Spaces:
Sleeping
Sleeping
Daniel Varga
commited on
Commit
•
85e0a54
1
Parent(s):
9e9b514
app
Browse files
app.py
ADDED
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1 |
+
# port of
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2 |
+
# https://colab.research.google.com/drive/1PJgcJ4ly7x5GuZy344eJeYSODo8trbM4#scrollTo=39F2u-4hvwLU
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+
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from dataclasses import dataclass
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+
import numpy as np
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import pandas as pd
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+
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import matplotlib.pyplot as plt
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9 |
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import matplotlib
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import datetime
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from scipy.interpolate import interp1d
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import gradio as gr
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import plotly.express as px
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import plotly.graph_objects as go
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#@title ### Downloading the data
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# !wget "https://static.renyi.hu/ai-shared/daniel/pq/PL_44527.19-21.csv"
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# !wget "https://static.renyi.hu/ai-shared/daniel/pq/pq_terheles_2021_adatok.tsv"
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+
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PATH_PREFIX = "./"
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matplotlib.rcParams['figure.figsize'] = [12, 8]
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START = f"2021-01-01"
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END = f"2022-01-01"
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+
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def read_datasets():
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#@title ### Preprocessing meteorologic data
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33 |
+
met_data = pd.read_csv(PATH_PREFIX + 'PL_44527.19-21.csv', sep=';', skipinitialspace=True, na_values='n/a', skiprows=[0, 1, 2, 3, 4])
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34 |
+
met_data['Time'] = met_data['Time'].astype(str)
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35 |
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date_time = met_data['Time'] = pd.to_datetime(met_data['Time'], format='%Y%m%d%H%M')
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36 |
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met_data = met_data.set_index('Time')
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37 |
+
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+
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39 |
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#@title ### Preprocessing consumption data
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40 |
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cons_data = pd.read_csv(PATH_PREFIX + 'pq_terheles_2021_adatok.tsv', sep='\t', skipinitialspace=True, na_values='n/a', decimal=',')
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41 |
+
cons_data['Time'] = pd.to_datetime(cons_data['Korrigált időpont'], format='%m/%d/%y %H:%M')
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42 |
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cons_data = cons_data.set_index('Time')
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43 |
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cons_data['Consumption'] = cons_data['Hatásos teljesítmény [kW]']
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44 |
+
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45 |
+
# consumption data is at 14 29 44 59 minutes, we move it by 1 minute
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46 |
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# to sync it with production data:
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47 |
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cons_data.index = cons_data.index + pd.DateOffset(minutes=1)
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48 |
+
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met_2021_data = met_data[(met_data.index >= START) & (met_data.index < END)]
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50 |
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cons_2021_data = cons_data[(cons_data.index >= START) & (cons_data.index < END)]
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51 |
+
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52 |
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return met_2021_data, cons_2021_data
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53 |
+
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+
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55 |
+
@dataclass
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56 |
+
class Parameters:
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57 |
+
solar_cell_num: float = 114 # units
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58 |
+
solar_efficiency: float = 0.93 * 0.96 # [dimensionless]
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59 |
+
NOCT: float = 280 # [W]
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60 |
+
NOCT_irradiation: float = 800 # [W/m^2]
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61 |
+
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62 |
+
bess_nominal_capacity: float = 330 # [Ah]
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63 |
+
bess_charge: float = 50 # [kW]
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64 |
+
bess_discharge: float = 60 # [kW]
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65 |
+
voltage: float = 600 # [V]
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66 |
+
maximal_depth_of_discharge: float = 0.75 # [dimensionless]
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67 |
+
energy_loss: float = 0.1 # [dimensionless]
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68 |
+
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69 |
+
@property
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70 |
+
def bess_capacity(self):
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71 |
+
return self.bess_nominal_capacity * self.voltage / 1000
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72 |
+
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73 |
+
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74 |
+
# mutates met_2021_data
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75 |
+
def add_production_field(met_2021_data, parameters):
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76 |
