def test_const_pq_gen_trafo_tap(simple_test_net):
# allows to use recycle = {"bus_pq", "gen", "trafo"}
net = simple_test_net
profiles, _ = create_data_source(n_timesteps)
profiles['ext_grid'] = np.ones(n_timesteps) + np.arange(0,
n_timesteps) * 1e-2
ds = DFData(profiles)
# 1load
c1 = add_const(net, ds, recycle=None)
c2 = add_const(net,
ds,
recycle=None,
profile_name="ext_grid",
variable="vm_pu",
element_index=0,
element="ext_grid")
c3 = ContinuousTapControl(net, 0, 1.01, tol=1e-9)
vm_pu, ll = _run_recycle(net)
del c1, c2, c3
# calculate the same results without recycle
c = add_const(net, ds, recycle=False)
c2 = add_const(net,
ds,
recycle=False,
profile_name="ext_grid",
variable="vm_pu",
element_index=0,
element="ext_grid")
c3 = ContinuousTapControl(net, 0, 1.01, recycle=False, tol=1e-9)
ow = _run_normal(net)
assert np.allclose(vm_pu, ow.output["res_bus.vm_pu"])
assert np.allclose(ll, ow.output["res_line.loading_percent"])
def test_const_gen(simple_test_net):
# allows to use recycle = {"gen"} and fast output read
net = simple_test_net
pp.create_gen(net, 1, p_mw=2.)
profiles, _ = create_data_source(n_timesteps)
profiles['gen'] = np.random.random(n_timesteps) * 2e1
ds = DFData(profiles)
# 1load
c1 = add_const(net, ds, recycle=None)
c2 = add_const(net,
ds,
recycle=None,
profile_name="gen",
element_index=0,
element="gen")
vm_pu, ll = _run_recycle(net)
del c1, c2
# calculate the same results without recycle
c = add_const(net, ds, recycle=False)
c2 = add_const(net,
ds,
recycle=None,
profile_name="gen",
element_index=0,
element="gen")
ow = _run_normal(net)
assert np.allclose(vm_pu, ow.output["res_bus.vm_pu"])
assert np.allclose(ll, ow.output["res_line.loading_percent"])
def test_const_ext_grid(simple_test_net):
# allows to use recycle = {"gen"} and fast output read
net = simple_test_net
profiles, _ = create_data_source(n_timesteps)
profiles['ext_grid'] = np.ones(n_timesteps) + np.arange(0,
n_timesteps) * 1e-2
ds = DFData(profiles)
# 1load
c1 = add_const(net, ds, recycle=None)
c2 = add_const(net,
ds,
recycle=None,
profile_name="ext_grid",
variable="vm_pu",
element_index=0,
element="ext_grid")
vm_pu, ll = _run_recycle(net)
del c1, c2
# calculate the same results without recycle
c = add_const(net, ds, recycle=False)
c2 = add_const(net,
ds,
recycle=False,
profile_name="ext_grid",
variable="vm_pu",
element_index=0,
element="ext_grid")
ow = _run_normal(net)
assert np.allclose(vm_pu, ow.output["res_bus.vm_pu"])
assert np.allclose(ll, ow.output["res_line.loading_percent"])
assert np.allclose(ow.output["res_bus.vm_pu"].values[:, 0],
profiles["ext_grid"])
assert np.allclose(vm_pu.values[:, 0], profiles["ext_grid"])
# net.sgen["controllable"] = False
net["load"].loc[:, "type"] = "residential"
net.sgen.loc[:, "type"] = "pv"
net.sgen.loc[8, "type"] = "wind"
input_file = r"C:\Users\x230\.julia\dev\PandaModels\test\pycodes\cigre_timeseries_15min.json"
time_series = pd.read_json(input_file)
time_series.sort_index(inplace=True)
load_df = pd.DataFrame(time_series["residential"]).dot([net["load"]["p_mw"].values])
ConstControl(net, "load", "p_mw", element_index=net.load.index[net.load.type == "residential"],
profile_name=net.load.index[net.load.type == "residential"], data_source=DFData(load_df))
pv_df = pd.DataFrame(time_series["pv"]).dot([net["sgen"].loc[:7, "p_mw"].values])
ConstControl(net, "sgen", "p_mw", element_index=net.sgen.index[net.sgen.type == "pv"],
profile_name=net.sgen.index[net.sgen.type == "pv"], data_source=DFData(pv_df))
wind_df = pd.DataFrame(time_series["wind"]).dot([net["sgen"].loc[8, "p_mw"]])
ConstControl(net, "sgen", "p_mw", element_index=net.sgen.index[net.sgen.type == "wind"],
profile_name=net.sgen.index[net.sgen.type == "wind"], data_source=DFData(time_series["wind"]))
storage_results = pp.runpm_storage_opf(net, n_timesteps=96, time_elapsed=0.25)
# time_series.sort_values(by="timestep", inplace=True)
import pandapower.control as control
