def save_net(best_net, path: str, format_='pickle'):
""" Save pandapower network to some format. """
filename = 'best_net'
if format_ == 'pickle':
pp.to_pickle(best_net, path + filename + '.p')
elif format_ == 'json':
pp.to_json(best_net, path + filename + '.json')
else:
print(f'File format "{format_}" not implemented yet!')
def deploiement_EV_freqreg(net,
dic_param_trajets,
profil_mob,
dic_nblois,
dic_tranchlois,
dic_parklois,
dic_dureelois,
dic_retourdom,
df_freq,
taux_penet=0.30,
p_evse_mw=0.01,
k_flotte=False):
try:
if k_flotte == True:
raise UserWarning("")
net_flotte = pp.from_pickle(
os.path.join("construction_reseau", "data", "flotte.p"))
net.storage = net_flotte.storage.copy()
for control in net_flotte.controller.iterrows():
control[1].controller.net = net
net_flotte.controller['name'] = net_flotte.controller.controller.apply(
lambda x: str(x))
net.controller = pd.concat([
net.controller, net_flotte.controller[
net_flotte.controller.name.str.contains('EV')].drop(['name'],
axis=1)
],
ignore_index=True)
except UserWarning:
print("Calcul de la flotte...")
nb_ev_fin = 0
for x in net.load.itertuples():
bus = x[2]
p_noeud = x[3]
nb_ev_max = int(p_noeud / 0.02)
for i in range(nb_ev_max):
if rand() <= taux_penet:
per = init_personne(dic_param_trajets, profil_mob,
dic_nblois, dic_tranchlois,
dic_parklois, dic_dureelois,
dic_retourdom)
if rand() <= 0.2:
def_EV_QReg(net, bus, per.creer_df(), per)
else:
def_EV(net, bus,
pd.concat([per.creer_df(), df_freq], axis=1),
per)
nb_ev_fin += 1
pp.to_pickle(net,
os.path.join("construction_reseau", "data", "flotte.p"))
print(nb_ev_fin)
def execute(app,
path_src,
path_dst,
pv_as_slack,
scale_feeder_loads=False,
var_load='plini',
var_gen='pgini',
flag_graphics='GPS'):
"""
Executes import of a .dgs file, runs load flow, and exports net as .p
Args:
path_src: full path to the input .dgs file
path_dst: full path to the result .p file
pv_as_slack: whether "PV" nodes are to be imported as "Slack
scale_feeder_loads: whether loads are to be scaled according to feeder scaling factor
Returns: net
"""
logger.debug('started')
echo_off(app)
prj = import_project(path_src, app)
logger.info('activating project')
prj.Activate()
trafo_name, trafo_desc = check_network(app)
logger.info('gathering network elements')
dict_net = create_network_dict(app, flag_graphics=flag_graphics)
run_load_flow(app, scale_feeder_loads, gen_scaling=0)
logger.info('exporting network to pandapower')
app.SetAttributeModeInternal(1)
net = from_pf(dict_net,
pv_as_slack=pv_as_slack,
pf_variable_p_loads=var_load,
pf_variable_p_gen=var_gen,
flag_graphics=flag_graphics)
app.SetAttributeModeInternal(0)
logger.info(net)
prj.Deactivate()
echo_on(app)
pp.to_pickle(net, path_dst)
return net, trafo_name, trafo_desc
def test_pickle(net_in, tmp_path):
filename = os.path.abspath(str(tmp_path)) + "testfile.p"
pp.to_pickle(net_in, filename)
net_out = pp.from_pickle(filename)
assert_net_equal(net_in, net_out)
def test_pickle(net_in, tempdir):
filename = os.path.join(tempdir, "testfile.p")
pp.to_pickle(net_in, filename)
net_out = pp.from_pickle(filename)
assert_net_equal(net_in, net_out)
def test_pickle():
net_in = create_test_network()
pp.to_pickle(net_in, "testfile.p")
net_out = pp.from_pickle("testfile.p")
assert_net_equal(net_in, net_out)
os.remove('testfile.p')