diff --git a/ArbitrageTool/Plots.py b/ArbitrageTool/Plots.py
index a11b3b19a7e68f4a620e45f47defb7e0b49e7d7b..2dbddbff60d7ee70283609f4a02dd2df73f58db6 100644
--- a/ArbitrageTool/Plots.py
+++ b/ArbitrageTool/Plots.py
@@ -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'
diff --git a/ArbitrageTool/generate_signals_profile.py b/ArbitrageTool/generate_signals_profile.py
index 2b5c3a82f20133a5db3686cb09d2604073bde8ec..232e8693aa92666d8bd3e04667e787c4a87be4ec 100644
--- a/ArbitrageTool/generate_signals_profile.py
+++ b/ArbitrageTool/generate_signals_profile.py
@@ -1,9 +1,8 @@
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')
+ 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