+
sr = met_2021_data['sr']
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77 |
+
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78 |
+
nop_total = sr * parameters.solar_cell_num * parameters.solar_efficiency * parameters.NOCT / parameters.NOCT_irradiation / 1e3
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79 |
+
nop_total = nop_total.clip(0)
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80 |
+
met_2021_data['Production'] = nop_total
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81 |
+
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82 |
+
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83 |
+
def interpolate_and_join(met_2021_data, cons_2021_data):
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84 |
+
applicable = 24*60*365 - 15 + 5
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85 |
+
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86 |
+
demand_f = interp1d(range(0, 365*24*60, 15), cons_2021_data['Consumption'])
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87 |
+
#demand_f = interp1d(range(0, 6*24*60, 15), cons_2021_data['Consumption'])
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88 |
+
demand_interp = demand_f(range(0, applicable, 5))
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89 |
+
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90 |
+
production_f = interp1d(range(0, 365*24*60, 10), met_2021_data['Production'])
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91 |
+
#production_f = interp1d(range(0, 6*24*60, 10), met_2021_data['Production'])
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92 |
+
production_interp = production_f(range(0, applicable, 5))
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93 |
+
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94 |
+
all_2021_datetimeindex = pd.date_range(start=START, end=END, freq='5min')[:len(production_interp)]
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95 |
+
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96 |
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all_2021_data = pd.DataFrame({'Consumption': demand_interp, 'Production': production_interp})
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97 |
+
all_2021_data = all_2021_data.set_index(all_2021_datetimeindex)
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98 |
+
return all_2021_data
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99 |
+
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100 |
+
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101 |
+
def simulator_with_solar(all_data, parameters):
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102 |
+
demand_np = all_data['Consumption'].to_numpy()
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103 |
+
production_np = all_data['Production'].to_numpy()
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104 |
+
assert len(demand_np) == len(production_np)
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105 |
+
step_in_minutes = all_data.index.freq.n
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106 |
+
print("Simulating for", len(demand_np), "time steps. Each step is", step_in_minutes, "minutes.")
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107 |
+
soc_series = [] # soc = state_of_charge.
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108 |
+
# by convention, we only call end user demand, demand,
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109 |
+
# and we only call end user consumption, consumption.
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110 |
+
# in our simple model, demand is always satisfied, hence demand=consumption.
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111 |
+
# BESS demand is called charge.
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112 |
+
consumption_from_solar_series = [] # demand satisfied by solar production
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113 |
+
consumption_from_network_series = [] # demand satisfied by network
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114 |
+
consumption_from_bess_series = [] # demand satisfied by BESS
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115 |
+
# the previous three must sum to demand_series.
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116 |
+
charge_of_bess_series = [] # power taken from solar by BESS. note: power never taken from network by BESS.
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117 |
+
discarded_production_series = [] # solar power thrown away
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118 |
+
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119 |
+
# 1 is not nominal but targeted (healthy) maximum charge.
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120 |
+
# we start with an empty battery, but not emptier than what's healthy for the batteries.
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121 |
+
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122 |
+
#Remark from Jutka
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123 |
+
#For the sake of simplicity 0<= soc <=1
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124 |
+
#soc = 1 - maximal_depth_of_discharge
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125 |
+
#and will use only maximal_depth_of_discharge percent of the real battery capacity
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126 |
+
soc = 0
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127 |
+
max_cap_of_battery = parameters.bess_capacity * parameters.maximal_depth_of_discharge
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128 |
+
cap_of_battery = soc * max_cap_of_battery
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129 |
+
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130 |
+
time_interval = step_in_minutes / 60 # amount of time step in hours
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131 |
+
for i, (demand, production) in enumerate(zip(demand_np, production_np)):
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132 |
+
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133 |
+
# these three are modified on the appropriate codepaths:
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134 |
+
consumption_from_solar = 0
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135 |
+
consumption_from_bess = 0
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136 |
+
consumption_from_network = 0
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137 |
+
charge_of_bess = 0
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138 |
+
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139 |
+
#Remark: If the consumption stable for ex. 10 kwh
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140 |
+
# demand = 10
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141 |
+
unsatisfied_demand = demand
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142 |
+
remaining_production = production # max((production, 0))
