Sizing¶

import numpy as np
import pandas as pd
import plotly.express as px
import xarray as xr

import flixopt as fx

fx.CONFIG.notebook()

# One week in summer, hourly
timesteps = pd.date_range('2024-07-15', periods=168, freq='h')
hours = np.arange(168)
hour_of_day = hours % 24

# Solar radiation profile (kW/m² equivalent, simplified)
# Peak around noon, zero at night
solar_profile = np.maximum(0, np.sin((hour_of_day - 6) * np.pi / 12)) * 0.8
solar_profile = np.where((hour_of_day >= 6) & (hour_of_day <= 20), solar_profile, 0)

# Add some cloud variation
np.random.seed(42)
cloud_factor = np.random.uniform(0.6, 1.0, len(timesteps))
solar_profile = solar_profile * cloud_factor

# Pool operates 8am-10pm, constant demand when open
pool_demand = np.where((hour_of_day >= 8) & (hour_of_day <= 22), 150, 50)  # kW

print(f'Peak solar: {solar_profile.max():.2f} kW/kW_installed')
print(f'Pool demand: {pool_demand.max():.0f} kW (open), {pool_demand.min():.0f} kW (closed)')

# Visualize profiles with plotly - using xarray and faceting
profiles = xr.Dataset(
    {
        'Solar Profile [kW/kW]': xr.DataArray(solar_profile, dims=['time'], coords={'time': timesteps}),
        'Pool Demand [kW]': xr.DataArray(pool_demand, dims=['time'], coords={'time': timesteps}),
    }
)

# Convert to long format for faceting
df = profiles.to_dataframe().reset_index().melt(id_vars='time', var_name='variable', value_name='value')
fig = px.line(df, x='time', y='value', facet_col='variable', height=300)
fig.update_yaxes(matches=None, showticklabels=True)
fig.for_each_annotation(lambda a: a.update(text=a.text.split('=')[-1]))
fig

# Cost parameters
GAS_PRICE = 0.12  # €/kWh - high gas price makes solar attractive

# Solar collectors: 400 €/kW installed, 20-year lifetime → ~25 €/kW/year annualized
# (simplified, real calculation would include interest rate)
SOLAR_COST_PER_KW = 20  # €/kW/year

# Buffer tank: 50 €/kWh capacity, 30-year lifetime → ~2 €/kWh/year
TANK_COST_PER_KWH = 1.5  # €/kWh/year

# Scale factor: We model 1 week, but costs are annual
# So we scale investment costs to weekly equivalent
WEEKS_PER_YEAR = 52
SOLAR_COST_WEEKLY = SOLAR_COST_PER_KW / WEEKS_PER_YEAR
TANK_COST_WEEKLY = TANK_COST_PER_KWH / WEEKS_PER_YEAR

print(f'Solar cost: {SOLAR_COST_WEEKLY:.3f} €/kW/week')
print(f'Tank cost: {TANK_COST_WEEKLY:.4f} €/kWh/week')

solar_size = flow_system.statistics.sizes['SolarCollectors(Heat)'].item()
tank_size = flow_system.statistics.sizes['BufferTank'].item()

print(
    f'Optimal sizes: Solar {solar_size:.0f} kW, Tank {tank_size:.0f} kWh (ratio: {tank_size / solar_size:.1f} kWh/kW)'
)

flow_system.statistics.plot.sizes()

total_costs = flow_system.solution['costs'].item()

# Calculate cost components
solar_invest = solar_size * SOLAR_COST_WEEKLY
tank_invest = tank_size * TANK_COST_WEEKLY
gas_costs = total_costs - solar_invest - tank_invest

print(
    f'Weekly costs: Solar {solar_invest:.1f}€ ({solar_invest / total_costs * 100:.0f}%) + Tank {tank_invest:.1f}€ ({tank_invest / total_costs * 100:.0f}%) + Gas {gas_costs:.1f}€ ({gas_costs / total_costs * 100:.0f}%) = {total_costs:.1f}€'
)

flow_system.statistics.plot.balance('Heat')

flow_system.statistics.plot.heatmap('SolarCollectors(Heat)')

flow_system.statistics.plot.balance('BufferTank')

# Gas-only scenario
total_demand = pool_demand.sum()
gas_only_cost = total_demand / 0.92 * GAS_PRICE  # All heat from gas boiler

savings = gas_only_cost - total_costs
print(
    f'Solar saves {savings:.1f}€/week ({savings / gas_only_cost * 100:.0f}%) vs gas-only ({gas_only_cost:.1f}€) → {savings * 52:.0f}€/year'
)

flow_system.statistics.plot.sankey.flows()