Life Cycle Assessment of Solar Photovoltaic Microgrid Systems in Off

Dec 23, 2016 - Access to a reliable source of electricity creates significant benefits for developing communities. Smaller versions of electricity gri...
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Life Cycle Assessment of Solar Photovoltaic Microgrid Systems in Off-Grid Communities Andrew Bilich,*,† Kevin Langham,† Roland Geyer,† Love Goyal,† James Hansen,† Anjana Krishnan,† Joseph Bergesen,† and Parikhit Sinha‡ †

Bren School of Environmental Science and Management, University of California, Santa Barbara, California 93106-5131, United States ‡ First Solar, 350 W. Washington St., Suite 600, Tempe, Arizona 85281, United States S Supporting Information *

ABSTRACT: Access to a reliable source of electricity creates significant benefits for developing communities. Smaller versions of electricity grids, known as microgrids, have been developed as a solution to energy access problems. Using attributional life cycle assessment, this project evaluates the environmental and energy impacts of three photovoltiac (PV) microgrids compared to other energy options for a model village in Kenya. When normalized per kilowatt hour of electricity consumed, PV microgrids, particularly PV−battery systems, have lower impacts than other energy access solutions in climate change, particulate matter, photochemical oxidants, and terrestrial acidification. When compared to small-scale diesel generators, PV−battery systems save 94−99% in the above categories. When compared to the marginal electricity grid in Kenya, PV−battery systems save 80−88%. Contribution analysis suggests that electricity and primary metal use during component, particularly battery, manufacturing are the largest contributors to overall PV−battery microgrid impacts. Accordingly, additional savings could be seen from changing battery manufacturing location and ensuring end of life recycling. Overall, this project highlights the potential for PV microgrids to be feasible, adaptable, long-term energy access solutions, with health and environmental advantages compared to traditional electrification options.

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INTRODUCTION Currently over 1.3 billion people (18% of the global population) worldwide lack access to an electrical grid.1−3 Without grid access, rural and suburban communities rely on alternative energy sources such as diesel generators and kerosene or biomass combustion for cooking and lighting.3−5 Unfortunately, the use of these energy sources causes numerous health impacts. Uncontrolled combustion of kerosene, diesel, and biomass can lead to damages to human and environmental health from accidental ingestion, fires, respiratory illnesses, carcinogenic emissions, and the destruction of local habitats.5−8 Access to a reliable source of electricity creates significant benefits for communities in terms of health, economic development, and overall quality of life. Communities receive health benefits through sanitation improvements, clean cooking methods (improved air quality), refrigeration, and propagation of health education through media (i.e., radios and television).1,6,9,10 Electricity access is also significantly linked to improved productivity, growth, poverty alleviation, household income, employment, new enterprise development, and enterprise productivity.11,12 Significant quality of life benefits are also created, most notably in improved educational opportunities such as vocational classes and improved educational performance through better teachers and extended study environments.13−15 © XXXX American Chemical Society

Traditionally, electrification has been achieved by extending the central electricity grid. However, grid extension is capital and time intensive and accounts for substantial environmental impacts.16−21 Due to these issues, grid extension may not be the best option to electrify rural communities in Africa.22 An alternative solution is smaller stand-alone versions of electrical grids known as microgrids.22 Microgrids are an attractive option for off-grid communities because they can be preconstructed and directly installed in the communities relatively rapidly, and with reduced impacts to the local environment. A basic microgrid system consists of the energy generation component (e.g., photovoltaic (PV) modules), an energy back up system (e.g., battery bank or diesel generator), and other necessary components like charge controllers and balance of systems. When implemented in small villages, microgrids are intended to power small electronic devices, the charging of cell phones, small-scale cooking, and simple lighting.22 There are over 3700 microgrids already in operation around the world with varying energy storage and generation technologies, and a wide range of output capacities. These Received: October 27, 2016 Accepted: December 12, 2016

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DOI: 10.1021/acs.est.6b05455 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology facilities have proven the potential of microgrids and paved the way for expansion.22 Studies from the International Energy Agency (IEA) have predicted that over 50% of the connections necessary to electrify the remaining 1.3 billion people in the world will rely on alternative energy sources, such as microgrids.23 First Solar, a major manufacturer of thin film cadmium telluride (CdTe) PV panels, has teamed up with Powerhive to install three microgrid pilot projects in Kenya with 10, 20, and 50 kW capacities.24 Off-grid developers would benefit from understanding all of the benefits and trade-offs of microgrid designs. Therefore, the primary objective of this analysis was to evaluate the life cycle environmental impacts and trade-offs of three microgrid systems using process-based life cycle assessment (LCA) and to determine how these impacts compare to each other, to traditional solutions for electrification, and to small scale energy options in Kenya. In addition to the overall microgrid system design, this project also seeks to explore the effect of component variation and other factors on the life cycle impacts of these microgrid systems. While there have been a number of lifetime technological and economic feasibility studies of solar microgrids in off-grid communities, focusing on the lifecycle environmental performance of microgrid systems in off-grid communities is a relatively new application of LCA methodology.25−27 As of the time of this report, only one study focused on the lifetime impacts of microgrids at a system level across a variety of environmental categories and that was an analysis of a diesel−PV−wind hybrid microgrid on Koh Jig, an island near Thailand.28 Instead, most of the pertinent LCA research focuses on components of microgrids (i.e., modules and batteries) in isolation or in other applications. Utility scale, rooftop, and other grid connected applications of PV modules have been the subject of numerous LCA studies summarized by the NREL LCA harmonization studies and the International Energy Agency’s Task 12 working group.29−32 First Solar has also published several LCA studies on the manufacturing and application of the CdTe modules that have been modeled in this analysis.33−35 Batteries, particularly in electric vehicles, have similarly been a recent focus in LCA research. Argonne National Lab published a comprehensive review and aggregation of previous LCAs on lead-acid (PbA), nickel metal hydride (NiMh), and lithium-ion (Li-ion) batteries.36,37 Various other battery LCAs have also been published considering mobility applications and different battery chemistries.37−42 Life cycle estimates for other major components such as diesel generators,28 charge controllers,43 and electricity wiring32,44 have been produced as part of larger studies. Given this current state of research, by modeling three complete microgrid systems including all required microgrid components and providing a systematic comparison of the environmental impact of PV microgrids for electrification in offgrid communities in Kenya, this study significantly contributes to both life cycle assessment and solar microgrid research.

