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update pypsa-earth.config to fit the updated submodule
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hazemakhalek committed Mar 4, 2024
1 parent b0be70c commit 0e50a09
Showing 1 changed file with 34 additions and 15 deletions.
49 changes: 34 additions & 15 deletions config.pypsa-earth.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ enable:
retrieve_databundle: true # Recommended 'true', for the first run. Otherwise data might be missing.
retrieve_cost_data: true # true: retrieves cost data from technology data and saves in resources/costs.csv, false: uses cost data in data/costs.csv
download_osm_data: true # If 'true', OpenStreetMap data will be downloaded for the above given countries
build_natura_raster: true # If True, than an exclusion raster will be build
build_natura_raster: false # If True, than an exclusion raster will be build
build_cutout: false
# If "build_cutout" : true, then environmental data is extracted according to `snapshots` date range and `countries`
# requires cds API key https://cds.climate.copernicus.eu/api-how-to
Expand Down Expand Up @@ -90,7 +90,6 @@ build_shape_options:
out_logging: true # When true, logging is printed to console
year: 2020 # reference year used to derive shapes, info on population and info on GDP
nprocesses: 3 # number of processes to be used in build_shapes
nchunks: 3 # number of data chuncks for build_shapes. If not specified or smaller than nprocesses, specified as nprocesses
worldpop_method: "standard" # "standard" pulls from web 1kmx1km raster, "api" pulls from API 100mx100m raster, false (not "false") no pop addition to shape which is useful when generating only cutout
gdp_method: "standard" # "standard" pulls from web 1x1km raster, false (not "false") no gdp addition to shape which useful when generating only cutout
contended_flag: "set_by_country" # "set_by_country" assigns the contended areas to the countries according to the GADM database, "drop" drops these contended areas from the model
Expand Down Expand Up @@ -292,9 +291,10 @@ renewable:
extendable: true
hydro:
cutout: cutout-2013-era5
hydrobasins_level: 6
resource:
method: hydro
hydrobasins: data/hydrobasins/hybas_world_lev06_v1c.shp
hydrobasins: data/hydrobasins/hybas_world.shp
flowspeed: 1.0 # m/s
# weight_with_height: false
# show_progress: true
Expand Down Expand Up @@ -324,7 +324,7 @@ costs:
investment: 0
lifetime: 25
CO2 intensity: 0
discount rate: 0.15
discount rate: 0.071
marginal_cost: # EUR/MWh
solar: 0.01
onwind: 0.015
Expand All @@ -340,18 +340,37 @@ costs:


monte_carlo:
# Description: Specify Monte Carlo sampling options for uncertainty analysis.
# Define the option list for Monte Carlo sampling.
# Make sure add_to_snakefile is set to true to enable Monte-Carlo
options:
add_to_snakefile: false
samples: 7 # number of optimizations
sampling_strategy: "chaospy" # "pydoe2", "chaospy", "scipy", packages that are supported
pypsa_standard:
# User can add here flexibly more features for the Monte-Carlo sampling.
# Given as "key: value" format
# Key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering!
# Value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object
loads_t.p_set: [0.9, 1.1]
# generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]: [0.9, 1.1]
# generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]: [0.9, 1.1]
add_to_snakefile: false # When set to true, enables Monte Carlo sampling
samples: 9 # number of optimizations. Note that number of samples when using scipy has to be the square of a prime number
sampling_strategy: "chaospy" # "pydoe2", "chaospy", "scipy", packages that are supported
seed: 42 # set seedling for reproducibilty
# Uncertanties on any PyPSA object are specified by declaring the specific PyPSA object under the key 'uncertainties'.
# For each PyPSA object, the 'type' and 'args' keys represent the type of distribution and its argument, respectively.
# Supported distributions types are uniform, normal, lognormal, triangle, beta and gamma.
# The arguments of the distribution are passed using the key 'args' as follows, tailored by distribution type
# normal: [mean, std], lognormal: [mean, std], uniform: [lower_bound, upper_bound],
# triangle: [mid_point (between 0 - 1)], beta: [alpha, beta], gamma: [shape, scale]
# More info on the distributions are documented in the Chaospy reference guide...
# https://chaospy.readthedocs.io/en/master/reference/distribution/index.html
# An abstract example is as follows:
# {pypsa network object, e.g. "loads_t.p_set"}:
# type: {any supported distribution among the previous: "uniform", "normal", ...}
# args: {arguments passed as a list depending on the distribution, see the above and more at https://pypsa.readthedocs.io/}
uncertainties:
loads_t.p_set:
type: uniform
args: [0, 1]
generators_t.p_max_pu.loc[:, n.generators.carrier == "onwind"]:
type: lognormal
args: [1.5]
generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]:
type: beta
args: [0.5, 2]



solving:
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