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This open-source tool explores the effects of Circular Economy (CE) pathways for photovoltaic (PV) materials. It can be used to quantify and assign a value framework to CE efforts including re-design, reduction, replacement, reuse, recycling, and lifetime and reliability improvements across the PV value chain. PV ICE enables tradeoff analysis through scenario comparisons, and is highly customizable through user inputs such as deployment schedules, module properties and component materials, and CE pathways.
The provided PV ICE module and material baselines leverage published data from many sources on PV manufacturing and predicted technological changes. Input data are being compiled here and the baselines are available here for use in other projects as well as for the PV ICE tool.
This section provides a brief description of how the PV ICE tool works. FULL DOCUMENTATION CAN BE FOUND AT readthedocs.
PV ICE is a dynamic mass flow based tool. It takes in any deployment forecast of any evolving module design along with it's component materials and uses sophisticated lifetime and reliability parameters to calculate effective capacity, virgin material demand, and life cycle wastes. The calculator captures all the mass flows shown in the simplified diagram below for all years studied in a simulation (ex: 2020-2050).
Annually deployed cohorts of modules are tracked through the simulation, subjected to lifetime, degradation, and reliability parameters, and guided along user defined CE pathways (ex: resell, recycling). The PV ICE framework is designed for scenario comparisons (ex: different deployment schedules, module designs, or circular pathways) and is capable of both geospatial and temporal analysis (i.e. when and where materials will be demanded or are available).
Module and material properties are known to be variable with time, and PV ICE can capture this dynamic evolution of PV technology. Dynamic baseline inputs for crystalline silicon PV modules and component materials are provided in the PV_ICE \ baselines folder. These baselines are dervied from literature and report data. Module baselines capture the annual average crystalline silicon module (i.e. a market share weighted average of the silicon PV technologies deployed). Each material similarly is a market share weighted average of silicon PV technologies, compiled from multiple sources, most notably consistent with ITRPV data. Please see the Jupyter Journals (tutorials \ baseline development documentation) for the derivations and sources (baselines \ SupportingMaterials) of the provided c-Si baselines. Alternate module and material files can be created by the user, and an expanded set of PV technology baselines is planned for the future, including CdTe and perovskites.
The energy balance of renewable energy technologies is as important and the mass balance when evaluating sustainability. Additionally, few studies of Circular Economy (CE) pathways consider the energy return on investment of a particular pathway. PV ICE energy flows fill this analysis gap, and provide useful insights into the potential tradeoffs between mass and energy of CE pathways.
The energy flows of PV ICE are based on the mass flows. These energy flows, like the mass flows, are dynamic with time and are seperated into module and material energies. For each supply chain process step captured in the mass flows, an energy per module area or energy per material mass is captured as an input (ex: module manufacturing energy, energy to manufacture rolled glass from silica sand, energy to crush a module for recycling ). The energy demanded for each step is the sum of all electrical energy demands and all fuel/heating energy demands.
We provide an energy baseline for crystalline silicon modules and component materials. Data for these baselines is being compiled from literature and report data. For the complete derivation of the energy demands for crystalline silicon modules and materials, please see the Jupyter Journals (tutorials \ baseline development documentation) and (baselines \ SupportingMaterials). Alternate module and material files can be created by the user, and an expanded set of PV technology baselines is planned for the future, including CdTe and perovskites.
After running a mass flow simulation, an energy flow calculation can be run which will multiply the energy demands by the mass flows and calculate annual generation from the deployed modules. Results of this calculation provide annual, cumulative, and lifetime energy demands and energy generated. These values can be used to calculate energy balance metrics such as energy return on investment (EROI), net energy, and energy payback time (EPBT). These features are actively under development, so check back for updates soon!
PV ICE releases may be installed using the pip and conda tools.
Please see the Installation page of the documentation for complete instructions.
PV ICE is compatible with Python 3.5 and above.
Install with:
pip install PV_ICE
For developer installation, download the repository, navigate to the folder location and install as:
pip install -e .
After you have installed PV ICE, we recommend heading over to our tutorials jupyter journals (PV ICE \ docs \ tutorials). There you will find journals "0 - quick start Example" and "1 - Beginner Example" which can help guide you through your first simulation using the PV ICE provided crystalline silicon PV baselines. In journals 2-4 we walk you through modifications to the basic simulation, including modifying parameters with PV ICE functions to suit your analysis needs.
PV ICE has been used in a variety of published analyses, including:
High Impact Report: The Solar Futures Report and Circular Economy Technical Report
Ardani, Kristen, Paul Denholm, Trieu Mai, Robert Margolis,
Eric O’Shaughnessy, Timothy Silverman, and Jarett Zuboy. 2021.
“Solar Futures Study.” EERE DOE.
https://www.energy.gov/eere/solar/solar-futures-study.
Heath, Garvin, Dwarakanath Ravikumar, Silvana Ovaitt,
Leroy Walston, Taylor Curtis, Dev Millstein, Heather Mirletz,
Heidi Hartman, and James McCall. 2022.
“Environmental and Circular Economy Implications of Solar Energy
in a Decarbonized U.S. Grid.” NREL/TP-6A20-80818. NREL.
Peer Reviewed Journals
H. Mirletz, S. Ovaitt, S. Sridhar, and T. M. Barnes. 2022.
“Circular Economy Priorities for Photovoltaics in the Energy Transition.”
PLOS ONE 17 (9): e0274351. https://doi.org/10.1371/journal.pone.0274351.
S. Ovaitt & H. Mirletz, S. Seetharaman, and T. Barnes,
“PV in the Circular Economy, A Dynamic Framework Analyzing
Technology Evolution and Reliability Impacts,”
ISCIENCE, Jan. 2022, doi: https://doi.org/10.1016/j.isci.2021.103488.
There are other multiple publications citing PV ICE like PVSC, PVRW, etc. Please see the list in the readthedocs documentation.
We need your help to make PV ICE a great tool! Please see the Contributing page for more on how you can contribute. The long-term success of PV ICE requires substantial community support.
PV_ICE open-source code is copyrighted by the Alliance for Sustainable Energy and licensed with BSD-3-Clause terms, found here.
If you suspect that you may have discovered a bug or if you'd like to change something about CF-MFA, then please make an issue on our GitHub issues page.
If you use PV_ICE in a published work, please cite:
S. Ovaitt & H. Mirletz, S. Seetharaman, and T. Barnes,
“PV in the Circular Economy, A Dynamic Framework Analyzing
Technology Evolution and Reliability Impacts,”
ISCIENCE, Jan. 2022, doi: https://doi.org/10.1016/j.isci.2021.103488.
and also please also cite the DOI corresponding to the specific version of PV_ICE that you used. PV_ICE DOIs are listed at Zenodo.org. For example for version 0.3.2:
S. Ovaitt, H. Mirletz, M. Mendez Ribo (2023).
NREL/PV_ICE: v0.3.2 Release. Zenodo.
https://doi.org/10.5281/zenodo.7651576
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