(a) PV−Battery: a PV microgrid system with battery bank (b) PV−Diesel: a PV microgrid system with a diesel generator (c) PV−Hybrid: a PV microgrid system with both a battery bank and diesel generator Each system is modeled to include all necessary components that are part of real world installations. All of the systems are designed to meet the demand of a model community in Kenya and operate under the same meteorological conditions. Therefore, 1 kWh of electricity consumed by the community is chosen as the functional unit. Component Modeling. Using a combination of existing LCA databases such as Ecoinvent 2.2 and Gabi 6, manufacturer’s specification sheets, Hybrid Optimization of Multiple Energy Resources (HOMER) software, and technical and scientific literature supplemented by discussions with industry experts and microgrid developers, life cycle inventories (LCIs) are developed for each of the microgrid components. Table 1 shows details about the LCI data and the data sources. System Modeling. All three systems are modeled using a set of common baseline parameters: • Village demographics: 100 people/5.7 people per household76 • Daily demand per household: 1.545 kWh/day76 • Total daily demand: 27.108 kWh/day • Peak load factor: 2.6977 • 22 year average daily solar insolation: (1°16′ S, 36°48′ E): 5.935 kWh/m2·day78 • Microgrid lifetime: 25 years • PV module type: CdTe • Battery chemistry: Li-Ion (nickel−cobalt−manganese) • End of life scenario: Landfill The demographics, meteorology, electrical demand, end of life, and CdTe PV module parameters were largely chosen to reflect First Solar’s microgrid pilots in Kenya. This analysis focuses on future microgrid projects and environmental performance which is why the lithium ion battery chemistry was chosen in favor of the more traditional lead acid chemistry. Complete LCAs for microgrids with Mono-Si modules and PbA batteries have been added as scenarios. The type and size of these components vary for each of the three microgrid systems. For the PV−battery system, these components include PV modules, balance of systems (BOS), inverters, charge controllers, batteries, distribution systems, and security fencing. The PV−hybrid system utilizes the same components (some sized differently) and a diesel generator. The PV−diesel system contains a diesel generator with no charge controllers or batteries. The system-specific assumptions and sizing are detailed below. PV−Battery System. In the PV−battery system, the PV modules are sized to meet the average daily electricity demand with average daily insolation. As with any PV installation, the issue of solar intermittency needs to be accounted for and managed. In this system, a battery bank with a 24-h autonomy period is modeled to complement the PV array. During periods of large variability an autonomy period of 24 h is unlikely to buffer entirely against mismatched supply and demand. From the HOMER modeling it was determined that depending on the efficiencies of the microgrid components, the PV−battery system would have 7−11% unmet demand. Therefore, in this analysis, a 7% variability loss was modeled in the baseline PV− battery system, meaning that the PV−battery system could only

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METHODOLOGY Overview. This study quantifies the life cycle environmental impacts of three types of PV microgrid systems in off-grid communities in Kenya according to ISO 14044:2006.45 The PV microgrids are also compared to small-scale diesel generators and electricity from the central grid. The three types of PV microgrids modeled are B

DOI: 10.1021/acs.est.6b05455 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology Table 1. Lifetime, Specifications, and Data Sources for Modeled Microgrid Components Component

Lifetime (y)

Specifications/Assumptions

Data sources

photovoltaic module

25

32, 46−53

balance of systems battery pack

25 31 (PV−battery) 30 (PV−hybrid)

charge controller

15

diesel generator

30 (PV−hybrid), 1.7 (PV−diesel) (15,000 operational hours)

inverter

10

electricity wiring electricity meters security fencing

25 25 25

cadmium telluride (CdTe) module nameplate efficiency 15.3% annual degradation rate 0.5%/y average lifetime efficiency 13.9% average lifetime power output 0.1 kW module area 0.72 m2 open circuit voltage 88.2 V short circuit current 1.79 amps fixed tilt mounting system lithium-ion (nickel cobalt manganese) 24 V efficiency 95% discharge depth 50% cell capacity 20 Ah nominal cell voltage 3.65 V average efficiency 85% maximum input operating current 15 Amps nameplate capacity 3.3 kW (PV−diesel) 3.89 kW (PV−hybrid) power to weight ratio 0.0155 (PV−diesel) 0.0168 (PV−hybrid) efficiency 26.77% (PV−hybrid) 26.66% (PV−diesel) 5 kW inverter 92% efficiency average distance to households 20 m, each household has 2 wires 1 m per household 10 ft high, 1 and 3/4 in. fence with posts every 3 m, steel chain link fence

62−67 28, 68−72

34 32, 44 73 74, 75

system could be covered by electricity production from the generator. Using HOMER, the genset in this system is modeled to have an efficiency of 26.77%. The PV part of the Hybrid system also assumes a 0.743 derate factor for system sizing. PV−Diesel System. The PV−diesel system is modeled based on the hourly variability in solar insolation, the average daily electricity demand, and the peak electricity demand. Three hour average insolation values from the NASA SSE database are used to estimate the solar insolation variability over a 24 h period.78 Hourly electricity demand is calculated based on the 24-h load profile of the village of Marsabit in Northern Kenya.72 The difference between available solar insolation and electricity demand is used to calculate the proportion of total daily demand met by the PV modules and diesel generators. The electricity demand curve lags the insolation curve. As a result of this, when the installed capacity of PV in the PV−diesel system is increased, the share of daily demand the can be supplied by PV grows at a decreasing rate. The PV−diesel system is sized such that all of the solar electricity generated is used, meeting 22.44% of the daily demand. The remaining 77.56% is met by a 3.3 kW diesel generator. The efficiency for the diesel generator in the PV− diesel system is modeled in HOMER to be 27.66%. The derate factor for the PV−diesel system is calculated to be 0.92 because it excludes charge controllers and batteries. System Sizing. Based on the above parameters and methodology, the systems were modeled in Excel and Gabi with the sizing and lifetime production/operation of specific electrical components like generators, PV panels, and batteries verified using HOMER software. Additional information on the HOMER modeling can be found in the Supporting Information. Table 2 shows the components and characteristics of the three microgrid systems over their 25 year lifetime.

