Dear reader,

I am trying to optimize a simple model with oemof-solph, using flexible energy tariffs. I have implemented the most basic situation I could think of where the energy prices follow a sinusoidal trend, there is a battery that charges when energy is cheap and feeds back in to the grid when energy is expensive, while meeting demand.

The battery however does not charge.

I have created energy systems where the battery charges, but I could not get it to work with flexible tariffs. I am lost. Does anybody understand what I am doing wrong?

See example code below (and note vertical scroll bar):

```
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from oemof.solph import Sink, Source, Transformer, Bus, Flow, Model, EnergySystem, processing, views, GenericStorage
from oemof_visio import ESGraphRenderer
### initialize and provide data
datetimeindex = pd.date_range('1/1/2016', periods=24*10, freq='H')
filename = 'oemof/input_data.csv'
filename = os.path.join(os.getcwd(), filename)
data = pd.read_csv(filename, sep=",")
def negative(positive_list):
return [-elem for elem in positive_list]
energy_prices = 0.50 + np.sin(2 * np.pi / 24 * np.linspace(0, len(datetimeindex), len(datetimeindex)))/2
energy_prices = energy_prices.tolist()
energysystem = EnergySystem(timeindex=datetimeindex)
### electricity
electricityBus = Bus(label='electricityBus')
energysystem.add(electricityBus)
### grid
energysystem.add(Source(label='grid', outputs={electricityBus: Flow(variable_cost=energy_prices)}))
### feed in, variable cost set to negative energy prices to get the full price of electricity as feed in tariff
energysystem.add(Sink(label='excessElectricity', inputs={electricityBus: Flow(nominal_value=10, variable_cost=negative(energy_prices))}))
### battery
energysystem.add(GenericStorage(
label='battery',
inputs={electricityBus: Flow(nominal_value=200, variable_cost=0)},
outputs={electricityBus: Flow(nominal_value=10, variable_cost=0)},
nominal_storage_capacity=1000,
initial_storage_level=0.1,
balanced=False,
))
### demands (electricity)
energysystem.add(Sink(label='demandElectricity', inputs={electricityBus: Flow(nominal_value=85, fix=data['demandElectricity'])}))
graph_renderer = ESGraphRenderer(energysystem)
graph_renderer.view()
pd.options.plotting.backend = "plotly"
def transform_multi_col_name(col_names):
return [f'{col_name[0][0]} {col_name[0][1]}' for col_name in col_names]
def plotAll(data, ylabel='value', solver='GLPK'):
data.columns = transform_multi_col_name(data.columns)
data["EnergyPricesTimes100"] = np.asarray(energy_prices)*100
for k, v in energysystem.results[solver].items():
data["Storage"] = v['sequences']
break
fig = data.plot()
fig.update_layout(yaxis_title=ylabel)
fig.show()
### create a glpk optimization model based on energy_system
glpkModel = Model(energysystem=energysystem)
glpkModel.solve(solver='glpk', solve_kwargs={'tee': True, 'keepfiles': False})
energysystem.results['GLPK'] = processing.results(glpkModel)
energysystem.results['GLPKmeta'] = processing.meta_results(glpkModel)
glpkElectricityResults = views.node(energysystem.results['GLPK'], 'electricityBus')
glpkDataElectricity = glpkElectricityResults['sequences']
plotAll(glpkDataElectricity, ylabel='Watt', solver='GLPK')
```

# input data

```
### The input data looks as follows:
demandThermal,demandElectricity,solar,wind
0.1469452338,0.6137606201,0.00E+000,2.78E-001
0.1500444621,0.6137606201,0.00E+000,2.81E-001
0.1569258683,0.5575605123,0.00E+000,2.83E-001
0.1740503842,0.5317384943,0.00E+000,2.83E-001
0.2175136794,0.5198798737,0.00E+000,2.52E-001
0.2993166845,0.4987936035,0.00E+000,1.86E-001
0.3541620446,0.4596755564,0.00E+000,1.86E-001
0.3316082816,0.456313047,0.00E+000,1.85E-001
0.3062301219,0.4653995226,9.10E-003,1.85E-001
```

etc…

**Thanks in advance!**