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143 |
+
discarded_production = 0
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144 |
+
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145 |
+
# crucially, we never charge the BESS from the network.
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146 |
+
# if demand >= production:
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147 |
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# all goes to demand
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148 |
+
# we try to cover the rest from BESS
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149 |
+
# we cover the rest from network
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150 |
+
# else:
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151 |
+
# demand fully satisfied by production
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152 |
+
# if exploitable production still remains:
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153 |
+
# if is_battery_chargeable:
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154 |
+
# charge_battery
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155 |
+
# else:
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156 |
+
# log discarded production
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157 |
+
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158 |
+
#battery_charged_enough = (soc > 1- maximal_depth_of_discharge)
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159 |
+
is_battery_charged_enough = (soc > 0 )
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160 |
+
is_battery_chargeable = (soc < 1.0)
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161 |
+
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162 |
+
if unsatisfied_demand >= remaining_production:
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163 |
+
# all goes to demand
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164 |
+
consumption_from_solar = remaining_production
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165 |
+
unsatisfied_demand -= consumption_from_solar
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166 |
+
remaining_production=0 #edited by Jutka
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167 |
+
# we try to cover the rest from BESS
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168 |
+
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169 |
+
if unsatisfied_demand > 0:
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170 |
+
if is_battery_charged_enough:
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171 |
+
# simplifying assumption for now:
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172 |
+
# throughput is enough to completely fulfill extra demand.
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173 |
+
# TODO get rid of simplifying assumption.
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174 |
+
# Remarks from Jutka
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175 |
+
# It is a very bed assumption. The reality needs, that the BESS has limited capacity.
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176 |
+
#
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177 |
+
#
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178 |
+
# cap_of_bess=soc * bess_capacity
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179 |
+
# if cap_of_bess > unsatisfied_demand
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180 |
+
# consumption_from_bess = unsatisfied_demand
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181 |
+
# unsatisfied_demand = 0
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182 |
+
# cap_of_bess -= consumption_from_bess
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183 |
+
# soc = cap_of_bess / bess_capacity
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184 |
+
# else: unsatisfied_demand -= cap_of_bess
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185 |
+
# cap_of_bess = 0
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186 |
+
# soc = 0
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187 |
+
# if unsatisfied_demand > 0
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188 |
+
# consumption_from_network = unsatisfied_demand
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189 |
+
# unsatisfied_demand = 0
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190 |
+
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191 |
+
#Remarks: battery capacity is limited!
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192 |
+
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193 |
+
if cap_of_battery >= unsatisfied_demand * time_interval :
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194 |
+