supply 93% of the community electricity demand. Different levels of variability loss are explored in a scenario analysis in order to examine their impact on the LCA results. A derate factor combining the efficiencies of the microgrid components is also calculated to account for conversion losses in the system using the equation below: derate factor = Effbattery × Effcontrollers × Eff inverters

Using the efficiencies from Table 1, the derate factor for the PV−battery system was calculated as 0.743. Using this derate factor, the required installed capacity of the PV array in kilowatt hours per day can be calculated as follows: ⎛ kWh ⎞ required daily capacity ⎜ ⎟ ⎝ day ⎠ daily electricity demand of community = derate factor

Based on this, the total number of PV modules needed is required no. of PV modules =

32, 34 36, 40, 41, 54−61

required daily capacity daily PV production per squared meter of module × module unit area

Where the daily PV production per squared meter of module is daily PV production = average daily solar irradiation × average lifetime module efficiency

PV−Hybrid System. The PV−hybrid system is modeled similarly to the PV−battery system, except with an added 3.89 kW diesel generator to complement the electricity generation from the PV array. The addition of the diesel generator allows the PV−hybrid system to fully meet the electricity demand as, in principal, any unmet demand in the baseline PV−battery C

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aluminum is also modeled. Detailed recycling tables can be seen in the Supporting Information. CdTe vs Mono-Si PV Modules. In addition to the baseline CdTe module, a monocrystalline silicon (mono-Si) module was also modeled. The mono-Si panel modeled primarily utilized life cycle inventory (LCI) data from sections 5.1.4−5.1.9 of the International Energy Agency’s PVPS Task 12 LCI report.32 The processes and materials are altered slightly to reflect First Solar’s TetraSun mono-Si module.49 Important differences between the CdTe and Mono-Si modules are their average lifetime efficiencies (13.9% vs 16.95%), average lifetime power output (0.1 vs 0.278 kWp), and module areas (0.72 vs 1.64 m2). Li-ion vs Pb-A Batteries. The baseline PV−battery and PV−hybrid systems utilized a lithium-ion (Li-Ion) battery pack, but an additional lead-acid battery (PbA) chemistry was also modeled. The PbA batteries in this analysis are based off the life cycle inventory for Exide Absolyte GX batteries.37 In terms of design characteristics the specific energy density of lithium-ion batteries is 175 Wh/kg compared to 26 Wh/kg for Pb-A. Li-ion batteries also have much higher roundtrip efficiency than PbA batteries (95% vs 80%). Combined, these differences result in a heavier Pb-A battery pack. Lastly, Li-ion batteries also offer a much higher lifetime throughput than PbA batteries. In the modeled microgrids, the Li-ion battery bank lasts 30 years, compared to only 13 years for PbA batteries.36,37,40,41 Other Scenarios. Additional scenarios are developed for the location (electricity grid) used in the manufacturing of the lithium-ion batteries. The baseline is assumed to be an average European electricity grid. Four other grids (United States, China, France, and Switzerland) are also modeled to test the influence of component sourcing on overall microgrid impact. The baseline value for unmet demand is 7% (a 7% production loss for the PV−battery system and a corresponding 7% diesel contribution for the PV−hybrid), but additional scenarios of 9% and 11% are also modeled. Impact Assessment. Seven categories from ReCiPe 2008 are used for impact assessment, because they represent an important mix of impacts to climate, air, water, ecosystems, and human health. They are climate change (kg CO2e), freshwater eutrophication (kg P equiv), human toxicity (kg 1,4-DB equiv), particulate matter formation (kg PM10 equiv), photochemical oxidant formation (kg NMVOC), terrestrial acidification (kg SO2 equiv), and terrestrial ecotoxicity (kg 1,4-DB equiv).83 In addition to these environmental impacts, the cumulative energy demand (MJ) and energy yield ratio are also calculated for each electrification option. The energy yield ratio is calculated as

Table 2. Lifetime Amounts and Characteristics of PV− Microgrid Components Component solar PV module area (m2) BOS area (m2) charge controller (units) batteries mass (kg) unmet demand (%) diesel contribution (%) electricity from diesel (kWh) diesel generators (units) security fencing length (m) distribution wiring length (m) residential meters (units) derate factor lifetime electricity consumption by the community (kWh)

PV− battery

PV− hybrid

PV− diesel

42 42 7.18 675 7 N/A N/A N/A 29 702 17.5 0.743 230,053

42 42 7.18 675 N/A 7 17,316 1 29 702 17.5 0.743 247,368

8 8 N/A N/A N/A 77.56 191,859 14.3 12.7 702 17.5 0.92 247,368

As stated, these three microgrid systems were modeled to meet the maximum amount of community’s daily electricity demand with the minimum environmental impact. A 24-h load and generation break down for these three microgrid systems as well as details on specific system modeling can be found in the Supporting Information. Comparison to Traditional Electrification. In addition to the three microgrid systems, inventory models for small-scale home diesel generators and central grid electricity are developed to provide a wider comparison of energy access options in Kenya. The home diesel genset is modeled the same as the generator in the PV−diesel microgrid. A marginal electricity grid for Kenya is modeled to represent the impact of the energy resources that may be developed to provide electricity access in these communities in the future. This marginal grid is based on the Ten Year Power Sector Expansion Plan in Kenya. It includes the following resource mix: 3% from oil, 6% from imported hydro, 31% from coal, 33% from geothermal, 10% from wind, and 17% from natural gas.79 It is important to point out that the inventory model of the Kenya electricity grid does not include any impacts related to the expansion of the conventional grid to off-grid communities. The impacts modeled only include electricity production adjusted by the projected transmission losses (14%) from Kenya’s power grid.79 The impacts from the construction of new power plants and transmission infrastructure are excluded. Therefore, the estimates of impacts from the Kenya electricity grid are conservative, and impacts are likely much higher. For example, an LCA study of electricity infrastructure found that climate change impacts from transmission extension alone could range from 12.6 to 336 tons of CO2e/km depending on the type of transmission needed.80 End of Life Scenarios. The study includes a recycling scenario as an alternative to the baseline end-of-life scenario, which is landfill. Since all of the components are modeled using only primary materials (i.e., no recycled content), the avoided burden approach is used for microgrid recycling. This means a credit is given for recycling materials in the components to account for the potentially avoided production of primary materials. The materials that cannot be recycled are landfilled. Using available literature, recycling processes are modeled for the PV modules,33,81 BOS,81 batteries,82 and diesel generators.28 Generic recycling of charge controllers to recover

energy yield ratio =

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cumulative engery demand (MJ) lifetime electricity delivered to consumers (MJ)

