Comments (6)
FabianHofmann
commented on August 25, 2024
Hey @loongmxbt , the broadcasting works automatically in linopy, so no need for loops or extra broadcasting. I am not sure if df_units_uc.index is now a subset of df_units.index?
Either way, what you wrote in the last section should work if the dimension names are set correctly(!). What I suggest to you is that you explicitly set the names of your axes in the dataframe before defining the variables and constraints. Something like
df_time.index.name = 'time'
df_units.index.name = 'units'
bx = m.add_variables(binary=True, coords=[df_time.index, df_units.index], name='bx' )
unit_power = m.add_variables(lower=0, coords=[df_time.index, df_units.index], name='unit_power' )
m.add_constraints(bx*unit_power >= df_units['p_min'], name='min_power_constraint')
m.add_constraints(unit_power * 1 <= df_units['p_max'], name='max_power_constraint')(Assuming now, that df_units equals to df_units_uc)
Hope that helps!
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loongmxbt
commented on August 25, 2024
Thanks for your answer. I modified all "df_units_uc" into "dict_units", also the related data, and the model works fine. Seems an error in my data.
I'll check when I continue to write the model.
from linopy import Model
import pandas as pd
dict_bus_predict = {
't01': { 'B01': 420, 'B02': 530, 'B03': 670 },
't02': { 'B01': 520, 'B02': 580, 'B03': 770 },
't03': { 'B01': 620, 'B02': 570, 'B03': 800 }
}
dict_re_predict = {
't01': { 'PV01': 30, 'WD01': 30, },
't02': { 'PV01': 22, 'WD01': 80, },
't03': { 'PV01': 24, 'WD01': 70, }
}
dict_unit = {
'G01a': { 'is_uc': 1, 'bus': 'B01', 'p_min': 100, 'p_max': 1000, 'init_power':320, 'init_status': -3, 'ramp_limit_up': 260, 'ramp_limit_down': 260, 'cost_a': 0.002, 'cost_b': 0.21, 'cost_c': 21, 'cost_p': 800, 'hot_start_cost': 100, 'cold_start_cost': 150, 'no_load_cost': 100, 'shut_down_cost': 120, 'cold_start_hours': 3 },
'G01b': { 'is_uc': 1, 'bus': 'B01', 'p_min': 100, 'p_max': 1000, 'init_power':320, 'init_status': -6, 'ramp_limit_up': 260, 'ramp_limit_down': 260, 'cost_a': 0.002, 'cost_b': 0.21, 'cost_c': 21, 'cost_p': 800, 'hot_start_cost': 100, 'cold_start_cost': 150, 'no_load_cost': 100, 'shut_down_cost': 120, 'cold_start_hours': 3 },
'G02': { 'is_uc': 1, 'bus': 'B02', 'p_min': 100, 'p_max': 1000, 'init_power':320, 'init_status': 0, 'ramp_limit_up': 260, 'ramp_limit_down': 260, 'cost_a': 0.0015, 'cost_b': 0.2, 'cost_c': 21, 'cost_p': 800, 'hot_start_cost': 100, 'cold_start_cost': 150, 'no_load_cost': 100, 'shut_down_cost': 120, 'cold_start_hours': 3 },
'G03': { 'is_uc': 1, 'bus': 'B03', 'p_min': 100, 'p_max': 1000, 'init_power':320, 'init_status': 0, 'ramp_limit_up': 260, 'ramp_limit_down': 260, 'cost_a': 0.0025, 'cost_b': 0.2, 'cost_c': 21, 'cost_p': 800, 'hot_start_cost': 100, 'cold_start_cost': 150, 'no_load_cost': 100, 'shut_down_cost': 120, 'cold_start_hours': 3 },
}
df_units = pd.DataFrame.from_dict(dict_unit, orient='index')
print(df_units)
df_units_uc = df_units[df_units["is_uc"] == 1]
df_bus_predict = pd.DataFrame.from_dict(dict_bus_predict, orient='index')
print(df_bus_predict)
def main():
m = Model()
unit_power = m.add_variables(lower=0, coords=[df_bus_predict.index, df_units_uc.index], name='unit_power' )
bx = m.add_variables(binary=True, coords=[df_bus_predict.index, df_units_uc.index], name='bx' )
by = m.add_variables(binary=True, coords=[df_bus_predict.index, df_units_uc.index], name='by' )
bz = m.add_variables(binary=True, coords=[df_bus_predict.index, df_units_uc.index], name='bz' )
m.add_constraints(bx * unit_power >= df_units_uc['p_min'], name='min_power_constraint')
m.add_constraints(unit_power * 1 <= df_units_uc['p_max'], name='max_power_constraint')
print(m)
if __name__ == '__main__':
main()
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joga4er
commented on August 25, 2024
I have a similar problem; I have one pandas dataframe and one pandas series:
#storage dataframe
storage_df = pd.DataFrame( { 'name': [ '422', '435' ], 'pmin': [ 5, 2.5 ], 'pmax': [ 8, 7.5 ], 'Tmin': [ 49, 48.1 ], 'Tmax': [ 59.8, 54.9 ] } )
storage_df.index.name = 'storages'
#time basis vector
dimtime = 24 * 4
vectime = pd.Series( np.arange( dimtime ) )
vectime.index.name = 'time'
I want to create a variable in both dimensions:
m = opt.Model()
#variable
content = m.add_variables( coords = [ storage_df.index, vectime.index ], name = 'content' )
and set constraints in one dimension:
#constraint
m.add_constraints( content >= storage_df[ 'Tmin' ], name = 'content_low' )
I get the following error message (which makes sense):
ValueError: operands could not be broadcast together with shapes (2,) (2,96)
So my question is how to implement such constraints efficiently?
from linopy.
joga4er
commented on August 25, 2024
To clarify the imports:
import linopy as opt
import numpy as np
import pandas as pd
from linopy.
joga4er
commented on August 25, 2024
Sorry for spaming this issue:
The solution is to multiply the variable with one:
#constraint
m.add_constraints( content * 1 >= storage_df[ 'Tmin' ], name = 'content_low' )
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FabianHofmann
commented on August 25, 2024
Good that you found a solution. I will update the docs soon on how to work with pandas objects and different approaches to create constraints. But note, always watch out for the names of the dimensions (that the most "tricky" thing)
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