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195 |
+
#discharge_of_bess = min ( unsatisfied_demand, bess_discharge )
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196 |
+
#discharge = discharge_of_bess
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197 |
+
#consumption_from_bess = discharge * time_interval
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198 |
+
consumption_from_bess = unsatisfied_demand
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199 |
+
#unsatisfied_demand -= consumption_from_bess
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200 |
+
unsatisfied_demand = 0
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201 |
+
cap_of_battery -= consumption_from_bess * time_interval
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202 |
+
soc = cap_of_battery / max_cap_of_battery
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203 |
+
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204 |
+
else:
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205 |
+
#discharge_of_bess = cap_of_battery /time_interval
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206 |
+
#discharge = min( bess_discharge, discharge_of_bess )
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207 |
+
consumption_from_bess = cap_of_battery / time_interval
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208 |
+
unsatisfied_demand -= consumption_from_bess
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209 |
+
cap_of_battery -=consumption_from_bess * time_interval
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210 |
+
soc = cap_of_battery / max_cap_of_battery
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211 |
+
consumption_from_network = unsatisfied_demand
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212 |
+
unsatisfied_demand = 0
|
213 |
+
#bess_sacrifice = consumption_from_bess / (1 - energy_loss) # kW
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214 |
+
#energy = bess_sacrifice * time_interval # kWh
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215 |
+
#soc -= energy / bess_capacity
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216 |
+
# print("soc after discharge", soc)
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217 |
+
#consumption_from_network = unsatisfied_demand
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218 |
+
#unsatisfied_demand = 0
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219 |
+
else:
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220 |
+
# we cover the rest from network
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221 |
+
consumption_from_network = unsatisfied_demand
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222 |
+
unsatisfied_demand = 0
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223 |
+
|
224 |
+
else:
|
225 |
+
# demand fully satisfied by production
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226 |
+
|
227 |
+
consumption_from_solar = unsatisfied_demand
|
228 |
+
remaining_production -= unsatisfied_demand
|
229 |
+
unsatisfied_demand = 0
|
230 |
+
# if exploitable production still remains:
|
231 |
+
if remaining_production > 0:
|
232 |
+
if is_battery_chargeable:
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233 |
+
charge_of_bess = remaining_production
|
234 |
+
energy = charge_of_bess * time_interval # kWh
|
235 |
+
#Remarks: battery alowed to charge until its capacity maximum
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236 |
+
#energy_charge = min(energy, max_cap_of_battery-cap_of_battery)
|
237 |
+
cap_of_battery += energy
|
238 |
+
#soc += energy / bess_capacity
|
239 |
+
soc = cap_of_battery / max_cap_of_battery
|
240 |
+
#print("soc after charge", soc)
|
241 |
+
else:
|
242 |
+
discarded_production = remaining_production
|
243 |
+
|
244 |
+
soc_series.append(soc)
|
245 |
+
consumption_from_solar_series.append(consumption_from_solar)
|
246 |
+
consumption_from_network_series.append(consumption_from_network)
|
247 |
+
consumption_from_bess_series.append(consumption_from_bess)
|
248 |
+
charge_of_bess_series.append(charge_of_bess)
|
249 |
+
discarded_production_series.append(discarded_production)
|
250 |
+
|
251 |
+
soc_series = np.array(soc_series)
|
252 |
+
consumption_from_solar_series = np.array(consumption_from_solar_series)
|
253 |
+
consumption_from_network_series = np.array(consumption_from_network_series)
|
254 |
+
consumption_from_bess_series = np.array(consumption_from_bess_series)
|
255 |
+
charge_of_bess_series = np.array(charge_of_bess_series)
|
256 |
+
discarded_production_series = np.array(discarded_production)
|
257 |
+
|
258 |
+
results = pd.DataFrame({'soc_series': soc_series, 'consumption_from_solar': consumption_from_solar_series,
|
259 |
+