RESULTS The five energy access options (home genset, grid extension, and the three microgrids) are modeled and normalized based on the lifetime electricity consumed by the community (i.e., impacts per kWh). The normalized environmental impacts and the lifetime energy impacts for the five options with the baseline landfilling at the end of life are presented in Table 3. First, looking at the energy impacts in Table 3, it is clear to see that the PV−battery and the PV−hybrid systems have substantially lower energy burden than the other electrification D

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Environmental Science & Technology Table 3. Life Cycle Environmental and Energy Impacts of Energy Access Options in Kenyaa

a

Values in parentheses indicate an option’s impacts relative to the PV−battery. Shading indicates the highest (orange) and lowest (green) impacts in a given impact category.

options. This is almost exclusively due to the large reliance on fossil fuels for electricity production in the home diesel genset, marginal grid mix, and PV−diesel systems. The energy payback time for the PV−battery system is 9.2 years. Table 3 shows substantial differences between the environmental impacts of the five electricity options. The following uncertainties are used for impact assessment:84 10% for climate change, 30% for respiratory inorganic effects, acidification, or eutrophication, 1−2 orders of magnitude for human toxicity, and 1−3 orders of magnitude for ecotoxicity. Applying these rules to the life cycle impacts above shows that there are significant differences across the energy access options in the climate change, eutrophication, particulate matter, photochemical oxidants, and acidification categories.

The differences in both toxicity categories, particularly ecotoxicity, may not be significant. Since the climate change, particulate matter, photochemical oxidants, and acidification categories are highly correlated with one another, we will focus on these categories first and return to eutrophication and toxicity later (Figure 1). Impacts in these four categories are driven by the combustion of fossil fuels. Unlike the other energy access options, the PV−battery system is not reliant on fossil fuels to generate electricity. Instead, the majority of the impacts from the PV−battery system come from battery production, primarily the production of the copper, cobalt, and manganese contained in the battery. Accordingly, across the four categories E

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Figure 1. PV Microgrids vs Traditional Electrification. All impacts are relative to the PV−battery impact in a given category.

Figure 2. Microgrid impact savings from end of life recycling.

the PV−battery system has the lowest impacts of the five energy access options. Figure 1 shows that PV−microgrids, particularly PV−battery microgrids, have substantial environmental benefits (22−99%) per kilowatt hour in all four categories compared to home diesel gensets. Compared to the marginal electricity grid mix, the PV−battery system saves 80−88% per kWh depending on the category, whereas the PV−hybrid system saves 34−76% per kWh. The largest savings for both systems comes in the climate change impact category. Finally, the PV−diesel appears to have higher impacts per kWh in all four categories compared to the marginal grid mix. The comparisons of microgrids to the marginal mix, however, are likely to underestimate the benefits of microgrids, because this current comparison does not include the impacts from actually extending the electricity infrastructure,80 but rather just producing 1 kWh of electricity with Kenya’s future marginal energy resources. Despite clear benefits in the climate change, particulate matter, photochemical oxidants, and acidification categories,

there are apparent trade-offs in the eutrophication category for the PV−battery and PV−hybrid systems compared to the other energy access options (Table 3). Recycling. Adding takeback and recycling to the end of life for these microgrid systems substantially affects the overall comparison of PV−microgrids to each other, to home diesel gensets, and to the marginal electricity grid in Kenya (Figure 2). Since the majority of its impacts come from primary material use rather than the burning of fossil fuels, the PV−battery system sees the greatest reduction in impacts from recycling. In total, the PV−battery impact is reduced 17−66% depending on the category. The PV−hybrid system sees slightly lower savings from landfill substitution (7−65%; Figure 2). For both systems, the largest savings come in the human toxicity and eutrophication categories in large part because of the avoided copper. These two categories are also the categories where the potential trade-offs were initially seen. In contrast to the other two systems, recycling of the PV−diesel system had much F

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Figure 3. Contribution analysis for PV−battery microgrids.

mind, location of manufacturing substantially influences overall climate change impact for the PV−battery microgrid. For example, shifting the battery production from the baseline European grid mix to a Chinese grid mix would increase the total microgrid climate change impact by over 43%, whereas shifting from a European grid to the grid of France or Switzerland would decrease overall impacts 19−23%. This highlights the importance of component sourcing on overall microgrid impact. CdTe vs Mono-Si. The only significant difference between the CdTe and mono-Si PV modules comes in the climate change category. In total, using CdTe modules instead of mono-Si modules in these PV-microgrids saves between 6 and 22 g of CO2e/kWh. A comparison of the systems’ per kilowatt hour impacts can be seen in the Supporting Information. Li-Ion vs PbA. Replacing the Li-Ion batteries with PbA batteries in the PV−battery and PV−hybrid systems increases the per kilowatt hour impacts in the climate change (33% and 52% respectively), particulate matter (25% and 53%), photochemical oxidant (37% and 63%), and acidification (35% and 54%) categories. Utilizing PbA batteries also decreases the freshwater eutrophication impact by 11% and 8% in the PV− battery and PV−hybrid systems, respectively. A comparison of the systems’ per kWh impacts can be seen in the Supporting Information. Unmet Demand. Increasing the percent unmet demand (lost solar electricity due to insufficient battery backup), decreases the total amount of electricity that can be delivered by the PV−battery system. Since the environmental impacts remain the same, decreasing the total amount of electricity consumed by the community with the PV−battery system consequently increases the system’s environmental impacts per kWh. The change in impact is proportional to the change in variability. Increasing the variability from 7% to 9% and 11% increases the per kilowatt hour impacts by 2% and 4%, respectively. Changing the unmet demand also affects the per kWh environmental impacts of the PV−hybrid system since it is accounting for the unmet electricity demand from the PV− battery system by generating electricity with a diesel generator. For example, a 4% increase in the amount of diesel electricity

