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create_results.py
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create_results.py
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from sqlalchemy import create_engine
from matplotlib import pyplot as plt
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
import os
import numpy as np
from config import DATABASE_URI
ENGINE = create_engine(DATABASE_URI)
SCENARIOS = ['EV100PV100PRC1.0', 'EV100PV100PRC1.7', 'EV100PV100PRC2.7', 'EV100PV100PRC4.0']
FONT = dict(family="Verdana", size=12, color="black")
COLORS = {'EV100PV100PRC1.0': 'rgb(0,204,0)', 'EV100PV100PRC1.7': 'rgb(0,204,153)',
'EV100PV100PRC2.7': 'rgb(0,102,153)', 'EV100PV100PRC4.0': 'rgb(0,0,153)'}
COLORS_SENS = {1.0: 'rgb(0,204,0)', 1.7: 'rgb(0,204,153)',
2.7: 'rgb(0,102,153)', 4.0: 'rgb(0,0,153)'}
def get_transformer_utilization(scenario):
# -> get transformer utilization
query = f"select time, utilization from grid " \
f"where scenario='{scenario}' and asset='transformer'"
utilization = pd.read_sql(query, ENGINE)
utilization['time'] = utilization['time'].apply(pd.to_datetime)
return utilization
def get_final_charge(scenario):
# -> get sum power over each iteration in a scenario
query = f"select time, 0.25 * sum(final_charge) as demand from cars" \
f" where scenario='{scenario}' " \
f"group by time"
demand = pd.read_sql(query, ENGINE)
demand['time'] = demand['time'].apply(pd.to_datetime)
demand['demand'] /= 30
return demand
def get_charging_at_quarters(scenario):
# ->
query = f"select to_char(res.ti, 'hh24:mi') as inter, avg(res.planned) as planned, avg(res.final) as final " \
f"from (select time as ti, sum(planned_grid_consumption) as planned, sum(final_grid_consumption) as final " \
f"from residential where scenario='{scenario}' group by ti) as res " \
f"group by inter"
data = pd.read_sql(query, ENGINE)
return data
def get_car_charging_at_quarters(scenario):
query = f"select to_char(car.ti, 'hh24:mi') as inter, avg(car.final) as final " \
f"from (select time as ti, sum(final_charge) as final " \
f"from cars where scenario='{scenario}' group by ti) as car " \
f"group by inter"
data = pd.read_sql(query, ENGINE)
return data
def get_values_in_price_intervals(d: pd.DataFrame, parameter: str = 'utilization'):
values, names, counter = [], [], []
intervals = list(np.linspace(0, 40, 5)) + [1e9]
for min_, max_ in zip(intervals[:-1], intervals[1:]):
values += [d.loc[(d['price'] >= min_) & (d['price'] < max_), parameter].values]
counter += [sum((d['price'] >= min_) & (d['price'] < max_))]
if max_ == 1e9:
names += [f'{round(min_, 0)} <']
else:
names += [f'{round(min_, 0)} - {round(max_, 0)}']
return values, names, counter
def create_scatter_box_plot():
fig = make_subplots(rows=2, cols=1, specs=[[{"secondary_y": False}], [{"secondary_y": False}]],
shared_xaxes=True, row_width=[0.1, 0.4])
values, names, counter = [], [], []
for SCENARIO in SCENARIOS:
util = get_transformer_utilization(SCENARIO)
dem = get_final_charge(SCENARIO)
prc = get_prices()
util['price'] = [prc.loc[t, 'price'] for t in util['time'].values]
data = util.loc[:, ['utilization', 'price']]
data = data.drop_duplicates()
values, names, counter = get_values_in_price_intervals(d=data, parameter='utilization')
for val, name in zip(values, names):
fig.add_trace(go.Box(y=val, name=name, showlegend=False, marker_color=COLORS[SCENARIO],
boxpoints=False), row=1, col=1)
dem['price'] = [prc.loc[t, 'price'] for t in dem['time'].values]
data = dem.loc[:, ['demand', 'price']]
data = data.drop_duplicates()
values, names, counter = get_values_in_price_intervals(d=data, parameter='demand')
fig.add_trace(go.Bar(name=SCENARIO, x=names, y=[sum(val) / 1e3 for val in values],
marker_color=COLORS[SCENARIO]), row=2, col=1)
for n, c in zip(names, counter):
fig.add_annotation(x=n, y=50, showarrow=False, text=f'Charging Options: {c}', row=2, col=1)
# -> set axes titles
fig.update_yaxes(title_text="Utilization [%]",
secondary_y=False,
showgrid=True,
range=[0, 201],
gridwidth=0.1,
gridcolor='rgba(0, 0, 0, 0.5)',
row=1, col=1)
fig.update_yaxes(title_text="Total Charged [MWh]",
secondary_y=False,
showgrid=True,
gridwidth=0.1,
gridcolor='rgba(0, 0, 0, 0.5)',
row=2, col=1)
fig.update_xaxes(title_text="Price [ct/kWh]",
showgrid=False,
gridwidth=0.1,
gridcolor='rgba(0, 0, 0, 0.5)',
row=2, col=1)
