Carbon footprint assessment of photovoltaic modules manufacture scenario

Transcription

1 20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. Carbon footprint assessment of photovoltaic modules manufacture scenario Antonio Dominguez-Ramos, a,b Michael Held, b Ruben Aldaco, a Matthias Fischer, c Angel Irabien a a Department of Chemical Engineering and Inorganic Chemistry, University of Cantabria, Avda. Los Castros s.n., Santander, Spain, domingueza@unican.es b Department of Life Cycle Engineering, LBP, University of Stuttgart, Hauptstrasse 113, Echterdingen, Germany c Fraunhofer Institute for Building Physics Department Life Cycle Engineering, Hauptstrasse 113, Echterdingen, Germany Abstract The influence of production sites location within process design and optimization is addressed to consider operational costs such as transportation or labor. But when the environmental sustainability of a system product is considered in terms of Life Cycle Assessment, this influence is sometimes minimized due to the lack of reliable data. The impact of grid mix in each echelon within the supply chain of a product can be significant. In this work, it is demonstrated the influence of the location of production sites for photovoltaic solar modules in the final environmental profile, expressed as CO 2 -eq. Results show that the Global Warming metric can be as large as twice when using this global approach. Keywords: Location, grid mix, photovoltaic solar energy, environmental impact, life cycle analysis 1. Introduction Life Cycle Assessment (LCA) has been largely applied in process design and optimization [1]. The LCA methodology is typically considered within optimization problems with environmental constraints, which allows us to consider different topics such as the influence of the production sites location, especially in the relation with the environmental impact related to transport issues. However, the different raw materials coming into a production process can be produced in different countries. When power grid is required in the production of those raw materials, the national country grid mix could influence in the results of the environmental profile of the final product in a relevance way, specially when the product is energy intensive. In this work, as a case study from the photovoltaic (PV) industry, it was considered the impact of the different national grid mixes (global mix) in the environmental profile of PV modules using a LCA approach. Most of LCA studies related to PV solar modules consider specific country grid mix for the origin of the required electricity in almost all the production process along the production chain, being one of the key parameters affecting the environmental profile the grid mix [2]. However, the current global market makes the production sites to be spread along the world. This fact makes that the components that integrate a PV module could be produced in different countries. Therefore, using individual national grid mix to assess the environmental performance of a solar module

2 A. Dominguez-Ramos et al. could lead to disturbances from the potential real values of world-wide produced PV modules. 2. Application of the LCA methodology The LCA approach is essential for an objective quantification of the environmental impact related to system product, giving a vision of the relationship between human activities and environment, and identifying the environmental impacts [3]. The LCA software is conventionally utilized in combination with professional databases that provide life cycle inventories for system products (chemicals, electricity, transportation ). GaBi 4 [4] was used as reference LCA software (foreground data integration) in combination with ecoinvent v2.0 database [5] (for the background data). The basic stages in Life Cycle Analysis methodology are shown in the left box of the Figure 1 and were applied in this work as reported in [6,7]: GOAL & SCOPE DEFINITION DATA SEARCH: ANNUAL REPORTS, EXPANSION CAPACITY ANNOUNCEMENTS, SEC FILLINGS COMPANY PRODUCTION SITES, PRODUCTION CAPACITY FOR EACH SITE, COVERED ECHELONS IN THE SUPPLY CHAIN INTERPRETATION LIFE CYCLE INVENTORY data search DATA QUALITY CHECK NO DISCARD LIFE CYCLE IMPACT ASSESSMENT YES INCORPORATION TO THE COUNTRY CONTRIBUTION Ci,j,k BASIC STAGES IN THE LIFE CYCLE ASSESSMENT METHODOLOGY DATA SEARCH METHODOLOGY Fig 1. Basic stages in Life Cycle Assessment methodology and data search methodology used to compile the relevant information from the different PV companies 2.1. Goal and scope As previously described, the goal and scope of this work is to assess the environmental loads as CO 2 -eq. produced by the module manufacturing process considering three different PV technologies, non-global grid mixes and global grid mixes for each step within the production of PV modules, so assuming the influence of the location of the production sites. The selected functional unit is one kwh coming for the different technologies PV modules using Spanish framework conditions as reference for the module s performance. All the energy and materials required to produce the photovoltaic modules as well as all the required products and processes were based on ecoinvent database (the final disposal is not modeled) Inventory analysis Companies could cover different stages within the supply chain of photovoltaic solar modules. They can be specialized just in one echelon of the supply chain or in covering the whole process in a corporation with vertical structure. Additionally, companies could have different branches covering different PV technologies. Within conventional silicon wafer based photovoltaic solar technologies, it was selected the monocrystalline (c-si) silicon and cadmium telluride (CdTe) and copper-indium-gallium-selenide (CIGS) among thin films technologies. It is possible to list the basic stages for monocrystalline silicon wafer based photovoltaic solar modules within the supply chain: metallurgical grade (MG) silicon, polysilicon production, Czochralski (CZ) ingots and

