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Tooling
Commits
28327135
Commit
28327135
authored
2 years ago
by
Chenxue Mao
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changes for import of electricity price of different years
adapted to 15 min resolution
parent
b8cb3e12
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ArbitrageTool/Plots.py
+51
-65
51 additions, 65 deletions
ArbitrageTool/Plots.py
ArbitrageTool/generate_signals_profile.py
+20
-28
20 additions, 28 deletions
ArbitrageTool/generate_signals_profile.py
with
71 additions
and
93 deletions
ArbitrageTool/Plots.py
+
51
−
65
View file @
28327135
...
...
@@ -5,6 +5,7 @@ import numpy as np
import
matplotlib.pyplot
as
plt
import
matplotlib.dates
as
md
from
matplotlib.ticker
import
MaxNLocator
import
matplotlib
as
mp
...
...
@@ -54,22 +55,60 @@ def Barplotting_group(path):
ax
.
set_ylabel
(
'
Energy in kWh
'
)
ax
.
set_title
(
'
Energy demand at 2019/6/1 0:00:00
'
)
ax
.
set_xticks
(
x
,
[
'
electricity
'
,
'
heat
'
])
#matplotlib version >= 3.4
ax
.
legend
()
ax
.
legend
(
loc
=
'
upper left
'
)
ax
.
bar_label
(
rects1
,
labels
=
[
"
elec_demand
"
,
"
therm_dmd
"
])
ax
.
bar_label
(
rects2
,
labels
=
[
"
(
'
grd
'
,
'
elec_cns
'
)
"
,
"
(
'
gas_boi
'
,
'
therm_cns
'
)
"
])
fig
.
tight_layout
()
plt
.
show
()
def
Barplotting_sum
(
path
):
df
=
pd
.
read_excel
(
path
,
index_col
=
0
)
#, header=0
values
=
df
.
iloc
[
140
]
#pd.series
dmd1
=
values
[
'
elec_demand
'
]
dmd2
=
values
[
'
therm_demand
'
]
supel_1
=
values
[
"
(
'
grd
'
,
'
elec_cns
'
)
"
]
supel_2
=
values
[
"
(
'
bhkw
'
,
'
elec_cns
'
)
"
]
supth_1
=
values
[
"
(
'
bhkw
'
,
'
therm_cns
'
)
"
]
supth_2
=
values
[
"
(
'
gas_boi
'
,
'
therm_cns
'
)
"
]
def
plots
():
X_axis
=
[
"
2021-01
"
,
"
2021-07
"
,
"
2021-09
"
,
"
2021-12
"
,
"
2022-01
"
,
"
2022-05
"
,
"
2022-07
"
]
y_val
=
[
6969
,
8851
,
17411
,
24097
,
38194
,
38212
,
38250
]
y_val2
=
[
13.62
,
15.66
,
12.92
,
11.79
,
13.34
,
12.94
,
15.31
]
y_val3
=
[
15.33
,
17.90
,
15.13
,
14.62
,
15.05
,
15.05
,
17.56
]
x
=
np
.
arange
(
len
(
X_axis
))
width
=
0.3
fig
,
ax
=
plt
.
subplots
()
#ax.grid(True)
f1
=
ax
.
bar
(
x
-
width
,
y_val
,
width
,
color
=
[
0.1686
,
0.3098
,
0.5059
],
label
=
'
Arbitrage profit
'
)
#
#f2 = ax.bar(x + width / 2, y_val2, width, color=[0.251, 0.498, 0.7176], label='Arbitrage profit'))
# f3 = ax.bar(x + width / 2, y_val2, width, color=[0.251, 0.498, 0.7176])
ax2
=
ax
.
twinx
()
f2
=
ax2
.
bar
(
x
,
y_val2
,
width
,
color
=
[
0.251
,
0.498
,
0.7176
],
label
=
'
Electric price without investment
'
)
f3
=
ax2
.
bar
(
x
+
width
,
y_val3
,
width
,
color
=
[
0.7804
,
0.8667
,
0.949
],
label
=
'
Electric price with investment
'
)
#
ax
.
set_ylabel
(
'
mothly arbitrage profit in €
'
,
fontsize
=
14
)
ax
.
yaxis
.
set_major_formatter
(
mp
.
ticker
.
StrMethodFormatter
(
'
{x:,.0f}
'
))
ax2
.
set_ylabel
(
'
Electric price in Cent/kWh
'
,
fontsize
=
14
)
ax
.
set_title
(
'
Trend of Arbitrage profit and electric price
'
,
fontsize
=
16
)
ax
.
set_xticks
(
x
,
X_axis
)
ax
.
legend
(
loc
=
'
upper left
'
)
ax2
.
legend
()
#loc = (0.005, 0.85)
ax
.
bar_label
(
f1
,
labels
=
y_val
)
#, padding=3
ax2
.
bar_label
(
f2
,
labels
=
y_val2
)
ax2
.
bar_label
(
f3
,
labels
=
y_val3
)
fig
.
tight_layout
()
plt
.