fig.update_layout(font=FONT, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
boxmode='group', boxgroupgap=0.005, boxgap=0.005, barmode='group',
bargap=0.005)
fig.update_layout(legend=dict(orientation="h", yanchor="bottom", y=-0.1, xanchor="right", x=1))
fig.write_html(f'scatter_boxplot_utilization.html')
def create_shifted_charging(all: bool = False):
if all:
rows = len(SCENARIOS)
specs = [[{"secondary_y": True}] for _ in range(rows)]
scenarios = SCENARIOS
else:
rows=1
specs = [[{"secondary_y": True}]]
scenarios = [SCENARIOS[-1]]
prc = get_prices()
prc = prc.groupby(prc.index.hour).mean()
prc = np.asarray([4*[p] for p in prc['price'].values]).flatten()
fig = make_subplots(rows=rows, cols=1, specs=specs, shared_xaxes=True)
for scenario, row in zip(scenarios, range(1, rows+1)):
d = get_charging_at_quarters(scenario=scenario)
fig.add_trace(go.Bar(
x=d['inter'].values,
y=d['planned'].values / 1e3,
name='Planned Charging',
showlegend=True,
marker_color='rgb(0,204,153)'
), col=1, row=row)
fig.add_trace(go.Bar(
x=d['inter'].values,
y=d['final'].values / 1e3,
name='Final Charging',
showlegend=True,
marker_color='rgb(0,102,153)'
), col=1, row=row)
fig.add_trace(go.Scatter(
x=d['inter'].values,
y=prc,
mode='lines',
name='DayAhead Price',
line=dict(color='rgb(204,0,0)')
), col=1, row=row, secondary_y=True)
# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_yaxes(title_text="Mean Charging Power [MW]",
secondary_y=False,
showgrid=True,
range=[0, 45],
gridwidth=0.1,
gridcolor='rgba(0, 0, 0, 0.5)')
fig.update_yaxes(title_text="Mean Market Price [ct/kWh]",
secondary_y=True,
showgrid=False,
gridwidth=0.1,
range=[0, 45],
gridcolor='rgba(0, 0, 0, 0.5)')
# fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.update_layout(legend=dict(orientation="h", yanchor="bottom", y=-0.1, xanchor="right", x=1))
fig.update_layout(font=FONT, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
boxmode='group', barmode='group', bargap=0.005)
fig.write_html(f'test.html')
def get_price_sensitivities():
senses = []
for slope in [1.0, 1.7, 2.7, 4.0]:
X = 100/slope
sens = [40*np.exp(-x/X)/X for x in np.arange(0.1, 100.1, 0.1)]
# sens -= min(sens)
senses += [np.asarray(sens)]
return senses
def create_price_sensitivity_plot():
price = get_prices()
prc = price.sort_values('price', ascending=False).values.flatten() + 0.11
sens = get_price_sensitivities()
fig = make_subplots(rows=1, cols=1, specs=[[{"secondary_y": True}]], shared_xaxes=True)
for s, name in zip(sens, [1.0, 1.7, 2.7, 4.0]):
fig.add_trace(go.Scatter(
x=np.linspace(0, 100, len(s)),
y=100 * s,
mode='lines',
name=f'Sensitivity {name}',
line=dict(color=COLORS_SENS[name])
), col=1, row=1, secondary_y=True)
fig.add_trace(go.Scatter(
x=np.linspace(0, 100, len(prc)),
y=prc,
mode='lines',
name=f'DayAhead Price',
line=dict(color='rgb(204,0,0)')
), col=1, row=1, secondary_y=False)
# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_yaxes(title_text="Price [ct/kWh]",
showgrid=True,
gridwidth=0.1,
range=[0, 90],
gridcolor='rgba(0, 0, 0, 0.5)')
fig.update_yaxes(title_text="Marginal utility [ct/dSoC]",
showgrid=False,
secondary_y=True,
gridwidth=0.1,
range=[0, 90],
gridcolor='rgba(0, 0, 0, 0.5)')
fig.update_xaxes(title_text="SoC [%]",
showgrid=True,
gridwidth=0.1,
gridcolor='rgba(0, 0, 0, 0.5)')
# fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.update_layout(legend=dict(orientation="h", yanchor="bottom", y=-0.1, xanchor="right", x=1))
fig.update_layout(font=FONT, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig.write_html(f'sensitivities.html')
if __name__ == "__main__":
pass
# create_scatter_box_plot()
# create_shifted_charging()
# create_price_sensitivity_plot()
case_a = {
50: get_car_charging_at_quarters(scenario='EV100PV50PRC40.0STR-S'),
80: get_car_charging_at_quarters(scenario='EV100PV80PRC40.0STR-S'),
100: get_car_charging_at_quarters(scenario='EV100PV100PRC40.0STR-S')
}
legend = []
for key, value in case_a.items():
plt.plot(value['final'].values)
legend.append(key)
plt.legend = legend
plt.show()
case_b = {
50: get_car_charging_at_quarters(scenario='EV100PV50PRC40.0STR-SPV'),
80: get_car_charging_at_quarters(scenario='EV100PV80PRC40.0STR-SPV'),
100: get_car_charging_at_quarters(scenario='EV100PV100PRC40.0STR-SPV')
}
legend = []
for key, value in case_b.items():
plt.plot(value['final'].values)
legend.append(key)
plt.legend = legend
plt.show()