measures are necessary in order to increase the efficiency of the wet mechanical processing regarding the improvement of the valuable yield and the related enrichment of the semiconductor material.The investigation of the environmental impacts of both recycling strategies indicates that the strategy, which includes wet mechanical separation, has clear advantages in comparison to the thermal treatment or disposal on landfills. (C) 2009 Elsevier B.V. All rights reserved.

We give an overview of historical developments with respect to the price and the Energy Pay-Back Time of crystalline silicon photovoltaic modules. We investigate the drivers behind both developments and observe that there is a large overlap between them. Reduction of silicon consumption, improved cell efficiency and the production technology for solar grade silicon have been identified as major drivers for both cost and impact reductions in the past. Also we look into future prospects for reduction of environmental impacts. It is estimated that developments underway to reduce costs will also result in a reduction of the Energy Pay-Back Time of a PV installation (in South-Europe) from 1.5-2.0 year presently to well below 1 year.

Learning curves are extensively used in policy and scenario studies. Progress ratios (PRs) are derived from historical data and are used for forecasting cost development of many technologies, including photovoltaics (PV). Forecasts are highly sensitive to uncertainties in the PR. A PR usually is determined together with the coefficient of determination R-2, which should approach unity for a good fit of the available data. Although the R-2 is instructive, we recommend using the error in the PR determined from the fit because it is a direct measure of the range in PR values that is recommended to be used in sensitivity analyses within scenario studies. We present a simple equation to calculate the error in PR from the fit parameters. In the case of crystalline PV module technology development we find a PR = 0-794 +/- 0.003 by fitting price data of the period 1976-2006. A moving average approach with a 10-year time window shows that PR varies from 0.818 +/- 0.017 up to a starting year of 1987, and is reduced considerably to a minimum value of 0.704 +/- 0.014 for the starting year 1991. For the most recent starting year 1997, the average PR is considerably higher at 0.884 +/- 0.022, highlighting the recent silicon feedstock supply problem. When available, error in individual data points can be used to perform weighted fits in order to decrease fitting errors. To illustrate this approach, an analysis of Dutch PV system price development over the period 1992-2002 shows that PR is 0.876 +/- 0.010, where the error is decreased with respect to unweighted fitting. The PR = 0.794 has been used to analyze the cost targets stated in the Strategic Research Agenda as formulated by the European PV Technology Platform for the years 2013, 2020 and 2030. Assuming that such a PR is maintained, it is concluded that these targets may be attained at sustained annual growth rates of 21-42%, which seems feasible. Copyright (C) 2007 John Wiley & Sons, Ltd.

Zonne-energie is een schone en onuitputbare vorm van energie. De energie kan kleinschalig door consumenten geproduceerd worden, maar ook grootschalig met centrales in het open veld. Dit document beoogt consumenten bewust te maken van de manieren die er zijn om gebruik te maken van zonne-energie. Er wordt ingegaan op verschillende vormen van zonne-energie: passieve zonne-energie, zonneboilers, zonnecelsystemen, zonnecellen ingebouwd in consumentenproducten en tenslotte zonnecentrales.

Photovoltaic (PV) technologies have shown remarkable progress recently in terms of annual production capacity and life cycle environmental performances, which necessitate timely updates of environmental indicators. Based on PV production data of 2004-2006, this study presents the life-cycle greenhouse gas emissions, criteria pollutant emissions, and heavy metal emissions from four types of major commercial PV systems: multicrystalline silicon, monocrystalline silicon, ribbon silicon, and thin-film cadmium telluride. Life-cycle emissions were determined by employing average electricity mixtures in Europe and the United States during the materials and module production for each PV system. Among the current vintage of PV technologies, thin-film cadmium telluride (CdTe) PV emits the least amount of harmful air emissions as it requires the least amount of energy during the module production. However, the differences in the emissions between different PV technologies are very small in comparison to the emissions from conventional energy technologies that PV could displace. As a part of prospective analysis, the effect of PV breeder was investigated. Overall, all PV technologies generate far less life-cycle air emissions per GWh than conventional fossil-fuel-based electricity generation technologies. At least 89% of air emissions associated with electricity generation could be prevented if electricity from photovoltaics displaces electricity from the grid.