show
()
def
Barplots
():
#X_axis = ['2019','2020','2021']
#X_axis = ['10% - 90%','15% - 85%','20% - 80%']
X_axis
=
[
'
1000kWh
'
,
'
5000kWh
'
,
'
10000kWh
'
]
y_values
=
[
3724.576
/
1000
,
18622.882
/
5000
,
37245.765
/
10000
]
width
=
0.5
fig
,
ax
=
plt
.
subplots
()
f1
=
ax
.
bar
(
X_axis
,
y_values
,
width
,
color
=
[
'
tab:blue
'
,
'
tab:blue
'
,
'
tab:orange
'
,
'
tab:orange
'
])
ax
.
set_ylabel
(
'
annual arbitrage resulted cost in €/kWh
'
)
ax
.
set_title
(
'
2021 Arbitrage results per kWh of LiionBatteryStack
'
)
ax
.
legend
()
ax
.
bar_label
(
f1
)
#, padding=3
plt
.
show
()
def
Barplotting0
(
path
):
df
=
pd
.
read_excel
(
path
,
index_col
=
0
)
# , header=0
...
...
@@ -91,59 +130,6 @@ def Barplotting0(path):
ax
.
bar_label
(
f1
)
plt
.
show
()
def
Barplots
():
# path_lst
y
=
list
()
# cycles=[]
# for i in range(len(path_lst)):
# df = pd.read_excel(path_lst[i], index_col=0)
# arb_cost = df["('arbitrage_cost', 'batterySta')"].iloc[-1]
# y.append(arb_cost)
#cyc = df["('cum_cycle', 'batterySta')"].iloc[-1]
#cycles.append(cyc)
#X_axis = ['2019','2020','2021']
#X_axis = ['10% - 90%','15% - 85%','20% - 80%']
#X_axis = ['1000kWh','5000kWh','10000kWh']
X_axis
=
[
"
SCN1
"
,
"
SCN2
"
,
"
SCN3_10MW
"
,
"
SCN3_15MW
"
,
"
SCN_2030
"
]
y_val
=
[
0.13879954
,
0.13879954
,
0.13755484
,
0.13693249
,
0.19911065
]
y_val2
=
[
0.13879954
,
0.13816321
,
0.13691852
,
0.13629624
,
0.19848812
]
#y[0], y[1], y[2]
#y_values = [round(i, 3) for i in y_val]
#cyc_val = [cycles[0], cycles[1],cycles[2]]
#cyc_val = [round(i, 3) for i in cyc_val]
x
=
np
.
arange
(
len
(
X_axis
))
# rects1 = ax.bar(x - width/2, y1, width, label='demand') #(0-0.1) (1-0.1)
# rects2 = ax.bar(x + width/2, y2, width, label='supplier')
width
=
0.35
fig
,
ax
=
plt
.
subplots
()
f1
=
ax
.
bar
(
x
-
width
/
2
,
y_val
,
width
,
color
=
[
'
tab:blue
'
,
'
tab:red
'
,
'
tab:orange
'
,
'
tab:green
'
,
'
tab:brown
'
])
#
f2
=
ax
.
bar
(
x
+
width
/
2
,
y_val2
,
width
,
color
=
[
'
tab:blue
'
,
'
tab:red
'
,
'
tab:orange
'
,
'
tab:green
'
,
'
tab:brown
'
])
#
ax
.
legend
()
#ax.grid(True)
ax
.
bar_label
(
f1
)
#, padding=3
ax
.
bar_label
(
f2
)
##################grouped Barplots
# y1 = y_values
# y2 = cyc_val
# x = np.arange(len(X_axis)) ## the label locations
# width = 0.35
# fig, ax = plt.subplots()
# # ax2 = ax.twinx()
# rects1 = ax.bar(x - width / 2, y1, width, label='arbitrage profits') # (0-0.1) (1-0.1)
# rects2 = ax.bar(x + width / 2, y2, width, label='cumulative cycles') # (0+0.1) (1+0.1)
#
# ax.set_xticks(x, X_axis) # matplotlib version >= 3.4
# ax.legend()
# ax.bar_label(rects1, labels=y_values )
# ax.bar_label(rects2, labels=cyc_val)
#ax.set_ylabel('annual arbitrage profits in €/kWh')
ax
.
set_ylabel
(
'
Monthly Electric Gestehungskosten in (ohne fixed) €/kWh_el
'
)
#'montly arbitrage profits in €'
#ax2.set_ylabel('cumulative cycles in 2021')
#ax.set_title('2021-07 Arbitrage profits per kWh of LiionBatteryStack')
ax
.
set_title
(
'
Comparation of electricity price in €/kWh
'
)
#Arbitrage profits of LiionBatteryStack in July 2021
plt
.
show
()
def
plotting_BAS1
(
path
):
df
=
pd
.
read_excel
(
path
,
index_col
=
0
)
...
...
