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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Multisectoral energy system with oemof
General description
Step1:  Import input data
Step2:  Add entities to energy system
Step4:  Optimize energy system and plot results
Step5:  Adding the gas sector
In order to add a gas power plant, a gas ressource bus is needed. The gas power plant connects the gas and electricity busses and thereby couples the gas and electricity sector.
Step6:  Adding the heat sector
The heat sector is added and coupled to the electricity sector similarly to the gas sector. The same component, the LinearTransformer, is used to couple the two sectors. Only through its parametrisation it becomes a heater rod or a heat pump.
Step7:  Adding a heat pump
There are different ways to model a heat pump. Here the approach of precalculating a COP and using this as a conversion factor for the LinearTransformer is used. Another approach is to use the LinearN1Transformer that has two inputs - electricity and heat from a heat source. See the solph example "simple_dispatch".
Step8:  Adding a combined heat and power plant
The combined heat and power plant couples the gas, electricity and heat sector.
Step9:  Adding the mobility sector
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Python Code:
from oemof.solph import EnergySystem
import pandas as pd
# initialize energy system
energysystem = EnergySystem(timeindex=pd.date_range('1/1/2016', 
                                                    periods=168, 
                                                    freq='H'))
Explanation: Multisectoral energy system with oemof
General description:
The jupyter notebook gives a simple example of how to couple the sectors power, heat and mobility.
Installation requirements:
This example requires the latest oemof version and jupyter. Install by:
pip install oemof jupyter
Create a simple energy system
Initialize energy system
End of explanation
# import example data with scaled demands and feedin timeseries of renewables 
# as dataframe
data = pd.read_csv("data/example_data.csv", sep=",", index_col='timeindex', parse_dates=['timeindex'])
Explanation: Import input data
End of explanation
from oemof.solph import Bus, Flow, Sink, Source, Transformer
### BUS
# create electricity bus
b_el = Bus(label="b_el")
energysystem.add(b_el)
# add excess sink to help avoid infeasible problems
energysystem.add(Sink(label="excess_el",
                      inputs={b_el: Flow()}))
energysystem.add(Source(label="shortage_el",
                        outputs={b_el: Flow(variable_costs=1000)}))
### DEMAND
# add electricity demand
energysystem.add(Sink(label="demand_el",
                      inputs={b_el: Flow(nominal_value=85,
                                         actual_value=data['demand_el'],
                                         fixed=True)}))
### SUPPLY
# add wind and pv feedin
energysystem.add(Source(label="wind",
                        outputs={b_el: Flow(actual_value=data['wind'],
                                            nominal_value=60,
                                            fixed=True)}))
energysystem.add(Source(label="pv",
       outputs={b_el: Flow(actual_value=data['pv'],
                           nominal_value=200,
                           fixed=True)}))
Explanation: Add entities to energy system
End of explanation
from oemof.solph import Model
from oemof.outputlib import processing, views
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def optimize(energysystem):
    
    ### optimize
    # create operational model
    om = Model(energysystem=energysystem)
    # solve using the cbc solver
    om.solve(solver='cbc',
             solve_kwargs={'tee': False})
    # save LP-file
    om.write('sector_coupling.lp', io_options={'symbolic_solver_labels': True})
    # generic result object
    #results = processing.results(om=om)
    return om
def plot(om, bus_label):
    
    Plots...
    Parameters
    ----------
    om : oemof.solph.models.OperationalModel
    bus_label : String
        Label of bus to be plotted.
    
    
    #ToDo: remove this once #387 is solved
    def get_flows_to_and_from_bus(node_results_flows):
        "Function to devide flows into ingoing and outgoing flows."
        flows = list(node_results_flows.columns)
        to_flows = []
        from_flows = []
        for flow in flows:
            if flow[0][0] == bus_label:
                from_flows.append(flow)
            elif flow[0][1] == bus_label:
                to_flows.append(flow)
            else:
                print("{} is neither from nor to bus.".format(flow))
        return (to_flows, from_flows)
    # node_results is a dictionary keyed by 'scalars' and 'sequences'
    # holding respective data in a pandas Series and DataFrame.
    node_results = views.node(om.results(), bus_label)
    node_results_flows = node_results['sequences']
    to_flows, from_flows = get_flows_to_and_from_bus(node_results_flows)
    # set up plot
    f, (ax1, ax2) = plt.subplots(2, sharex=True, sharey=True)
    node_results_flows[to_flows].plot(kind='bar', stacked=True, ax=ax1)
    node_results_flows[from_flows].plot(kind='bar', stacked=True, ax=ax2)
    ax1.set_title('Flows to and from {}'.format(bus_label))
    ax1.legend(loc='upper right', bbox_to_anchor=(1.7, 1.0))
    ax2.legend(loc='upper right', bbox_to_anchor=(1.7, 1.0))
    ax1.set_ylabel('Power')
    ax2.set_xlabel('Time')
    ax2.set_ylabel('Power')
    