smaller savings across the categories with the majority of the savings coming in the freshwater eutrophication and human toxicity categories. The PV−diesel system, and to some extent the PV−hybrid system, have a large fraction of their impacts coming from the production and burning of diesel fuel which explains the relative decline in recycling savings compared to the PV−battery system. Initially, PV−battery microgrids show significant savings in the climate change, particulate matter, photochemical oxidant, and acidification categories compared to other energy access options in Kenya. When recycling is added, the comparative benefits of the PV−battery system are enhanced in these categories. In the eutrophication, human toxicity, and ecotoxicity categories there initially are potential trade-offs between PV microgrids and the other options, particularly in the eutrophication category. While it does not eliminate the trade-offs seen in these categories, adding recycling substantially reduces these trade-offs for the PV−battery and the PV−hybrid systems. The overall conclusion is that PV microgrid systems with battery backups have the least impacts of the energy access options considered for Kenya, particularly when coupled with a takeback and recycling program at the end of life. Contribution Analysis. Contribution analysis shows that 58−69% of the PV−battery system impacts come from just the lithium-ion battery bank. The next largest contributors are the PV module at 15−29%, and the balance of systems at 6−14% of the total impact (Figure 3). By comparison, in the PV−hybrid system only 11−62% of the impacts come from the lithium-ion battery. This decline is due to the impacts from the burning of diesel fuels. For the PV−diesel system the vast majority of the impacts (40−97%) also come from the burning of diesel. This analysis suggests an opportunity to substantially lower the overall impacts of the PV−battery microgrid by focusing on the battery bank. For example, looking closer at the climate change category for the PV−battery system, 58% of the total impact comes from the lithium-ion battery with 73% of those impacts coming from the manufacturing of the battery cell. In total, 50% of the total battery impact and 30% of the total microgrid impact in climate change comes from just the electricity used in the manufacturing of the battery cell. This in G

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The battery grid mix scenario suggests that the single biggest way to reduce the climate change impact of a PV−battery microgrid is to shift battery production to nations or regions that utilize higher levels of renewable energy. An elementary flow analysis highlighted that another hotspot for microgrid impacts comes from the metals used in the cathode and anode production within the battery cell. In the PV−battery system, the copper used in the production of the battery anode accounted for a majority of the total eutrophication, human toxicity impact, and the ecotoxicity impacts. The copper used for module cabling was also a substantial contributor to overall system impacts most notably in the PV−diesel system. This presents an opportunity to lessen the impact of microgrids substantially if less of these metals can be used in manufacturing or if they can be sourced from lower impact suppliers. The relative importance of the metals’ overall impacts also further highlights the need to establish takeback and recycling programs. While this analysis provides an in depth exploration into the environmental impacts of PV microgrids, it is important to acknowledge the limitations associated with this study. First, the present LCA uses a mix of primary and secondary data for the microgrid components, so the modeled system may not be an exact representation of a microgrid system installed in Kenya. As mentioned previously, this analysis also uses global characterization factors, whereas in reality the category impacts will vary depending on where the elementary flows occur. The development and use of local or regional characterization factors would provide a better representation of microgrid impacts in Kenya. Finally, this analysis does not model the socioeconomic impacts of microgrids (e.g., life cycle costs), the impacts from the inevitable increase in electricity demand, or additional microgrid energy technologies like wind or biomass. All of these limitations present opportunities for future research. Despite those limitations, this analysis highlights the potential for PV microgrids as feasible, adaptable, long-term energy access solutions, with health and environmental advantages over the expansion of central grids and existing incumbent energy options.

increases the per kWh environmental impacts of the system particularly in the climate change (29%), particulate matter (35%), photochemical oxidant (46%), terrestrial acidification (29%), and terrestrial ecotoxicity (14%) categories. Both the eutrophication and human toxicity categories are marginally affected (∼1%) by this change. The PV−diesel system is unaffected by this parameter (at least at low values). A table of the modeled unmet demand changes can be seen in the Supporting Information.

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DISCUSSION This analysis generates several major conclusions regarding the development of solar microgrids as low-impact energy access solutions. First, compared to home diesel gensets and the marginal grid mix, PV−battery and, to a lesser extent, PV−hybrid microgrids have significantly less impacts in the climate change, particulate matter, photochemical oxidant, and terrestrial acidification categories. For these impact categories, batteries are a better backup option for PV microgrids than diesel generators in places like Kenya. This insight is particularly important for particulate matter, photochemical oxidants, and terrestrial acidification because of the local nature of the effects.5−8 While the PV−battery design does have impacts in these categories, the majority of these impacts happen during the manufacturing stage, rather than during the use phase on site in off-grid communities. This suggests a greater opportunity to mitigate or control the impacts. A similar argument can be made for the potential trade-offs in the eutrophication category, namely that the impacts are likely to occur in places where additional controls or mitigation measures can be utilized. In comparing different energy backup technologies (i.e., batteries and diesel generators), our results highlight the potential for energy storage systems to substantially lower the environmental impacts of microgrid systems, particularly the LiIon batteries. At the same time, this analysis also demonstrates that the PV−battery system alone could potentially fall short of meeting community electricity demand. Adding an additional backup system like the diesel generator in the case of the PV− hybrid system can help bridge this gap, but at the cost of additional environmental impacts from diesel fuel consumption. An alternative to this performance/environment tradeoff is the use of other energy backups such as wind power which has been shown to be effective in other microgrid applications in northern Kenya.72 While the environmental impacts of microgrids are important, it is, however, important to keep in mind that there are other factors such as economic considerations and the availability of energy resources like solar and fossil fuels that ultimately affect microgrid system design in real world situations. Another conclusion of this study is the importance of establishing takeback and recycling programs for PV-microgrids at the end-of-life. Not only can this reduce the potential damages from leaving components in place,85−87 but adding recycling at end of life substantially reduces the life-cycle environmental impacts of PV-microgrids, particularly in the PV−battery system where the majority of the impacts come from primary metal use rather than the burning of fossil fuels. Relative to other electrification options, adding recycling increases the benefits of PV−battery microgrids in the climate change, particulate matter, photochemical oxidant, and acidification categories while reducing the potential tradeoffs in eutrophication and toxicity.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b05455. Life cycle inventories of microgrid components, HOMER modeling, daily load/generation profiles for the three microgrids, postrecycling comparisons, unmet demand tables for the PV−battery system, comparison of battery chemistries (Li-ion vs PbA), comparison of PV technology (CdTe vs Mono-si) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank Joao Arsenio (PROSOLIA), Daniel Soto (Sonoma State University), Mitchell Lee (First H