@@ -504,8 +490,8 @@ if __name__ == "__main__":
# Subplots_3(path_lst, titles)
Barplots
()
#
Barplots()
plots
()
#path = r'C:\Users\10947\sciebo\Scenarios_v3\Outputfiles\Jan\SCN2_SG_BAS1_BHKW\results_SCN2_SG_BAS1_BHKW.xlsx'
# path = r'C:\Users\10947\sciebo\Scenarios_v3\Outputfiles\July\SCN2_SG_BAS1_BHKW_6_8\results_SCN2_SG_BAS1_BHKW_6_8.xlsx'
#path = r'C:\Users\10947\sciebo\Scenarios_v3\Outputfiles\Jan\SCN3_SG_BAS1_BHKW_PV10MW\results_SCN3_SG_BAS1_BHKW_PV10MW.xlsx'
...
...
This diff is collapsed.
Click to expand it.
ArbitrageTool/generate_signals_profile.py
+
20
−
28
View file @
28327135
import
pandas
as
pd
import
datetime
#
import numpy as np
import
numpy
as
np
def
extract_elec_price_profile
(
dstart
,
dend
,
t_step
):
# year = 2021
# dstart = datetime.datetime(year,1,1,00,00,00)
# dend = datetime.datetime(year,12,31,23,00,00)
year
=
dstart
.
year
...
...
@@ -35,35 +34,29 @@ def extract_elec_price_profile(dstart, dend, t_step):
freq
=
str
(
t_step
)
+
'
H
'
)
price_df
=
price_year_data
else
:
# if year == 2020:
# pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2020.csv'
# elif year == 2021:
# pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2021.csv'
# elif year == 2030:
# pricedata_path = r'C:\Users\10947\sciebo\MA Mao\Preisdaten\predicted_Strompreise_in_Deutschland_2030.csv'
#
# raw_data = pd.read_csv(pricedata_path, usecols=['Time', 'Intraday Continuous Average Price'])
# if len(raw_data) == t_horizon:
# price_year_data = raw_data.iloc[:,1].astype(float)
# else:
# diff_len = int(t_horizon-len(raw_data))
# price_year_data = raw_data.iloc[:diff_len, 1].astype(float)
# price_year_data.index = pd.date_range(dstart, periods=t_horizon, freq= str(t_step) +'H')
# price_year_data = pd.DataFrame(price_year_data)
#
# if len(price_year_data) != t_horizon:
# price_df = pd.DataFrame(index=pd.date_range(datetime.datetime(year, 1, 1, 00, 00, 00), periods=t_horizon, freq= str(t_step) +'H'))
# price_df['price'] = price_year_data.iloc[:,0]
# price_df = price_df.fillna(method='ffill') #Todo: fill with rational values
# else:
# price_df = price_year_data
elif
year
==
2022
:
#2021
pricedata_path
=
r
'
C:\Users\10947\sciebo\MA Mao\Preisdaten\epex_price_2022.csv
'
price_df
=
pd
.
read_csv
(
pricedata_path
,
index_col
=
0
)
price_df
.
index
=
pd
.
date_range
(
dstart
,
periods
=
len
(
price_df
),
freq
=
'
15min
'
)
price_df
.
index
=
pd
.
date_range
(
dstart
,
periods
=
len
(
price_df
),
freq
=
'
15min
'
)
# intraday price 2022 has 15 min resolution
price_df
=
price_df
.
loc
[
dstart
:
dend
]
price_df
=
price_df
.
resample
(
str
(
t_step
)
+
'
H
'
).
mean
().
interpolate
(
'
linear
'
)
else
:
if
year
==
2020
:
pricedata_path
=
r
'
C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2020.csv
'
elif
year
==
2021
:
pricedata_path
=
r
'
C:\Users\10947\sciebo\MA Mao\Preisdaten\Stromproduktion_und_Boersenstrompreise_in_Deutschland_2021.csv
'
elif
year
==
2030
:
pricedata_path
=
r
'
C:\Users\10947\sciebo\MA Mao\Preisdaten\predicted_Strompreise_in_Deutschland_2030.csv
'
raw_data
=
pd
.
read_csv
(
pricedata_path
,
usecols
=
[
'
Intraday Continuous Average Price
'
])
# this data has resolution of 1h
price_year_data
=
raw_data
.
replace
(
to_replace
=
'
None
'
,
value
=
np
.
nan
).
dropna
()
price_year_data
.
index
=
pd
.
date_range
(
datetime
.
datetime
(
year
,
1
,
1
,
00
,
00
,
00
),
periods
=
len
(
price_year_data
),
freq
=
'
1h
'
)
price_df
=
price_year_data
.
loc
[
dstart
:
dend
]
price_df
=
price_df
.
astype
(
float
)
price_df
=
price_df
.
resample
(
str
(
t_step
)
+
'
H
'
).
mean
().
interpolate
(
'
linear
'
)
return
price_df
...
...
@@ -119,4 +112,3 @@ def export_arbitrage_signals(dstart, dend, t_step):
#if __name__ == "__main__":
#df = export_arbitrage_signals()
#df.to_csv(r'C:\Users\10947\sciebo\MA Mao\Preisdaten_bearbeitet\Price_data_and_signals_2021.csv')
\ No newline at end of file
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