    # set x-tick-labels
    dates = node_results_flows.index
    tick_distance = int(len(dates) / 7) - 1
    ax2.set_xticks(range(0, len(dates), tick_distance), minor=False)
    ax2.set_xticklabels([item.strftime('%d-%m-%Y')
         for item in dates.tolist()[0::tick_distance]],
         rotation=90, minor=False)
    plt.show()
    
om = optimize(energysystem)
plot(om, 'b_el')
Explanation: Optimize energy system and plot results
End of explanation
# add gas bus
b_gas = Bus(label="b_gas",
            balanced=False)
energysystem.add(b_gas)
# add gas power plant
energysystem.add(Transformer(label="pp_gas",
                             inputs={b_gas: Flow(summed_max_flow=200)},
                             outputs={b_el: Flow(nominal_value=40,
                                                 variable_costs=40)},
                             conversion_factors={b_el: 0.50}));
om = optimize(energysystem)
plot(om, 'b_el')
Explanation: Adding the gas sector
In order to add a gas power plant, a gas ressource bus is needed. The gas power plant connects the gas and electricity busses and thereby couples the gas and electricity sector.
End of explanation
# add heat bus
b_heat = Bus(label="b_heat",
             balanced=True)
energysystem.add(b_heat)
# add heat demand
energysystem.add(Sink(label="demand_th",
                      inputs={b_heat: Flow(nominal_value=60,
                                           actual_value=data['demand_th'],
                                           fixed=True)}))
# add heater rod
energysystem.add(Transformer(label="heater_rod",
                             inputs={b_el: Flow()},
                             outputs={b_heat: Flow(variable_costs=10)},
                             conversion_factors={b_heat: 0.98}));
om = optimize(energysystem)
plot(om, 'b_el')
plot(om, 'b_heat')
Explanation: Adding the heat sector
The heat sector is added and coupled to the electricity sector similarly to the gas sector. The same component, the LinearTransformer, is used to couple the two sectors. Only through its parametrisation it becomes a heater rod or a heat pump.
End of explanation
# COP can be calculated beforehand, assuming the heat reservoir temperature 
# is infinite random timeseries for COP
import numpy as np
COP = np.random.uniform(low=3.0, high=5.0, size=(168,))
# add heater rod
#Transformer(label="heater_rod",
#            inputs={b_el: Flow()},
#            outputs={b_heat: Flow(variable_costs=10)},
#            conversion_factors={b_heat: 0.98});
# add heat pump
energysystem.add(Transformer(label="heat_pump",
                             inputs={b_el: Flow()},
                             outputs={b_heat: Flow(nominal_value=20,
                                                   variable_costs=10)},
                             conversion_factors={b_heat: COP}));
om = optimize(energysystem)
plot(om, 'b_heat')
Explanation: Adding a heat pump
There are different ways to model a heat pump. Here the approach of precalculating a COP and using this as a conversion factor for the LinearTransformer is used. Another approach is to use the LinearN1Transformer that has two inputs - electricity and heat from a heat source. See the solph example "simple_dispatch".
End of explanation
# add CHP with fixed ratio of heat and power (back-pressure turbine)
energysystem.add(Transformer(label='pp_chp',
                             inputs={b_gas: Flow()},
                             outputs={b_el: Flow(nominal_value=30,
                                                 variable_costs=42),
                                      b_heat: Flow(nominal_value=40)},
                             conversion_factors={b_el: 0.3,
                                                 b_heat: 0.4}));
from oemof.solph.components import ExtractionTurbineCHP
# add CHP with variable ratio of heat and power (extraction turbine)
energysystem.add(ExtractionTurbineCHP(label='pp_chp_extraction',
                                      inputs={b_gas: Flow()},
                                      outputs={b_el: Flow(nominal_value=30,
                                                          variable_costs=42),
                                               b_heat: Flow(nominal_value=40)},
                                      conversion_factors={b_el: 0.3,
                                                          b_heat: 0.4},
                                      conversion_factor_full_condensation={b_el: 0.5}));
om = optimize(energysystem)
plot(om, 'b_el')
om = optimize(energysystem)
plot(om, 'b_heat')
Explanation: Adding a combined heat and power plant
The combined heat and power plant couples the gas, electricity and heat sector.
End of explanation
from oemof.solph.components import GenericStorage as Storage
charging_power = 20
bev_battery_cap = 50
# add mobility bus
b_bev = Bus(label="b_bev",
            balanced=True)
energysystem.add(b_bev)
# add transformer to transport electricity from grid to mobility sector
energysystem.add(Transformer(label="transport_el_bev",
                             inputs={b_el: Flow()},
                             outputs={b_bev: Flow(variable_costs=10,
                                                  nominal_value=charging_power,
                                                  max=data['bev_charging_power'])},
                             conversion_factors={b_bev: 1.0}))
    
# add BEV storage
energysystem.add(Storage(label='bev_storage',
                 inputs={b_bev: Flow()},
                 outputs={b_bev: Flow()},
                 nominal_capacity=bev_battery_cap,
                 capacity_min=data['bev_cap_min'],
                 capacity_max=data['bev_cap_max'],
                 capacity_loss=0.00,
                 initial_capacity=None,
                 inflow_conversion_factor=1.0,
                 outflow_conversion_factor=1.0,
                 nominal_input_capacity_ratio=1.0,
                 nominal_output_capacity_ratio=1.0))
# add sink for leaving vehicles
energysystem.add(Sink(label="leaving_bev",
                      inputs={b_bev: Flow(nominal_value=bev_battery_cap,
                                          actual_value=data['bev_sink'],
                                          fixed=True)}))
# add source for returning vehicles
energysystem.add(Source(label="returning_bev",
                        outputs={b_bev: Flow(nominal_value=bev_battery_cap,
                                             actual_value=data['bev_source'],
                                             fixed=True)}));
om = optimize(energysystem)
plot(om, 'b_bev')
plot(om, 'b_el')
Explanation: Adding the mobility sector
End of explanation
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