"""
This script shows how to use the flixopt framework to model a simple energy system.
"""
import numpy as np
import pandas as pd
from rich.pretty import pprint # Used for pretty printing
import flixopt as fx
if __name__ == '__main__':
# --- Create Time Series Data ---
# Heat demand profile (e.g., kW) over time and corresponding power prices
heat_demand_per_h = np.array([30, 0, 90, 110, 110, 20, 20, 20, 20])
power_prices = 1 / 1000 * np.array([80, 80, 80, 80, 80, 80, 80, 80, 80])
# Create datetime array starting from '2020-01-01' for the given time period
timesteps = pd.date_range('2020-01-01', periods=len(heat_demand_per_h), freq='h')
flow_system = fx.FlowSystem(timesteps=timesteps)
# --- Define Energy Buses ---
# These represent nodes, where the used medias are balanced (electricity, heat, and gas)
flow_system.add_elements(fx.Bus(label='Strom'), fx.Bus(label='Fernwärme'), fx.Bus(label='Gas'))
# --- Define Effects (Objective and CO2 Emissions) ---
# Cost effect: used as the optimization objective --> minimizing costs
costs = fx.Effect(
label='costs',
unit='€',
description='Kosten',
is_standard=True, # standard effect: no explicit value needed for costs
is_objective=True, # Minimizing costs as the optimization objective
)
# CO2 emissions effect with an associated cost impact
CO2 = fx.Effect(
label='CO2',
unit='kg',
description='CO2_e-Emissionen',
specific_share_to_other_effects_operation={costs.label: 0.2},
maximum_operation_per_hour=1000, # Max CO2 emissions per hour
)
# --- Define Flow System Components ---
# Boiler: Converts fuel (gas) into thermal energy (heat)
boiler = fx.linear_converters.Boiler(
label='Boiler',
eta=0.5,
Q_th=fx.Flow(label='Q_th', bus='Fernwärme', size=50, relative_minimum=0.1, relative_maximum=1),
Q_fu=fx.Flow(label='Q_fu', bus='Gas'),
)
# Combined Heat and Power (CHP): Generates both electricity and heat from fuel
chp = fx.linear_converters.CHP(
label='CHP',
eta_th=0.5,
eta_el=0.4,
P_el=fx.Flow('P_el', bus='Strom', size=60, relative_minimum=5 / 60),
Q_th=fx.Flow('Q_th', bus='Fernwärme'),
Q_fu=fx.Flow('Q_fu', bus='Gas'),
)
# Storage: Energy storage system with charging and discharging capabilities
storage = fx.Storage(
label='Storage',
charging=fx.Flow('Q_th_load', bus='Fernwärme', size=1000),
discharging=fx.Flow('Q_th_unload', bus='Fernwärme', size=1000),
capacity_in_flow_hours=fx.InvestParameters(fix_effects=20, fixed_size=30, optional=False),
initial_charge_state=0, # Initial storage state: empty
relative_maximum_charge_state=1 / 100 * np.array([80, 70, 80, 80, 80, 80, 80, 80, 80, 80]),
eta_charge=0.9,
eta_discharge=1, # Efficiency factors for charging/discharging
relative_loss_per_hour=0.08, # 8% loss per hour. Absolute loss depends on current charge state
prevent_simultaneous_charge_and_discharge=True, # Prevent charging and discharging at the same time
)
# Heat Demand Sink: Represents a fixed heat demand profile
heat_sink = fx.Sink(
label='Heat Demand',
sink=fx.Flow(label='Q_th_Last', bus='Fernwärme', size=1, fixed_relative_profile=heat_demand_per_h),
)
# Gas Source: Gas tariff source with associated costs and CO2 emissions
gas_source = fx.Source(
label='Gastarif',
source=fx.Flow(label='Q_Gas', bus='Gas', size=1000, effects_per_flow_hour={costs.label: 0.04, CO2.label: 0.3}),
)
# Power Sink: Represents the export of electricity to the grid
power_sink = fx.Sink(
label='Einspeisung', sink=fx.Flow(label='P_el', bus='Strom', effects_per_flow_hour=-1 * power_prices)
)
# --- Build the Flow System ---
# Add all defined components and effects to the flow system
flow_system.add_elements(costs, CO2, boiler, storage, chp, heat_sink, gas_source, power_sink)
# Visualize the flow system for validation purposes
flow_system.plot_network(show=True)
# --- Define and Run Calculation ---
# Create a calculation object to model the Flow System
calculation = fx.FullCalculation(name='Sim1', flow_system=flow_system)
calculation.do_modeling() # Translate the model to a solvable form, creating equations and Variables
# --- Solve the Calculation and Save Results ---
calculation.solve(fx.solvers.HighsSolver(mip_gap=0, time_limit_seconds=30))
# --- Analyze Results ---
calculation.results['Fernwärme'].plot_node_balance_pie()
calculation.results['Fernwärme'].plot_node_balance()
calculation.results['Storage'].plot_node_balance()
calculation.results.plot_heatmap('CHP(Q_th)|flow_rate')
# Convert the results for the storage component to a dataframe and display
df = calculation.results['Storage'].node_balance_with_charge_state()
print(df)
# Save results to file for later usage
calculation.results.to_file()