## input data
file_profiles = join(mypath, 'load_profiles_DK.csv')
file_spot_prices = join(mypath, 'elspotprices.csv')
## output files for results
file_normed_assets = join(mypath, 'normed_assets.json')
file_scaling_assets = join(mypath, 'scaling_assets.json')
file_vol_matching_assets = join(mypath, 'vol_matching_assets.json')
file_timegrid = join(mypath, 'timegrid.json')
file_prices = join(mypath, 'prices.json')
############################################### create helpers
print('create timegrid and nodes')
timegrid = Timegrid(start = Start, end = End, freq = freq, main_time_unit= 'h')
to_json(timegrid, file_timegrid)
node_green = Node('green sources', commodity= 'power', unit = Unit('MWh', 'MW', 1.))
node_grey = Node('grey sources', commodity= 'power', unit = Unit('MWh', 'MW', 1.))
node_load = Node('client', commodity= 'power', unit = Unit('MWh', 'MW', 1.))
############################################### import data
print('import load profiles and prices')
df_profiles = pd.read_csv(file_profiles)
df_profiles.index = pd.to_datetime(df_profiles['HourUTC'], format='%Y-%m-%dT%H')
df_profiles.sort_index(inplace = True)
# filter for only DK1 price area and time grid
df_profiles = df_profiles.loc[df_profiles['PriceArea'] == 'DK1', ['OnshoreWindPower', 'OffshoreWindPower', 'SolarPower', 'TotalLoad']]
df_profiles = df_profiles.loc[Start:End]
# omit last
df_profiles = df_profiles.iloc[:-1]
# import prices
def test_serialize_portfolio(self):
""" Unit test serialize portfolio
"""
node1 = eao.assets.Node('node_1')
node2 = eao.assets.Node('node_2')
timegrid = eao.assets.Timegrid(dt.date(2021, 1, 1),
dt.date(2021, 2, 1),
freq='d')
a1 = eao.assets.SimpleContract(name='SC_1',
price='rand_price_1',
nodes=node1,
min_cap=-20.,
max_cap=20.,
start=dt.date(2021, 1, 10),
end=dt.date(2021, 1, 20))
#a1.set_timegrid(timegrid)
a2 = eao.assets.SimpleContract(name='SC_2',
price='rand_price_1',
nodes=node1,
min_cap=-5.,
max_cap=10.,
extra_costs=1.)
#a2.set_timegrid(timegrid)
a3 = eao.assets.SimpleContract(name='SC_3',
price='rand_price_2',
nodes=node2,
min_cap=-10.,
max_cap=10.,
extra_costs=1.,
start=dt.date(2021, 1, 10),
end=dt.date(2021, 1, 25))
a4 = eao.assets.Transport(name='Tr',
costs_const=1.,
nodes=[node1, node2],
min_cap=0.,
max_cap=1.)
a5 = eao.assets.Storage('storage', nodes = node1, \
start=dt.date(2021,1,1), end=dt.date(2021,2,1),size=10, \
cap_in=1.0/24.0, cap_out=1.0/24.0, start_level=5, end_level=5)
#a3.set_timegrid(timegrid)
prices = {
'rand_price_1': np.ones(timegrid.T) * 1.,
'rand_price_2': np.ones(timegrid.T) * 10.,
'rand_price_3': np.random.randn(timegrid.T) * 10.
}
portf = eao.portfolio.Portfolio([a1, a2, a3, a4, a5])
portf.set_timegrid(timegrid)
mys = json.dumps(portf,
indent=4,
default=eao.serialization.json_serialize_objects)
aa = json.loads(mys,
object_hook=eao.serialization.json_deserialize_objects)
# file activity
myf = 'test_portf.json'
to_json(portf, myf)
x = load_from_json(file_name=myf)
xx = load_from_json(mys)
run_from_json(file_name_in=myf,
prices=prices,
file_name_out='test_results.xlsx')
run_from_json(file_name_in = myf, prices = prices, file_name_out = 'test_results.csv',\
format_out='csv', csv_ger= True)
res = run_from_json(file_name_in=myf, prices=prices)
# array
mys = json.dumps(prices, indent=4, default=json_serialize_objects)
pp = json.loads(mys, object_hook=json_deserialize_objects)
check = True
for pr in prices:
check = check and all(prices[pr] == pp[pr])
return check
file_profiles = join(mypath, 'load_profiles_DK.csv')
file_spot_prices = join(mypath, 'elspotprices.csv')
## output files for results
file_normed_assets = join(mypath, 'normed_assets.json')
file_scaling_assets = join(mypath, 'scaling_assets.json')
file_vol_matching_assets = join(mypath, 'vol_matching_assets.json')
file_timegrid = join(mypath, 'timegrid.json')
file_prices = join(mypath, 'prices.json')
file_results = join(mypath, 'loop_results')
############################################### create helpers
print('create timegrid and nodes')
timegrid = Timegrid(start=Start, end=End, freq=freq, main_time_unit='h')
to_json(timegrid, file_timegrid)
node_green = Node('green sources',
commodity='power',
unit=Unit('MWh', 'MW', 1.))
node_grey = Node('grey sources', commodity='power', unit=Unit('MWh', 'MW', 1.))
node_load = Node('client', commodity='power', unit=Unit('MWh', 'MW', 1.))
############################################### import data
print('import load profiles and prices')
df_profiles = pd.read_csv(file_profiles)
df_profiles.index = pd.to_datetime(df_profiles['HourUTC'],
format='%Y-%m-%dT%H')
df_profiles.sort_index(inplace=True)
# filter for only DK1 price area and time grid
df_profiles = df_profiles.loc[
df_profiles['PriceArea'] == 'DK1',