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(23) Pathways for Concerted Action toward Sustainable Energy For All; A report of Sustainable Energy for All, 2012. (24) First Solar. First Solar Analyst Day Presentation, 2014. (25) Ma, T.; Yang, H.; Lu, L. Feasibility Study and Economic Analysis of Pumped Hydro Storage and Battery Storage for a Renewable Energy Powered Island. Energy Convers. Manage. 2014, 79, 387. (26) Ma, T.; Yang, H.; Lu, L. A Feasibility Study of a Stand-alone Hybrid Solar−wind−battery System for a Remote Island. Appl. Energy 2014, 121, 149. (27) Akinyele, D.; Rayudu, R. Techno-economic and Life Cycle Environmental Performance Analyses of a Solar Photovoltaic Microgrid System for Developing Countries. Energy 2016, 109, 160. (28) Smith, C.; Burrows, J.; Scheier, E.; Young, A.; Smith, J.; Young, T.; Gheewala, S. H. Comparative Life Cycle Assessment of a Thai Island’s Diesel/PV/wind Hybrid Microgrid. Renewable Energy 2015, 80, 85−100. (29) National Renewable Energy Laboratory Life Cycle Assessment Harmonization Page. http://www.nrel.gov/analysis/sustain_lcah.htm (accessed February 2016). (30) Kim, H.; Fthenakis, V.; Choi, J.-k.; Turney, D. E. Life Cycle Greenhouse Gas Emissions of Thin-film Photovoltaic Electricity Generation. J. Ind. Ecol. 2012, 16, S110−S121. (31) Hsu, D. D.; O’donoughue, P.; Fthenakis, V.; Heath, G. A.; Kim, H.; Sawyer, P.; Turney, D. E.; Choi, J.-K. Life Cycle Greenhouse Gas Emissions of Crystalline Silicon Photovoltaic Electricity Generation. J. Ind. Ecol. 2012, 16, S122−S135. (32) Frischknecht, R.; Itten, P.; Sinha, P.; de Wild-Scholten, M.; Zhang, J.; Fthenakis, V.; Stucki, M. Life Cycle Inventories and Life Cycle Assessment of Photovoltaic Systems; Report of the International Energy Agency PVPS Task 12 Working Group, 2015. (33) Sinha, P.; Cossette, M.; Menard, J. F. End-of-Life CdTe PV Recycling with Semiconductor Refining. In Proceedings of 27th European Photovoltaic Solar Energy Conference and Exhibition, 2012; pp 4653−4656. (34) Sinha, P.; de Wild-Scholten, M. Life Cycle Assessment of Utility-Scale CdTe PV Balance of Systems. In Proceedings of 27th European Photovoltaic Solar Energy Conference and Exhibition, 2012; pp 4657−4660. (35) Sinha, P. Life Cycle Materials and Water Management for CdTe Photovoltaics. Sol. Energy Mater. Sol. Cells 2013, 119, 271. (36) Sullivan, J.; Gaines, L. A review of battery life-cycle analysis: state of knowledge and critical needs, Report of the Argonne National Laboratory: Illinois, 2010. (37) Spanos, C.; Turney, D. E.; Fthenakis, V. Life-cycle analysis of flow-assisted nickel zinc-, manganese dioxide-, and valve-regulated lead-acid batteries designed for demand-charge reduction. Renewable Sustainable Energy Rev. 2015, 43, 478−494. (38) Notter, D. A.; Gauch, M.; Widmer, R.; Wager, P.; Stamp, A.; Zah, R.; Althaus, H.-J. Contribution of Li-ion batteries to the environmental impact of electric vehicles. Environ. Sci. Technol. 2010, 44, 6550−6556. (39) Rydh, C.; Sanden, B. A. Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Convers. Manage. 2005, 46, 1957−1979. (40) Ellingsen, L.-w.; Majeau-bettez, G.; Singh, B.; Srivastava, A.; Valoen, L.; Stromman, A. Life cycle assessment of a lithium-ion battery vehicle pack. J. Ind. Ecol. 2014, 18, 113−124. (41) Majeau-Bettez, G.; Hawkins, T. R.; Stromman, A. Life cycle environmental assessment of lithium-ion and nickel metal hydride batteries for plug-in hybrid and battery electric vehicles. Environ. Sci. Technol. 2011, 45, 4548−4554. (42) Zackrisson, M.; Avellan, L.; Orlenius, J. Life cycle assessment of lithium-ion batteries for plug-in hybrid electric vehicles−Critical issues. J. Cleaner Prod. 2010, 18, 1519−1529. (43) Posorski, R.; Bussmann, M.; Menke, C. Does the use of Solar Home Systems (SHS) contribute to climate protection? Renewable Energy 2003, 28, 1061−1080.

Solar), Lee Kraemer (First Solar), and Yardi systems for their support, insight, and guidance throughout the project.

