def columnise_energy_prices(df, island_split=True):
"""
Columnise the energy price dataframe
"""
# Separate the columns
hay = df.eq_mask("Bus Id", "HAY2201")
ben = df.eq_mask("Bus Id", "BEN2201")
sfd = df.eq_mask("Bus Id", "SFD2201")
hly = df.eq_mask("Bus Id", "HLY2201")
ota = df.eq_mask("Bus Id", "OTA2201")
# Rename the columns
hay = hay.rename(columns={"Price Sum": "HAY2201 Price"})
ben = ben.rename(columns={"Price Sum": "BEN2201 Price"})
sfd = sfd.rename(columns={"Price Sum": "SFD2201 Price"})
hly = hly.rename(columns={"Price Sum": "HLY2201 Price"})
ota = ota.rename(columns={"Price Sum": "OTA2201 Price"})
# Remerge them
mdf = nzem.merge_series(hay["HAY2201 Price"], ben["BEN2201 Price"])
mdf = nzem.merge_dfseries(mdf, sfd["SFD2201 Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, hly["HLY2201 Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, ota["OTA2201 Price"], left_index=True)
if island_split:
mdf["Island Price Split"] = mdf["HAY2201 Price"] - mdf["BEN2201 Price"]
return mdf
def columnise_energy_prices(df, island_split=True):
"""
Columnise the energy price dataframe
"""
# Separate the columns
hay = df.eq_mask("Bus Id", "HAY2201")
ben = df.eq_mask("Bus Id", "BEN2201")
sfd = df.eq_mask("Bus Id", "SFD2201")
hly = df.eq_mask("Bus Id", "HLY2201")
ota = df.eq_mask("Bus Id", "OTA2201")
# Rename the columns
hay = hay.rename(columns={"Price Sum": "HAY2201 Price"})
ben = ben.rename(columns={"Price Sum": "BEN2201 Price"})
sfd = sfd.rename(columns={"Price Sum": "SFD2201 Price"})
hly = hly.rename(columns={"Price Sum": "HLY2201 Price"})
ota = ota.rename(columns={"Price Sum": "OTA2201 Price"})
# Remerge them
mdf = nzem.merge_series(hay["HAY2201 Price"], ben["BEN2201 Price"])
mdf = nzem.merge_dfseries(mdf, sfd["SFD2201 Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, hly["HLY2201 Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, ota["OTA2201 Price"], left_index=True)
if island_split:
mdf["Island Price Split"] = mdf["HAY2201 Price"] - mdf["BEN2201 Price"]
return mdf
def load_masterset():
""" Load a master dataset will return a merged DataFrame containing the
following information time alligned:
1. Energy Prices (Five Nodes)
2. Reserve Prices (Two Region, Two Type)
3. Hydro Lake Storage
4. Hydro Inflow Levels
5. National Demand
"""
# Apply the masks to the dataframes and series
pd.DataFrame = nzem.apply_masks()
pd.Series = nzem.apply_series_masks()
# Load the five current datasets
energy_prices = nzem.load_csvfile("Nodal_Pricing_Five_Node.csv",
date_period=True)
reserve_prices = nzem.load_csvfile("reserve_prices.csv",
trading_period_id=True)
hydro_data = nzem.load_csvfile('hydro_data/Hydro_Lake_Data.csv',
niwa_date=True)
inflow_data = nzem.load_csvfile('hydro_data/Hydro_Inflow_Data.csv',
niwa_date=True)
demand_data = nzem.load_csvfile('island_demand_data.csv', date_period=True)
# Columnise the price data
all_res_prices = columnise_res_prices(reserve_prices)
all_en_prices = columnise_energy_prices(energy_prices)
# Resample the hydro data
hd = hydro_data["Daily Stored"].resample('30Min', fill_method='ffill')
ind = inflow_data["Daily Inflow"].resample('30Min', fill_method='ffill')
# Nationalise the demand data
nat_demand = demand_data.groupby(lambda x:x)["Demand Sum"].sum()
nat_demand.name = "National Demand"
# Merge all of the datasets
md = all_en_prices.merge(all_res_prices, left_index=True, right_index=True)
md = nzem.merge_dfseries(md, hd, left_index=True)
md = nzem.merge_dfseries(md, ind, left_index=True)
md = nzem.merge_dfseries(md, nat_demand, left_index=True)
return md
def load_masterset():
""" Load a master dataset will return a merged DataFrame containing the
