PV in the circular economy, a dynamic framework analyzing technology evolution and reliability impacts

Abstract

Rapid, terawatt-scale deployment of photovoltaic (PV) modules is required to decarbonize the energy sector. Despite efficiency and manufacturing improvements, material demand will increase, eventually resulting in waste as deployed modules reach end of life. Circular choices for decommissioned modules could reduce waste and offset virgin materials. We present PV ICE, an open-source python framework using modern reliability data, which tracks module material flows throughout PV life cycles. We provide dynamic baselines capturing PV module and material evolution. PV ICE includes multimodal end of life, circular pathways, and manufacturing losses. We present a validation of the framework and a sensitivity analysis. Results show that manufacturing efficiencies strongly affect material demand, representing >20% of the 9 million tons of waste cumulatively expected by 2050. Reliability and circular pathways represent the best opportunities to reduce waste by 56% while maintaining installed capacity. Shorter-lived modules generate 81% more waste and reduce 2050 capacity by 6%.

Keywords: Energy policy; Energy systems; Environmental science.

Graphical abstract

Figure 1

PV module technology has evolved rapidly. This image shows what an "average" module looked like 15 years ago (left) versus current technology (right). Power production per meter square has also improved due to efficiency improvements and other levers such as bifaciality (Fischer et al., 2020).

Figure 2

Megawatts of PV added in the United States per year, according to different sources of PV installation data. Please note that the scale is logarithmic, and the source data include all PV technologies. Sources: Morse, 1995; Bolcar and Ardani, 2010; Kann et al., 2013; Bolinger et al., 2019; Author Anonymous, 2020b; IRENA RE Time Series. The black dashed line shows the silicon market share weighted installations.

Figure 3

The market share of mono-Si technologies, from Costello and Rappaport, 1980; Maycock and Box, 1993; Maycock, 2003, ; Barbose and Darghouth, 2019; Mints, 2019; Fischer et al., 2020. Market shares of mc-Si (not shown) are the complement of these values. The dashed black line is the average of the sources.

Figure 4

Average module efficiency over time, from Nemet, 2006; Fischer, 2019; Fischer et al., 2020; Oberbeck et al., 2020; Wilson et al., 2020; Author Anonymous, 2021 and a proposed interpolated baseline in black.

Figure 5

Annual module composition by mass percentage, as calculated from the material baselines presented here. 2030 values are assumed to be constant through 2050.

Figure 6

Comparison of mass-power factors used in waste projection literature and a dynamic mass-power factor calculated from the module and material baselines presented here. The static mass-power factors are shown as a flat line for all the years considered in the study. The proposed baseline (black) only considers c-Si technology (excluding thin films) and incorporates granular module and material technology evolutions, whereas IRENA 2016 (Weckend et al., 2016) attempts to capture technology improvement trends using a best-fit curve.

Figure 7

Mass-flow diagram showing PV life cycle stages, processes, and decisions as represented in the PV ICE tool. Arrows represent the mass flow, circles represent process efficiencies, hexagons denote decision points, and rhombuses are final dispositions. The background colors show whether the variable is controlled by the material or module baselines. Dotted lines provide a visualization of the different life cycle stages.

Figure 8

Virgin material efficiencies and material manufacturing efficiencies Material properties of (A) virgin material extraction efficiency and (B) material use efficiency during PV manufacturing. 2030 values are held constant through 2050 for a conservative estimate.

Figure 9

Distributed and utility-scale PV annual installations and cumulative installed capacity in the United States through 2050 for the reference and high electrification scenarios as predicted by Murphy et al. (2021). The annual installations (bars) are modified by the market share of silicon technology and the DC/AC ratios, then used as input to the PV ICE tool. The shaded area shows the cumulative capacity resulting from these annual installations. The annual deployment data jumps are due to the ReEDS modeling process, and these jumps are found in other modeling projections.

Figure 10

Yearly and cumulative material needs and lifecycle wastes by scenario A series of three paired graphs, showing (A) the annual and cumulative virgin material needs, (B) the annual and cumulative EoL materials, and (C) the annual and cumulative manufacturing scrap, compared across four scenarios. The scenarios compared are the PV ICE reliability approach and baselines using the Electrification Futures ref and h.e. electrification installation projections and the IRENA early loss and regular-loss reliability approaches using the Electrification Futures h.e. installation projection. The comparison demonstrates that PV ICE's lifetime methods predict less EoL waste while forecasting a larger quantity of manufacturing scrap.

Figure 11

Comparison of predicted installed capacity in 2030 and 2050 across the three PV IC simulations and against literature values (Weckend et al., 2016; CSA Group, 2020). The solid bars represent the reference scenario, whereas the hashed bars represent the high electrification scenario. This comparison demonstrates that PV ICE's lifetime and reliability data predict significantly more active capacity in the field in 2050 than prior methodologies. This increase in active capacity is also present to a lesser extent in 2030.

Figure 12

Comparison of cumulative EoL material, each decade through 2050, for the scenarios modeled in PV ICE. Note the logarithmic scale on the y axis. Literature results have been included here as a comparison, demonstrating that prior EoL material predictions have more material reaching EoL earlier than PV ICE.

Figure 13

The percent improvement—i.e., decrease in virgin material needs—of the most impactful parameters affecting virgin material demand in the PV ICE tool. A decrease in material demands (larger bars to the left) is considered beneficial. The 10% columns indicate in what direction and magnitude the parameter, listed on the left, was varied (with the caveat of maintaining 0% and 100% boundaries).

Figure 14

The percent improvement (i.e., decrease) in life cycle waste from varying the most impactful parameters affecting waste in the PV ICE tool. A decrease (larger bars to the left) in life cycle waste is considered beneficial. The 10% columns indicate in what direction and magnitude the parameter, listed at the left, was varied.