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REFERENCES

(1) Mills. Evan. Identifying and reducing the health and safety impacts of fuel-based lighting. Energy Sustainable Dev. 2016, 30, 39− 50. (2) Williams, N. J.; Jaramillo, P.; Taneja, J.; Ustun, T. Enabling private sector investment in microgrid-based rural electrification in developing countries: A review. Renewable Sustainable Energy Rev. 2015, 52, 1268−1281. (3) Mills, E.; Jacobson, A. The Lumina Project; Research Memo, 2007; Vol. 1. (4) Rao, N. D. Kerosene subsidies in India: When energy policy fails as social policy. Energy Sustainable Dev. 2012, 16, 35−43. (5) Barnes, D. F.; Floor, W. M. Rural energy in developing countries: A challenge for economic development. Annual Review of Energy and the Environment. 1996, 21, 497−530. (6) Mills, E. Health impacts of fuel-based lighting. 3rd International Off-Grid Lighting Conference, Dakar, Senegal, 13−15 November 2012; Lighting Africa, 2013. (7) de Koning, H. W.; Smith, K. R.; Last, J. M. Biomass fuel combustion and health. Bull. World Health Org. 1985, 63, 11. (8) Ezzati, M.; Kammen, D. M. Indoor air pollution from biomass combustion and acute respiratory infections in Kenya: an exposureresponse study. Lancet 2001, 358, 619−624. (9) Poverty Reduction: Scaling Up Local Innovations For Transformational Change; United Nations Development Programme (UNDP), One United Nations Plaza: New York, NY, 2011. (10) The Welfare Impact of Rural Electrification: A Reassessment of the Costs and Benefits; World Bank, 2008. (11) Attigah, B.; Mayer-Tasch, L. The Impact of Electricity Access on Economic Development: A Literature Review; Productive Use of Energy (PRODUSE), 2013. (12) Kanagawa, M.; Nakata, T. Assessment of Access to Electricity and the Socio-economic Impacts in Rural Areas of Developing Countries. Energy Policy 2008, 36, 2016. (13) Jacobson, A. Connective power: Solar electrification and social change in Kenya. World Development 2007, 35, 144−162. (14) Kirubi, C.; Kammen, D. M.; Jacobson, A.; Mills, A. CommunityBased Electric Micro-Grids Can Contribute to Rural Development: Evidence from Kenya. World Development. 2009, 37, 1208−1221. (15) Books, buildings, and learning outcomes: An impact evaluation of World Bank support to basic education in Ghana; Independent Evaluation Group. World Bank: Washington, DC, 2004. (16) Deichmann, U.; Meisner, C.; Murray, S.; Wheeler, D. The economics of renewable energy expansion in rural Sub-Saharan Africa. Energy Policy 2011, 39, 215−227. (17) Weber, C. L.; Jaramillo, P.; Marriott, J.; Samaras, C. Life cycle assessment and grid electricity: what do we know and what can we know? Environ. Sci. Technol. 2010, 44, 1895−1901. (18) Lee, K.-M.; Lee, S.-Y.; Hur, T. Life cycle inventory analysis for electricity in Korea. Energy 2004, 29, 87−101. (19) Widiyanto, M.; Kato, N.; Sampattagul. Environmental impacts evaluation of electricity grid mix systems in four selected countries using a life cycle assessment point of view. EcoDesign 3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing 2003 2003, 26−33. (20) Turconi, R.; Simonsen, C.; Byriel, I.; Astrup, T. Life cycle assessment of the Danish electricity distribution network. Int. J. Life Cycle Assess. 2014, 19, 100−108. (21) Jorge, R.; Hawkins, T.; Hertwich, E. Life cycle assessment of electricity transmission and distributionpart 2: transformers and substation equipment. Int. J. Life Cycle Assess. 2012, 17, 184−191. (22) Schnitzer, D.; Lounsbury, D.; Carvallo, J.; Deshmukh, R.; Apt, J.; Kammen, D. M. Micro-grids for rural electrification: A critical review of best practices based on seven case studies; Report of The United Nations Foundation, 2014. I

DOI: 10.1021/acs.est.6b05455 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology (44) Socolof, M. L.; Smith, J.; Cooper, D.; Amarakoon, S. Design for the Environment: Wire and Cable Partnership; Environmental Protection Agency: Washington DC, 2014. (45) ISO 14044:2006 Environmental Management-Life Cycle Assessment-Requirements and guidelines; International Organization for Standardization: Geneva, 2006. (46) Kim, H.; Cha, K.; Fthenakis, V.; Sinha, P.; Hur, T. Life Cycle Assessment of Cadmium Telluride Photovoltaic (CdTe PV) Systems. Sol. Energy 2014, 103, 78−88. (47) 20150807_Series 4v2Module Datasheet; First Solar, Tempe, AZ, 2015. (48) Strevel, N.; Trippel, L.; Kotarba, C.; Khan, I. Improvements in CdTe module reliability and long-term degradation through advances in construction and device innovation. Photovoltaics Int. 2013, 22, 1−8. (49) Sinha, P.; de Wild-Scholten, M. Life Cycle Assessment of Silver Replacement With Copper Based Metallization in Tetrasun PV Modules. In Proceedings of 31st European Photovoltaic Solar Energy Conference and Exhibition, 2015; pp 2424−2429. (50) Rehman, A.; Lee, S. Review of the Potential of the Ni/Cu Plating Technique for Crystalline Silicon Solar Cells. Materials 2014, 7, 1318−1341. (51) Approved Retaining Clips for FS Modules; First Solar, Tempe, AZ, 2015. (52) Product Catalogue Mounting System 34; MP-Tec, Eberswalde, Germany, 2013. (53) Strevel, N.; Trippel, L.; Kotarba, C.; Khan, I. Improvements in CdTe module reliability and long-term degradation through advances in construction and device innovation. Photovoltaics Int. 2013, 22. (54) Zhu, S.-g.; He, W.-z.; Li, G.-m.; Zhou, X.; Zhang, X.-j.; Huang, J.-w. Recovery of Co and Li from spent lithium-ion batteries by combination method of acid leaching and chemical precipitation. Trans. Nonferrous Met. Soc. China 2012, 22, 2274−2281. (55) Warner, J. Handbook of Lithium-Ion Battery Pack Design Chemistry, components, Types and TerminologyIntroduction; Elsevier, 2015. (56) Poullikkas, A. A comparative overview of large-scale battery systems for electricity storage. Renewable Sustainable Energy Rev. 2013, 27, 778. (57) Shuva, M.; Kurny, A. Hydrometallurgical Recovery of Value Metals from Spent Lithium Ion Batteries. Am. J. Mater. Eng. Technol. 2013, 1, 8−12. (58) Wang, H.; Vest, M.; Friedrich, B. Hydrometallurgical processing of Li-Ion battery scrap from electric vehicles. EMC 2011, Aachen, 2011, p 1. (59) Malhotra, A.; Battke, B.; Beuse, M.; Stephan, A.; Schmidt, T. Use cases for stationary battery technologies: A review of the literature and existing projects. Renewable Sustainable Energy Rev. 2016, 56, 705− 721. (60) Dunn, B.; Kamath, H.; Tarascon, J. M. Electrical energy storage for the grid: a battery of choices. Science 2011, 334, 928−935. (61) Kalhammer, F. R.; Kopf, B. M.; Swan, D. H.; Roan, V. P.; Walsh, M. P. Status and prospects for zero emissions vehicle technology; California Air Resources Board: Sacramento, CA, 2007. (62) Chen, W.; Shen, H.; Shu, B.; Qin, H.; Deng, T. Evaluation of Performance Of MPPT Devices In PV Systems With Storage Batteries. Renewable Energy 2007, 32, 1611−1622. (63) Dunlop, J.; Farhi, B. Recommendations for maximizing battery life in photovoltaic systems: a review of lessons learned; Florida Solar Energy Center: Cocoa, FL, 2001. (64) Aranda, E. D.; Galan, J. G.; de Cardona, M. S.; Marquez, J. A. Measuring The I-V Curve Of PV Generators. IEEE Industrial Electronics Magazine 2009, 3, 4. (65) Grzesiak, W. MPPT Solar Charge Controller For High Voltage Thin Film PV Modules. In Proceedings of IEEE 4th World Conference on Photovoltaic Energy Conference, 2006. (66) Morningstar Corporation. TriStar MPPT 600V Installation and Operation Manual. http://www.morningstarcorp.com/wp-content/ uploads/2014/02/600V-TS-MPPT-Operators-Manual.pdf.