following information time alligned:
1. Energy Prices (Five Nodes)
2. Reserve Prices (Two Region, Two Type)
3. Hydro Lake Storage
4. Hydro Inflow Levels
5. National Demand
"""
# Apply the masks to the dataframes and series
pd.DataFrame = nzem.apply_masks()
pd.Series = nzem.apply_series_masks()
# Load the five current datasets
energy_prices = nzem.load_csvfile("Nodal_Pricing_Five_Node.csv",
date_period=True)
reserve_prices = nzem.load_csvfile("reserve_prices.csv",
trading_period_id=True)
hydro_data = nzem.load_csvfile('hydro_data/Hydro_Lake_Data.csv',
niwa_date=True)
inflow_data = nzem.load_csvfile('hydro_data/Hydro_Inflow_Data.csv',
niwa_date=True)
demand_data = nzem.load_csvfile('island_demand_data.csv', date_period=True)
# Columnise the price data
all_res_prices = columnise_res_prices(reserve_prices)
all_en_prices = columnise_energy_prices(energy_prices)
# Resample the hydro data
hd = hydro_data["Daily Stored"].resample('30Min', fill_method='ffill')
ind = inflow_data["Daily Inflow"].resample('30Min', fill_method='ffill')
# Nationalise the demand data
nat_demand = demand_data.groupby(lambda x: x)["Demand Sum"].sum()
nat_demand.name = "National Demand"
# Merge all of the datasets
md = all_en_prices.merge(all_res_prices, left_index=True, right_index=True)
md = nzem.merge_dfseries(md, hd, left_index=True)
md = nzem.merge_dfseries(md, ind, left_index=True)
md = nzem.merge_dfseries(md, nat_demand, left_index=True)
return md
def columnise_res_prices(df,
island_price=True,
islandid="Island Id",
longname=False):
"""
Columnise the reserve price dataframe
"""
if longname:
ni = "North Island"
si = "South Island"
else:
ni = "NI"
si = "SI"
# Split them
nif = df.eq_mask("Reserve Type", "F").eq_mask(islandid, ni)
nis = df.eq_mask("Reserve Type", "S").eq_mask(islandid, ni)
sif = df.eq_mask("Reserve Type", "F").eq_mask(islandid, si)
sis = df.eq_mask("Reserve Type", "S").eq_mask(islandid, si)
# Rename columns
nif = nif.rename(columns={"Price Sum": "NI FIR Price"})
nis = nis.rename(columns={"Price Sum": "NI SIR Price"})
sif = sif.rename(columns={"Price Sum": "SI FIR Price"})
sis = sis.rename(columns={"Price Sum": "SI SIR Price"})
# Remerge them back together
mdf = nzem.merge_series(nif["NI FIR Price"], nis["NI SIR Price"])
mdf = nzem.merge_dfseries(mdf, sif["SI FIR Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, sis["SI SIR Price"], left_index=True)
if island_price:
mdf["NI Reserve Price"] = mdf["NI FIR Price"] + mdf["NI SIR Price"]
mdf["SI Reserve Price"] = mdf["SI FIR Price"] + mdf["SI SIR Price"]
return mdf
def columnise_res_prices(df, island_price=True, islandid="Island Id",
longname=False):
"""
Columnise the reserve price dataframe
"""
if longname:
ni = "North Island"
si = "South Island"
else:
ni = "NI"
si = "SI"
# Split them
nif = df.eq_mask("Reserve Type", "F").eq_mask(islandid, ni)
nis = df.eq_mask("Reserve Type", "S").eq_mask(islandid, ni)
sif = df.eq_mask("Reserve Type", "F").eq_mask(islandid, si)
sis = df.eq_mask("Reserve Type", "S").eq_mask(islandid, si)
# Rename columns
nif = nif.rename(columns={"Price Sum": "NI FIR Price"})
nis = nis.rename(columns={"Price Sum": "NI SIR Price"})
sif = sif.rename(columns={"Price Sum": "SI FIR Price"})
sis = sis.rename(columns={"Price Sum": "SI SIR Price"})
# Remerge them back together
mdf = nzem.merge_series(nif["NI FIR Price"], nis["NI SIR Price"])
mdf = nzem.merge_dfseries(mdf, sif["SI FIR Price"], left_index=True)
mdf = nzem.merge_dfseries(mdf, sis["SI SIR Price"], left_index=True)
if island_price:
mdf["NI Reserve Price"] = mdf["NI FIR Price"] + mdf["NI SIR Price"]
mdf["SI Reserve Price"] = mdf["SI FIR Price"] + mdf["SI SIR Price"]
return mdf