'consumption_from_network': consumption_from_network_series,
|
260 |
+
'consumption_from_bess': consumption_from_bess_series,
|
261 |
+
'charge_of_bess': charge_of_bess_series,
|
262 |
+
'discarded_production': discarded_production_series,
|
263 |
+
'Consumption': all_data['Consumption'],
|
264 |
+
'Production': all_data['Production']
|
265 |
+
})
|
266 |
+
results = results.set_index(all_data.index)
|
267 |
+
return results
|
268 |
+
|
269 |
+
|
270 |
+
def visualize_simulation(results, date_range):
|
271 |
+
start_date, end_date = date_range
|
272 |
+
|
273 |
+
fig = plt.figure()
|
274 |
+
results = results.loc[start_date: end_date]
|
275 |
+
|
276 |
+
x = results.index
|
277 |
+
y = [results.consumption_from_solar, results.consumption_from_network, results.consumption_from_bess]
|
278 |
+
plt.plot(x, y[0], label='Demand served by solar', color='yellow', linewidth=0.5)
|
279 |
+
plt.plot(x, y[0]+y[1], label='Demand served by network', color='blue', linewidth=0.5)
|
280 |
+
plt.plot(x, y[0]+y[1]+y[2], label='Demand served by BESS', color='green', linewidth=0.5)
|
281 |
+
plt.fill_between(x, y[0]+y[1]+y[2], 0, color='green')
|
282 |
+
plt.fill_between(x, y[0]+y[1], 0, color='blue')
|
283 |
+
plt.fill_between(x, y[0], 0, color='yellow')
|
284 |
+
|
285 |
+
# plt.xlim(datetime.datetime.fromisoformat(start_date), datetime.datetime.fromisoformat(end_date))
|
286 |
+
|
287 |
+
plt.legend()
|
288 |
+
return fig
|
289 |
+
|
290 |
+
|
291 |
+
def plotly_visualize_simulation(results, date_range):
|
292 |
+
start_date, end_date = date_range
|
293 |
+
results = results.loc[start_date: end_date]
|
294 |
+
'''
|
295 |
+
fig = px.area(results, x=results.index, y="consumption_from_network")
|
296 |
+
return fig'''
|
297 |
+
fig = go.Figure()
|
298 |
+
fig.add_trace(go.Scatter(
|
299 |
+
x=results.index, y=results['consumption_from_network'],
|
300 |
+
hoverinfo='x+y',
|
301 |
+
mode='lines',
|
302 |
+
line=dict(width=0.5, color='blue'),
|
303 |
+
name='Network',
|
304 |
+
stackgroup='one' # define stack group
|
305 |
+
))
|
306 |
+
fig.add_trace(go.Scatter(
|
307 |
+
x=results.index, y=results['consumption_from_solar'],
|
308 |
+
hoverinfo='x+y',
|
309 |
+
mode='lines',
|
310 |
+
line=dict(width=0.5, color='orange'),
|
311 |
+
name='Solar',
|
312 |
+
stackgroup='one'
|
313 |
+
))
|
314 |
+
fig.add_trace(go.Scatter(
|
315 |
+
x=results.index, y=results['consumption_from_bess'],
|
316 |
+
hoverinfo='x+y',
|
317 |
+
mode='lines',
|
318 |
+
line=dict(width=0.5, color='green'),
|
319 |
+
name='BESS',
|
320 |
+
stackgroup='one'
|
321 |
+
))
|
322 |
+
fig.update_layout(
|
323 |
+
height=400
|
324 |
+
)
|
325 |
+
return fig
|
326 |
+
|
327 |
+
|
328 |
+
def monthly_analysis(results):
|
329 |
+
consumptions = []
|
330 |
+
for month in range(1, 13):
|
331 |
+
start = f"2021-{month:02}-01"
|
332 |
+
end = f"2021-{month+1:02}-01"
|
333 |
+
if month == 12:
|
334 |
+
end = "2022-01-01"
|
335 |
+
results_in_month = results[(results.index >= start) & (results.index < end)]
|
336 |
+
|
337 |
+
total = results_in_month['Consumption'].sum()
|
338 |
+
network = results_in_month['consumption_from_network'].sum()
|
339 |
+
solar = results_in_month['consumption_from_solar'].sum()
|
340 |
+
bess = results_in_month['consumption_from_bess'].sum()
|
341 |
+
consumptions.append([network, solar, bess])
|
342 |
+
|
343 |
+
consumptions = np.array(consumptions)
|
344 |
+
step_in_minutes = results.index.freq.n
|
345 |
+
# consumption is given in kW. each tick is step_in_minutes long (5mins, in fact)
|
346 |
+
# we get consumption in kWh if we multiply sum by step_in_minutes/60
|
347 |
+
consumptions_in_mwh = consumptions * (step_in_minutes / 60) / 1000
|
348 |
+
return consumptions_in_mwh
|
349 |
+
|
350 |
+
|
351 |
+
def monthly_visualization(consumptions_in_mwh):
|
352 |
+
percentages = consumptions_in_mwh[:, :3] / consumptions_in_mwh.sum(axis=1, keepdims=True) * 100
|
353 |
+
bats = 0
|
354 |
+
nws = 0
|
355 |
+
sols = 0
|
356 |
+
|
357 |
+
print("[Mwh]")
|
358 |
+
print("==========================")
|
359 |
+
print("month\tnetwork\tsolar\tbess")
|
360 |
+
for month_minus_1 in range(12):
|
361 |
+
network, solar, bess = consumptions_in_mwh[month_minus_1]
|
362 |
+
print(f"{month_minus_1+1}\t{network:0.2f}\t{solar:0.2f}\t{bess:0.2f}")
|
363 |
+
bats += bess
|
364 |
+
nws += network
|
365 |
+
sols += solar
|
366 |
+
print(f"\t{nws:0.2f}\t{sols:0.2f}\t{bats:0.2f}")
|
367 |
+
|
368 |
+
|
369 |
+
fig, ax = plt.subplots()
|
370 |
+
|
371 |
+
ax.stackplot(range(1, 13),
|
372 |
+
percentages[:, 0], percentages[:, 1], percentages[:, 2],
|
373 |
+
labels=["hálózat", "egyenesen a naptól", "a naptól a BESS-en keresztül"])
|
374 |
+
ax.set_ylim(0, 100)
|
375 |
+
ax.legend()
|
376 |
+
plt.title('A fogyasztás hány százalékát fedezte az adott hónapban?')