(67) Morningstar TS-MPPT-600v specification sheet. http://www. morningstarcorp.com/wp-content/uploads/2014/02/MSC-DataSheet-TS-MPPT-600V-150526-04-MG.pdf. (68) Aurora 4kW Diesel Generator−Technical specifications sheet.http://www.auroragenerators.com/downloads/AGi4PSpecSheet.pdf. (69) Kohler 5.5 kW Diesel Generator−Technical specifications sheet. http://www.kohlerpower.com.sg/onlinecatalog/pdf/KM6M.pdf. (70) FG Wilson 6.8 kW Diesel Generator−Technical specifications sheet. http://s7d2.scene7.com/is/content/Caterpillar/C10545256. (71) Cummins Diesel Generator−Technical specifications sheet. https://coloradostandby.com/media/custom/upload/document_ technical_a-1483.pdf. (72) Lukuyu, J.; Cardell, J. Hybrid Power System Options for OffGrid Rural Electrification in Northern Kenya. Smart Grid Renewable Energy 2014, 5, 89−106. (73) Itron. CENTRON II C1219. https://www.itron.com/na/ technology/product-services-catalog/products/b/8/9/centron-ii (accessed September 2016). (74) Wheatland Tube, 2014. Chain Link Fabric Average Weights. http://www.wheatland.com/images/specs/Chain-link-fabric-weights. pdf (accessed September 2016). (75) Chain Link Fence Manufacturers Institute Product Manual. http://contractors.masterhalco.com/Contract.nsf/CLFMIProdMan. pdf (accessed September 2016). (76) Zeyringer, M.; Pachauri, S.; Schmid, E.; Schmidt, J.; Worrell, E.; Morawetz, U. Analyzing grid extension and stand-alone photovoltaic systems for the cost effective electrification of Kenya. Energy Sustainable Dev. 2015, 25, 75−86. (77) Mwangangi, S.; Njeru, R.; Koech, W.; Ngari, P.; Akinala, J.; Omwega, T.; Shalleh, A. Environmental and Social Impact Assessment Project Report For Proposed Two 250 kW Wind Turbines at Marsabit; Kenya Power and Lighting Company, 2010. (78) NASA Surface Meteorology and Solar Energy. https://eosweb. larc.nasa.gov/sse (accessed February 2016). (79) Republic of Kenya. 10 Year Power Sector Expansion Plan 2014− 2024; 2014. (80) Itten, R.; Frischknecht, R.; Stucki, M. Life Cycle Inventories of Electricity Mixes and Grid; treeze Ltd, Uster, 2014. (81) Bergesen, J.; Heath, G.; Gibon, T.; Suh, S. Thin-film photovoltaic power generation offers decreasing greenhouse gas emissions and increasing environmental co-benefits in the long term. Environ. Sci. Technol. 2014, 48, 9834. (82) Fisher, K.; Wallen, E.; Laenen, P.; Collins, M. Battery waste management life cycle assessment; Environmental Resources Management, 2006. (83) Goedkoop, M.; Heijungs, R.; Huijbregts, M.; de Schryver, A.; van Zelm, R. ReCiPe 2008: A Life Cycle Impact Assessment Method Which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level; 2009. (84) Jolliet, O.; Saade-Sbeih, M.; Shaked, S.; Jolliet, A.; Crettaz, P. Chapter 6: Interpretation. In Environmental Life Cycle Assessment; CRC Press, 2015; pp 149−197. (85) Kang, D.; Chen, M.; Ogunseitan, O. A. Potential Environmental And Human Health Impacts Of Rechargeable Lithium Batteries In Electronic Waste. Environ. Sci. Technol. 2013, 47, 5495−5503. (86) Simon, B.; Weil, M. Analysis of materials and energy flows of different lithium ion traction batteries. Revue de Métallurgie 2013, 110, 65−76. (87) Bakhiyi, B.; Labreche, F.; Zayed, J. The photovoltaic industry on the path to a sustainable future  Environmental and occupational health issues. Environ. Int. 2014, 73, 224−234.

J

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