parasol-lca-openpv 1.2.dev1

Implement parasol LCA database

Parasol LCA

Overview

Parasol LCA is a Life Cycle Assessment (LCA) parametrized model of crystalline silicon-based photovoltaic (PV) systems. It relies on the parametrization of existing life cycle inventory (LCI) data to better account for the progress already accomplished by the PV industry.

It was developed by Romain BESSEAU in 2019 for his PhD Thesis. The first publication of this model as a Jupyter Notebook and the results of the corresponding study can be found in the article by Besseau et al. (2023).

Its development is based on the Brightway2 and lca_algebraic libraries.

This python package makes Parasol LCA available as a ready-to-use module.

Installation

Parasol LCA has been deployed to the pypi index and can be installed using the following command:

pip install parasol-lca

The module adds all the dependencies required to run the parameterized model.

How to use Parasol LCA

The parameterized LCA model enables the assessment of the life cycle environmental impacts of a crystalline silicon-based PV system defined according to 35 input parameters.

To use the parameterized model, you may use the following code. Note that the input_parameters are meant to be changed depending on the parameters of the PV system to be evaluated. If no parameters are added to input_parameters, the model will use the default values.

NOTE: Parasol LCA is compatible with the background database Ecoinvent 3.7, and has also been tested with Ecoinvent 3.9, 3.10, and 3.11. Results are, however, not the same for all database versions. The results shown in the Parasol LCA article were produced with Ecoinvent 3.7.

import brightway2 as bw
import parasol_lca
import lca_algebraic as agb
agb.Settings.units_enabled = True

bw.projects.set_current("parasol-project")

#choose the corresponding ecoinvent release
EI_VERSION= "3.11"

import bw2io
bw2io.ecoinvent.import_ecoinvent_release(EI_VERSION, "cutoff", "your_ecoinvent_username", "your_ecoinvent_password")

MYDB = "parasol"
agb.resetDb(MYDB)
agb.resetParams(MYDB)
agb.setForeground(MYDB)

parasol_lca.create({
    "target_database": MYDB,
    "version":EI_VERSION,
    "biosphere":f"ecoinvent-{EI_VERSION}-biosphere",
    "technosphere":f"ecoinvent-{EI_VERSION}-cutoff"
})

# Select impact/activity
activity = agb.findActivity('[parasol] PV impact per kWh', db_name=MYDB, single=True)

# NOTE: LCA results can also be evaluated per kWp :
# activity = agb.findActivity('[parasol] PV impact per kWp', db_name=MYDB, single=True)

# Default parameters
input_parameters = dict(
    #Installation
    normalised_annual_PV_production_kWh_per_kWp = 1300,
    power_plant_installed_capacity = 3.0,
    pv_module_efficiency = 0.20, 
    has_bifacial_modules = False,
    aluminium_frame_surfacic_weight = 1.5,
    power_plant_lifetime = 30,
    electrical_installation_specific_weight = 3, 
    module_pv_technology = 'multiSi',

    #Inverter
    inverter_weight_per_kW = 2, 
    inverter_lifetime = 15,
    inverter_installed_capacity_ratio = 1,

    #Manufacturing
    manufacturing_electricity_mix = 'CN',
    electricity_mix_CO2_content = 0.5,
    manufacturing_efficiency_gains = 0,
    kerf_loss = 0.3,
    wafer_thickness = 160,
    silver_content = 9.6,
    silicon_production_electricity_intensity = 30,
    silicon_production_heat_intensity = 185, 
    silicon_casting_electricity_intensity = 15,
    sic_recycled_share = 0,
    diamond_wiring = 1,
    glass_thickness = 4,

    #Mounting system
    roof_vs_ground_ratio = 1,
    mounting_system_weight_alu = 1.5,
    mounting_system_weight_total = 5,
    mounting_system_weight_wood = 0,
    ground_coverage_ratio = 0.4,
    mounting_system_type = "ground",

    #Transport
    transport_distance_lorry = 1000,
    transport_distance_train = 500,
    transport_distance_boat = 4000,

    #Recycling
    recycling_rate = 0.9,
    recycling_rate_Al = 0.96,
    recycling_rate_Cu = 0.75,
    recycling_rate_glass = 0.9,
    electricity_consumption_for_recycling = 50,
    heat_consumption_for_recycling = 76
)

# Choose the LCIA methods from list(bw.methods) that you wish to evaluate
# In the original article, the LCIA methods used were ILCD 2018
# ILCD now recommends using the Environmental Footprint (EF) methods

# Example: 3 impact categories with ecoinvent 3.11
methods = [('EF v3.1', 'climate change', 'global warming potential (GWP100)'),
           ('EF v3.1', 'material resources: metals/minerals', 'abiotic depletion potential (ADP): elements (ultimate reserves)'),
           ('EF v3.1', 'land use','soil quality index')]

data = agb.compute_impacts(models = { activity: 1.0 }, methods=methods, **input_parameters)
#print data to show the LCA results
data.T