|
377 |
+
plt.show()
|
378 |
+
|
379 |
+
plt.stackplot(range(1, 13),
|
380 |
+
consumptions_in_mwh[:, 0], consumptions_in_mwh[:, 1], consumptions_in_mwh[:, 2],
|
381 |
+
labels=["hálózat", "egyenesen a naptól", "a naptól a BESS-en keresztül"])
|
382 |
+
plt.legend()
|
383 |
+
plt.title('Mennyi fogyasztást fedezett az adott hónapban? [MWh]')
|
384 |
+
plt.show()
|
385 |
+
|
386 |
+
|
387 |
+
def main():
|
388 |
+
parameters = Parameters()
|
389 |
+
|
390 |
+
met_2021_data, cons_2021_data = read_datasets()
|
391 |
+
|
392 |
+
add_production_field(met_2021_data, parameters)
|
393 |
+
|
394 |
+
all_2021_data = interpolate_and_join(met_2021_data, cons_2021_data)
|
395 |
+
|
396 |
+
results = simulator_with_solar(all_2021_data, parameters)
|
397 |
+
|
398 |
+
fig = visualize_simulation(results, date_range=("2021-02-01", "2021-03-01"))
|
399 |
+
plt.show()
|
400 |
+
|
401 |
+
consumptions_in_mwh = monthly_analysis(results)
|
402 |
+
monthly_visualization(consumptions_in_mwh)
|
403 |
+
|
404 |
+
|
405 |
+
# main() ; exit()
|
406 |
+
|
407 |
+
|
408 |
+
met_2021_data, cons_2021_data = read_datasets()
|
409 |
+
|
410 |
+
|
411 |
+
def recalculate(**uiParameters):
|
412 |
+
fixed_consumption = uiParameters['fixed_consumption']
|
413 |
+
del uiParameters['fixed_consumption']
|
414 |
+
|
415 |
+
parameters = Parameters()
|
416 |
+
for k, v in uiParameters.items():
|
417 |
+
setattr(parameters, k, v)
|
418 |
+
|
419 |
+
add_production_field(met_2021_data, parameters)
|
420 |
+
all_2021_data = interpolate_and_join(met_2021_data, cons_2021_data)
|
421 |
+
|
422 |
+
if fixed_consumption:
|
423 |
+
all_2021_data['Consumption'] = 10
|
424 |
+
|
425 |
+
results = simulator_with_solar(all_2021_data, parameters)
|
426 |
+
return results
|
427 |
+
|
428 |
+
|
429 |
+
def ui_refresh(solar_cell_num, bess_nominal_capacity, fixed_consumption):
|
430 |
+
results = recalculate(solar_cell_num=solar_cell_num, bess_nominal_capacity=bess_nominal_capacity, fixed_consumption=fixed_consumption)
|
431 |
+
|
432 |
+
fig1 = plotly_visualize_simulation(results, date_range=("2021-02-01", "2021-02-07"))
|
433 |
+
fig2 = plotly_visualize_simulation(results, date_range=("2021-08-02", "2021-08-08"))
|
434 |
+
|
435 |
+
# (12, 3), the 3 indexed with (network, solar, bess):
|
436 |
+
consumptions_in_mwh = monthly_analysis(results)
|
437 |
+
|
438 |
+
network, solar, bess = consumptions_in_mwh.sum(axis=0)
|
439 |
+
html = ""
|
440 |
+
for column, column_name in zip((network, solar, bess), ("network", "solar", "BESS")):
|
441 |
+
html += f"Yearly consumption satisfied by {column_name}: {column:0.2f} MWh<br>"
|
442 |
+
|
443 |
+
return (fig1, fig2, html)
|
444 |
+
|
445 |
+
|
446 |
+
ui = gr.Interface(
|
447 |
+
ui_refresh,
|
448 |
+
inputs = [
|
449 |
+
gr.Slider(0, 2000, 114, label="Solar cell number"),
|
450 |
+
gr.Slider(0, 1000, 330, label="BESS nominal capacity"),
|
451 |
+
gr.Checkbox(value=False, label="Fixed consumption")],
|
452 |
+
outputs = ["plot", "plot", "html"],
|
453 |
+
live=True,
|
454 |
+
)
|
455 |
+
|
456 |
+
ui.launch()
|