freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
# The bus objects were assigned to variables which makes it easier to connect
# components to these buses (see below).
## create (commodity (fuels + electricity in OSeMOSYS) busses Brandenburg & Berlin
# Brandenburg
GAS = solph.Bus(label="GAS")
## adding the buses to the energy system
#Brandenburg
ELECTRICITY = solph.Bus(label="ELECTRICITY")
energysystem.add(GAS, ELECTRICITY)
## create sources (fuel import technologies in OSeMOSYS) to represent the fuel input (annual limit)
#Brandenburg
energysystem.add(solph.Source(label='GAS_IMPORT', outputs={GAS: solph.Flow()}))
## create excess component for the electricity bus to allow overproduction
#Brandenburg
#energysystem.add(solph.Sink(label='excess_BBEL_FIN', inputs={BBEL_FIN: solph.Flow()}))
import pandas as pd
from oemof import solph
from oemof.solph import constraints
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
# create energy system
energysystem = solph.EnergySystem(
timeindex=pd.date_range("1/1/2012", periods=3, freq="H"))
# create gas bus
bgas = solph.Bus(label="gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# adding the buses to the energy system
energysystem.add(bel, bgas)
# create fixed source object representing biomass plants
energysystem.add(
solph.Source(
label="biomass",
outputs={
bel:
solph.Flow(
nominal_value=100,
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
##########################################################################
# Initialize the energy system and calculate necessary parameters
##########################################################################
periods = 365
time = pd.date_range('1/1/2018', periods=periods, freq='D')
es = solph.EnergySystem(timeindex=time)
b_heat = solph.Bus(label='b_heat')
es.add(b_heat)
def heat_demand(d):
"""basic model for heat demand, solely based on the day of the year"""
return 0.6 + 0.4 * np.cos(2 * np.pi * d / 356)
def solar_thermal(d):
"""
basic model for solar thermal yield, solely based on the day of the year
"""
return 0.5 - 0.5 * np.cos(2 * np.pi * d / 356)
# regular oemof_system #
# parameters for energy system
eta_losses = 0.8
elec_consumption = 0.05
backup_costs = 1000
cap_loss = 0.02
conversion_storage = 0.95
costs_storage = economics.annuity(20, 20, 0.06)
costs_electricity = 1000
conversion_factor_turbine = 0.4
size_collector = 1000
# busses
bth = solph.Bus(label='thermal', balanced=True)
bel = solph.Bus(label='electricity')
bcol = solph.Bus(label='solar')
#sources and sinks
col_heat = solph.Source(label='collector_heat',
outputs={
bcol:
solph.Flow(
fixed=True,
actual_value=data_precalc['collector_heat'],
nominal_value=size_collector)
})
el_grid = solph.Source(
label='grid', outputs={bel: solph.Flow(variable_costs=costs_electricity)})
def storage_example():
# read time series
timeseries = pd.read_csv(os.path.join(os.getcwd(), "storage_data.csv"))
# create an energy system
idx = pd.date_range("1/1/2017", periods=len(timeseries), freq="H")
es = solph.EnergySystem(timeindex=idx)
for data_set in DATA:
name = data_set["name"]
# power bus
bel = solph.Bus(label="bel_{0}".format(name))
es.add(bel)
es.add(
solph.Source(
label="source_el_{0}".format(name),
outputs={
bel: solph.Flow(variable_costs=PARAMETER["el_price"])
},
))
es.add(
solph.Source(
label="pv_el_{0}".format(name),
outputs={
bel: solph.Flow(fix=timeseries["pv_el"], nominal_value=1)
},
))
es.add(
solph.Sink(
label="demand_el_{0}".format(name),
inputs={
bel: solph.Flow(fix=timeseries["demand_el"],
nominal_value=1)
},
))
es.add(
solph.Sink(
label="shunt_el_{0}".format(name),
inputs={bel: solph.Flow(variable_costs=PARAMETER["sh_price"])},
))
# Electric Storage
es.add(
solph.components.GenericStorage(
label="storage_elec_{0}".format(name),
nominal_storage_capacity=PARAMETER["nominal_storage_capacity"],
inputs={bel: solph.Flow()},
outputs={bel: solph.Flow()},
initial_storage_level=data_set["initial_storage_level"],
balanced=data_set["balanced"],
))
# create an optimization problem and solve it
om = solph.Model(es)
# solve model
om.solve(solver="cbc")
# create result object
results = solph.processing.results(om)
flows = [x for x in results if x[1] is not None]
components = [x for x in results if x[1] is None]
storage_cap = pd.DataFrame()
costs = pd.Series(dtype=float)
balance = pd.Series(dtype=float)
for flow in [x for x in flows if "source_el" in x[0].label]:
name = "_".join(flow[0].label.split("_")[2:])
print(name, float(results[flow]["sequences"].sum()))
costs[name] = float(results[flow]["sequences"].sum() *
PARAMETER["el_price"])
for flow in [x for x in flows if "shunt_el" in x[1].label]:
name = "_".join(flow[1].label.split("_")[2:])
costs[name] += float(results[flow]["sequences"].sum() *
PARAMETER["sh_price"])
storages = [x[0] for x in components if "storage" in x[0].label]
idx = results[storages[0], None]["sequences"]["storage_content"].index
last = idx[-1]
prev = idx[0] - 1 * idx.freq
for s in storages:
name = s.label
storage_cap[name] = results[s, None]["sequences"]["storage_content"]
storage_cap.loc[prev, name] = results[s,
None]["scalars"]["init_content"]
balance[name] = (storage_cap.loc[last][name] -
storage_cap.loc[prev][name])
if plt is not None:
storage_cap.plot(drawstyle="steps-mid", subplots=False, sharey=True)
storage_cap.plot(drawstyle="steps-mid", subplots=True, sharey=True)
costs.plot(kind="bar", ax=plt.subplots()[1], rot=0)
balance.index = [
"balanced",
"balanced_None",
"unbalanced",
"unbalanced_None",
]
balance.plot(
kind="bar",
linewidth=1,
edgecolor="#000000",
rot=0,
ax=plt.subplots()[1],
)
plt.show()
print(storage_cap)
print(costs)
print(balance)