import pandapower.networks as nw
import pandapower.timeseries as timeseries
from pandapower.timeseries.data_sources.frame_data import DFData
# load a pandapower network
net = nw.mv_oberrhein(scenario='generation')
# number of time steps
n_ts = 95
# load your timeseries from a file (here csv file)
# df = pd.read_csv("sgen_timeseries.csv")
# or create a DataFrame with some random time series as an example
df = pd.DataFrame(np.random.normal(1., 0.1, size=(n_ts, len(net.sgen.index))),
index=list(range(n_ts)), columns=net.sgen.index) * net.sgen.p_mw.values
# create the data source from it
ds = DFData(df)
# initialising ConstControl controller to update values of the regenerative generators ("sgen" elements)
# the element_index specifies which elements to update (here all sgens in the net since net.sgen.index is passed)
# the controlled variable is "p_mw"
# the profile_name are the columns in the csv file (here this is also equal to the sgen indices 0-N )
const_sgen = control.ConstControl(net, element='sgen', element_index=net.sgen.index,
variable='p_mw', data_source=ds, profile_name=net.sgen.index)
# do the same for loads
# df = pd.read_csv("load_timeseries.csv")
# create a DataFrame with some random time series as an example
df = pd.DataFrame(np.random.normal(1., 0.1, size=(n_ts, len(net.load.index))),
index=list(range(n_ts)), columns=net.load.index) * net.load.p_mw.values
ds = DFData(df)
const_load = control.ConstControl(net, element='load', element_index=net.load.index,
def test_time_series_p2g_control(get_gas_example, get_power_example_simple):
net_gas = get_gas_example
net_power = get_power_example_simple
pandapipes.create_source(net_gas, 5, 0.003)
pandapipes.create_sink(net_gas, 3, 0.003)
pandapipes.create_sink(net_gas, 4, 0.003)
pandapower.create_load(net_power, 6, 0.004)
pandapower.create_load(net_power, 5, 0.004)
pandapower.create_sgen(net_power, 4, 0.004)
mn = create_empty_multinet('coupled net')
add_nets_to_multinet(mn, power=net_power, gas=net_gas)
data_p2g_power = pd.DataFrame(np.concatenate(
[np.array([1.0] * 5),
np.array([0.75] * 2),
np.array([1.0] * 3)]),
columns=['load_p2g_power'])
data_const_load = pd.DataFrame(np.concatenate(
[np.array([1.2] * 5),
np.array([1.75] * 2),
np.array([1.2] * 3)]),
columns=['load_const'])
data_const_sgen = pd.DataFrame(np.concatenate(
[np.array([0.8] * 5),
np.array([0.9] * 2),
np.array([0.8] * 3)]),
columns=['sgen_const'])
data_const_sink = np.zeros([10, 2])
data_const_sink[:5, :] = [0.6, 0.4]
data_const_sink[5:7, :] = [1.2, 0.3]
data_const_sink[7:, :] = [0.6, 0.4]
data_const_sink = pd.DataFrame(data_const_sink,
columns=['sink_const_0', 'sink_const_1'])
ds = DFData(data_p2g_power)
_, p2g = coupled_p2g_const_control(mn,
0,
0,
0.6,
initial_run=True,
profile_name='load_p2g_power',
data_source=ds)
ds = DFData(data_const_sink)
ConstControl(net_gas,
'sink',
'mdot_kg_per_s', [0, 1],
profile_name=['sink_const_0', 'sink_const_1'],
data_source=ds)
ds = DFData(data_const_load)
ConstControl(net_power,
'load',
'p_mw',
1,
profile_name=['load_const'],
data_source=ds)
ds = DFData(data_const_sgen)
ConstControl(net_power,
'sgen',
'p_mw',
0,
profile_name=['sgen_const'],
data_source=ds)
log_variables = [('res_source', 'mdot_kg_per_s'),
('res_sink', 'mdot_kg_per_s')]
ow_gas = OutputWriter(net_gas, range(10), log_variables=log_variables)
log_variables = [('res_load', 'p_mw'), ('res_sgen', 'p_mw')]
ow_power = OutputWriter(net_power, range(10), log_variables=log_variables)
run_timeseries(mn, range(10))
gas_res = ow_gas.np_results
power_res = ow_power.np_results
assert np.all(gas_res['res_sink.mdot_kg_per_s'] == data_const_sink.values)
assert np.all(gas_res['res_source.mdot_kg_per_s'] == data_p2g_power.values * \
p2g.conversion_factor_mw_to_kgps() * p2g.efficiency)
assert np.all(power_res['res_load.p_mw'][:, 1] == data_const_load.values.T)
assert np.all(power_res['res_sgen.p_mw'] == data_const_sgen.values)