3 Carbon footprint assessment of photovoltaic modules manufacture scenario wafers manufacture, cells production and modules assembly. For thin film technologies, all steps are assumed in the same production site. Figure 2 summarizes the considered stages in this work for which global grid mixes were developed: MG Silicon Polysilicon c-si CZ wafers c-si cell c-si Module CdTe module CIGS module Fig. 2 Supply chain in the production of monocrystalline silicon wafer based photovoltaic solar modules (c- Si) (top) and in the production of thin film modules (CdTe and CIGS) (bottom). Data gathering to build-up the contribution of each country to the global grid mix was based on a simple analysis of the available information from different sources, mainly annual reports, official capacity expansion announcements, web information from each company and other sources such as SEC fillings where available as shown in the right box of Figure 1. The representativeness of the gathered data was qualitatively checked and only those reports or announcements based on official information coming from the different companies was considered. As it was not always available a very well-defined year for the start-up of new plants or capacity expansions for those under operation, a time horizon fixed at 2015 was considered, that is the forecasted production capacities before In order to check the reliability of the data gathering, it was checked that the top companies in each PV technology in 2015 were considered. The uncertainty related to the possibility of installing new plants or to the future capacity expansions was assumed to be relevant due to market fluctuations [8]. Indeed, the idea behind this work was to assess the impact of using global grid mixes in the supply chain of the photovoltaic modules and it was not to have an accurate forecast of word-wide production capacities. For each company, the location of production sites and its corresponding annual manufacturing capacities (expressed as peak power from the whole annual output) was listed for the different PV technologies. Once the individual contribution of each relevant company is assessed, the total manufacturing capacity for one individual country can be assessed: consequently, the final contribution C i,j,k of each i country to the global production of each j step within each k technologies was completed: wi, j, k Ci, j, k = j N, k O i= M w i= 1 i, j, k Being M the total number of considered countries (21), N the total number of steps within the production chain of k technology (N=5 if k=c-si; N=1 if k= CdTe or CIGS) and O the total number of technologies (c-si, CdTe and CIGS). The contribution C i,j,k was used to consider the global grid mix for each step and PV technology: corresponding values are presented in Table 1. The Spanish frame conditions were chosen for key PV parameters in 2007 such as solar irradiation (1825 kwh m -2 y -1 ) and performance ratio (0.72); typical values for lifetime (30 years for c-si, 25 years for CdTe and CIGS) and efficiency for 2007 (15% for c-si, (1)

4 A. Dominguez-Ramos et al. 9% for CdTe and 10% for CIGS) were chosen for each technology [9]. Of course, it was assumed that for each country there is only one grid mix, and differences between regions in the same country were not considered. Once those values were available, appropriate models were implemented in GaBi 4 and the original non global mixes (corresponding grid mix from ecoinvent) in the modules production were substituted by the new global grid mixes in each step and technology. It is worthy to make explicit the fact that only the electricity at medium voltage level was considered and no modifications were taken when other energy sources such as natural gas or materials in the production lines were used. The database ecoinvent includes life cycle inventories for most of the grid mixes of the countries listed in Table 1 (for those cases where grid mix data were not available, external references were used [10]). National grid mix data in ecoinvent were mainly from 2004; data for c-si modules production range from 2005 to 2006; for CdTe data from 2006 was considered and finally, for CIGS, 2007 data was used in ecoinvent. Table 1. Contribution as percentage of each country to the total production of each echelon in the manufacturing process of the PV modules. Expected time horizon below 2015 Wafers b Cells Modules Modules Modules Country MG- poly- Si a Si c-si CZ c-si c-si CdTe CIGS Australia 1 1 Brazil 5 Canada 12 PR of China Czech Republic Germany Hungary 12 India 4 7 Italy 5 Japan Malaysia 65 Norway 6 4 Philippines Russia 10 Singapore 6 South Korea 2 Spain 4 8 Switzerland 6 Taiwan 5 United Kingdom 3 Unites States of America a Remaining 21% was modeled as UCTE production at medium voltage b Czochralski (CZ) ingot production and corresponding sawing process to convert c-si CZ ingots into c-si wafers included in one step: c-si CZ wafer production step 2.3. Life cycle impact assessment Global Warming (included in CML2001 methodology) was the impact category chosen to assess the total load expressed as CO 2 -eq. per produced kwh. Table 2 summarizes the values obtained for the different PV technologies using GaBi 4:

