def test_ctrl_unconverged(net):
output_writer = OutputWriter(net, time_steps=[0, 1])
class DivergentController(Controller):
def __init__(self, net):
super().__init__(net)
def time_step(self, time):
self.convergent = True if time % 2 == 0 else False
def is_converged(self):
return self.convergent
DivergentController(net)
with pytest.raises(ct.ControllerNotConverged):
run_timeseries(net,
time_steps=range(0, 3),
output_writer=output_writer,
max_iter=3)
# assert no exceptions but appropriate output in outputwriter
run_timeseries(net,
time_steps=range(0, 3),
output_writer=output_writer,
max_iter=3,
continue_on_divergence=True)
for i, out in enumerate(
output_writer.output["Parameters"].controller_unstable):
if i % 2 == 0:
assert not out
else:
assert out
def init_default_outputwriter(net, time_steps, **kwargs):
"""
Initializes the output writer. If output_writer is None, default output_writer is created
INPUT:
**net** - The pandapower format network
**time_steps** (list) - time steps to be calculated
"""
output_writer = kwargs.get("output_writer", None)
if output_writer is not None:
# write the output_writer to net
logger.warning("deprecated: output_writer should not be given to run_timeseries(). "
"This overwrites the stored one in net.output_writer.")
net.output_writer.iat[0, 0] = output_writer
if "output_writer" not in net or net.output_writer.iat[0, 0] is None:
# create a default output writer for this net
ow = OutputWriter(net, time_steps, output_path=tempfile.gettempdir())
logger.info("No output writer specified. Using default:")
logger.info(ow)
def init_default_outputwriter(net, time_steps, **kwargs):
"""
Creates a default output writer for the time series calculation.
:param net: The pandapipes format network
:type net: pandapipesNet
:param time_steps: Time steps to calculate as list
:type time_steps: list
:return: ow - The default output writer
:rtype: pandapower.timeseries.output_writer.OutputWriter
"""
output_writer = kwargs.get("output_writer", None)
if output_writer is not None:
# write the output_writer to net
logger.warning(
"deprecated: output_writer should not be given to run_timeseries(). "
"This overwrites the stored one in net.output_writer.")
net.output_writer.iat[0, 0] = output_writer
if "output_writer" not in net or net.output_writer.iat[0, 0] is None:
ow = OutputWriter(net,
time_steps,
output_path=tempfile.gettempdir(),
log_variables=[])
ow.log_variable('res_sink', 'mdot_kg_per_s')
ow.log_variable('res_source', 'mdot_kg_per_s')
ow.log_variable('res_ext_grid', 'mdot_kg_per_s')
ow.log_variable('res_pipe', 'v_mean_m_per_s')
ow.log_variable('res_junction', 'p_bar')
ow.log_variable('res_junction', 't_k')
logger.info("No output writer specified. Using default:")
logger.info(ow)
def get_default_output_writer_ppipe(net, timesteps):
"""
Creates a default output writer for the time series calculation.
:param net: The pandapipes format network
:type net: pandapipesNet
:param timesteps: timesteps to calculate as list
:type timesteps: list
:return: output_writer - The default output_writer
:rtype: ?
"""
ow = OutputWriter(net,
timesteps,
output_path=tempfile.gettempdir(),
log_variables=[])
ow.log_variable('res_sink', 'mdot_kg_per_s')
ow.log_variable('res_source', 'mdot_kg_per_s')
ow.log_variable('res_ext_grid', 'mdot_kg_per_s')
ow.log_variable('res_pipe', 'v_mean_m_per_s')
ow.log_variable('res_junction', 'p_bar')
ow.log_variable('res_junction', 't_k')
return ow
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)