5 Carbon footprint assessment of photovoltaic modules manufacture scenario Table 2. Comparison of environmental loads (expressed as gr CO 2 -eq.kwh -1 ) Silicon wafer based Thin film c-si CdTe CIGS Using non global grid mixes Using global grid mixes Interpretation A detail interpretation of results is given in the results and discussion section 3. Results and discussion From Table 1 it can be seen clearly that the main actors of selected PV technologies modules production are China, Germany, Malaysia and USA. It is clear that China has a predominant role in PV module production, being the focus the monocrystalline silicon wafer based PV technologies (especially from wafers to modules) rather than in selected thin film PV technologies. In the other hand, USA has a relevant contribution in polysilicon production and CIGS; Germany is focused in thin film technologies and Malaysia is completely oriented to CdTe technologies. Consequently, it can be expected that depending on the selected PV technology, the final environmental profile could be different due to the also expected different contribution of each grid mix in each echelon of the supply chain. Obtained values from Table 2 for non global values in 2007 reference were discussed in [9]. It can be observed from Table 2 a serious increase in the value of the CO 2 -eq. load from one reference kwh coming from the c-si module (36 to 50 gr CO 2 -eq. kwh -1 ). This difference is mainly due to the contribution of China to the production of polysilicon (Siemens process): an increase of 7 gr CO 2 -eq. kwh -1 is observed in this step. Figure 2 shows the relative contribution of each echelon in the supply chain to the total CO 2 -eq. load for the production of 1m 2 of c-si module. From Figure 2 it can be noticed that the use of global values makes the step related to production of modules, wafers and CZ ingots to have a lower contribution being CZ ingots and polysilicon the production steps that accounts for 60% of the total CO 2 -eq. load. Consequently, relative contributions are similar but no the total value for the load: moving from non global values to global values changes the environmental profile of the module. c-si modules c-si cells c-si wafers GLOBAL NON GLOBAL c-si CZ Polysilicon MG silicon Fig. 2 Contribution of each step to the total CO2-eq. load in the production of 1 m 2 of c-si module. The situation is different for technological-advanced production processes such as CdTe and CIGS. In CdTe modules, using as reference a 1m 2 module and non global values, around 34% of the CO 2 -eq. load is due to the solar glass substrate and the tempering flat glass of the module. Therefore, the cover-glass and the glass substrate have an

6 A. Dominguez-Ramos et al. important contribution to the total CO 2 -eq. emissions, so the impact of the grid mix in the module production is lower. Although the grid mix changes significantly from Germany (656 gr CO 2 -eq.) to Malaysa (875 gr CO 2 -eq.), the difference is meaningless and about 1 gr CO 2 -eq., which is logical as in the non global case the German grid mix is originally used (100% contribution) and in the global case the contribution is about 22% for a low-energy intensive step. On the other hand, in the case of CIGS modules and non global values, the contribution of solar glass substrate and the tempering flat glass is only 11%. Although the contribution of USA to the total production is about 45%, the rest of the production is mainly in Germany, so the expected changes versus the non global values (where 100% of production was allocated to Germany) are minor due to the similar emissions from the grid mix (770 gr CO 2 -eq. kwh -1 for the USA) in a low-energy intensive step. Consequently, it has been demonstrated that the current impact of the production sites location in the environmental sustainability of a product (photovoltaic modules in this case study) is relevant and that the use of global grid mixes is suggested to obtain more realistic results. Therefore, it is desirable that global contributions for the grid mix could be considered when assessing the environmental sustainability of a system product or process, because the location of the production site has a severe impact in the final environmental profile, especially if the product is energy-intensive. This methodology could be applied in any other system product if minimization of CO 2 -eq. load is an objective function. 4. Acknowledgements This research is financially supported by the Spanish Ministry of Science and Technology (Project CONSOLIDER CTM ) and by CDTI in the framework of the Ingenio 2010 Programme under the SOSTAQUA Project leaded by Sociedad General de Aguas de Barcelona. A. Dominguez-Ramos thanks the Ministry of Innovation and Science for the FPU fellowship reference number AP References [1] A. Azapagic, 1999, Life cycle assessment and its application to process selection, design and optimization, Chem. Eng. J., 73, 1, 1-21 [2] S. Pacca, D. Sivaraman, G.A. Keoleian, 2007, Parameters affecting the life cycle performance of PV technologies and systems, Energy Policy, 35, 6, [3] A. Dominguez-Ramos, R. Aldaco, A. Irabien, 2007, Life Cycle Assessment as a Tool for Cleaner Production: application to Aluminium Trifluoride, Int. J. Chem. React. Eng., 5, A33, 1-9 [4] GaBi, 2008, GaBi 4 Software-System and Databases for Life Cycle Engineering. Chair of Buildings Physics (University of Stuttgart), PE International, [5] ecoinvent, 2008, The Life Cycle Inventory Data version 2.0. Swiss Center for Life Cycle Inventories, [6] ISO, 2006, Environmental management. Life Cycle Assessment. Principles and framework (ISO 14040:2006). International Organization for Standardization [7] ISO, 2006, Environmental management. Life Cycle Assessment. Requirements and guidelines (ISO 14044:2006). International Organization for Standardization [8] A. Luque, 2001, Photovoltaic market and costs forecast based on a demand elasticity model, Prog. Photovoltaics, 9, 4, [9] A. Dominguez-Ramos, R. Aldaco, A. Irabien, 2009, Photovoltaic Solar Electro-Oxidation processes (PSEO): energy balance and lca approach, WCCE-8, Montreal, Canada [10] International Energy Agency, 2006, World Energy Outlook 2006, Tables for Reference and alternative policy scenario projections. Annex A.