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xml_to_pypsa.py
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xml_to_pypsa.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2018-2019 Christian Brosig (TH Köln)
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
This package uses the xmlimport package to import electrical networks from
PSS Sincal into PyPSA. It also provides functions to test and repair the
network.
"""
import os
import pandas as pd
import numpy as np
import math
import pypsa
import networkx as nx
import pickle
from xmlimport import XMLimport
class ImporterXMLSincal():
"""
This class enables you to import xmls from PSS-Sincal into PyPSA.
It provides functions to read the data in, convert it, test it and also
to repair it if necessary.
Parameters
----------
list_file: (dict)
list with all xml-files needed with their name and index-column
+++
TODO: - provide more functions to facilitate repairing of data.
- clean up the code!
+++
"""
list_file = {'node': ['Node.xml', 'Node_ID'],
'terminal': ['Terminal.xml', 'Terminal_ID'],
'line': ['Line.xml', 'Element_ID'],
'element': ['Element.xml', 'Element_ID'],
'load': ['Load.xml', 'Element_ID'],
'graphicNode': ['GraphicNode.xml', 'Node_ID'],
'calcParameter': ['CalcParameter.xml', ],
'ecoStation': ['EcoStation.xml','EcoStation_ID'],
'breaker': ['Breaker.xml','Terminal_ID']}
def __init__(self, name, foldername, path=False):
"""
Initialization of the ImporterXMLSincal class.
Parameters
----------
name: string
Name of the network.
foldername: string
Name of the folder to import from.
+++
TODO: - imply a possibility to indicate paths different
from the basepath.
+++
"""
self.name = name
self.foldername = foldername
if path is False:
# Filepath of the programm
dname = os.path.dirname(os.path.realpath(__file__))
else:
dname = path
self.base_path = dname+'/'+foldername
def __repr__(self):
return 'ImporterXMLSincal(name: {}, folder: {})'.format(self.name,
self.foldername)
# %%
def import_xml(self):
xml = XMLimport(self.name,
foldername=self.foldername,
list_file=self.list_file,
path=self.base_path)
xml.xmltodfs()
self.xmls = xml.xmls
# %%
def export_xml_topickles(self, directory):
"""
Exports the dataframes to the indicated directory.
Parameters
----------
:directory (str):
name of directory to export pickle to
:self.list_file (dict):
+++
TODO: - check, if directory and files get overwritten and
imply a warning!
+++
"""
if not os.path.exists(directory):
os.makedirs(directory)
else:
print('path already exists - files might get overwritten!')
input("PRESS ENTER TO CONTINUE. TO ABORT PRESS CTRL+C!")
for name in self.list_file:
filename = directory+'/'+str(name)+'.p'
pickle.dump(self.xmls[name], open(filename, 'wb'))
def import_xml_frompickles(self, directory):
"""
Imports the dataframes from the indicated directory.
Parameters
----------
:directory (str):
name of directory to import pickle from
:self.list_file (dict):
indicates which files need to be imported
:self.xmls (dict):
dict with all imported dataframes
"""
self.xmls = {}
for name in self.list_file:
filename = directory+'/'+str(name)+'.p'
self.xmls[name] = pickle.load(open(filename, 'rb'))
def rawdataintegrity(self):
"""
Performs all the checks and repairs automatically...
+++
TODO: - Write the function!
- Create own functions for each check.
- Extend the ability for checking.
+++
"""
print('performing line-check...')
self.linecheck()
print('further dataintegrity-check still needs to be written...')
def linecheck(self):
"""
Check if the data from imported xml files is valid, or if there are
problems.
Problems being checked for, are:
- Elements (Lines), that connect more than one node.
This would raise an error in the import function.
+++
TODO: -maybe check if nodes are connected otherwise (see comment ins
function)
+++
"""
# Number of Nodes, that are connected by one element.
nofel = self.xmls['terminal'].Element_ID.value_counts()
# Sum of Elements (mostly lines), that connect more than 2 Nodes and
# would thus create an error
nofelsum = nofel[nofel > 2].count()
if nofelsum > 0:
print("WARNING: " + str(nofelsum) +
" Elements connect more than two nodes.")
nofelmore = pd.DataFrame()
nofelmore['#_nodes'] = nofel[nofel > 2]
nofelmore['Element_ID'] = self.xmls['element'].loc[nofelmore.index,
'Element_ID']
nofelmore['Type'] = self.xmls['element'].loc[nofelmore.index,
'Type']
nofelmore['Name'] = self.xmls['element'].loc[nofelmore.index,
'Name']
a = nofelmore.groupby('Type')['Type']
print("These Elements have {} different Type(s): \n{}".format(
str(a.ngroups), str(list(a.groups.keys()))))
# TODO:
# Hier könnte man noch prüfen, ob die Knoten untereinander noch
# eine Verbindung haben, oder ob diese alle nur einmal
# im Element auftauchen.
# nodes = self.xmls['Terminal'].loc[nofelmore.index,'Node_ID']
return nofelmore
elif nofel[nofel < 2].count() > 0:
print('{} '.format(nofel[nofel < 2].count()),
'lines have less than two connected nodes!')
else:
print('lines checked - no line connects more than two components')
def dummyparameters_tozerolines(self,
r=0.0001,
x=0.0001,
b=0.0000002):
"""
This function adds (missing) dummy-parameters to lines for r, x, and b.
PyPSA might get problems in the power flow, if these parameters are
missing. So this function detects missing values and filles them with
values for r, x and b.
Parameters
----------
:r (float): default: 0.0001
:x (float): default: 0.0001
:b (float): default: 0.0000002
+++
TODO
- implement any security checks?
+++
"""
r = 0.0001
x = 0.0001
b = 0.0000002
linesr0 = self.lines[self.lines['r'] == 0]
for line in linesr0.index:
self.lines.at[line, 'r'] = r
linesx0 = self.lines[self.lines['x'] == 0]
for line in linesx0.index:
self.lines.at[line, 'x'] = x
linesb0 = self.lines[self.lines['b'] == 0]
for line in linesb0.index:
self.lines.at[line, 'b'] = b
print('The line parameters on following lines were changed:n/')
print('r: {}'.format(linesr0.index.values))
print('x: {}'.format(linesx0.index.values))
print('b: {}'.format(linesb0.index.values))
def repairlines(self, brokenlines):
"""
This function repairs the lines, that are connected to more than
two nodes, by splitting surplus nodes from the line and creating
dummy lines with neglectable x, r and b to connect the other nodes.
Parameters
----------
:brokenlines (DataFrame):
a list of those lines, that are broken with three columns
#_nodes - the number of nodes connected to the element
type - the Type of this element
name - the Name of this element
index - the Element-ID of this element
+++
TODO: clean up the code!
- use .at instead of .loc for speedup??
+++
"""
line_num = 1
for line in brokenlines['Element_ID']:
# read and separate the data:
filter_nodes = self.xmls['terminal']['Element_ID'] == line
nodes = self.xmls['terminal'].loc[filter_nodes]
line_inputnode = nodes[nodes['TerminalNo'] == '1']['Node_ID'].values[0] # unused? --> delete!
line_outputnodes = nodes[nodes['TerminalNo'] == '2']['Node_ID']
line_outputnode = line_outputnodes.values[0]
surplus_nodes = line_outputnodes.values[1:]
# change data in the xmls-dataframes
i = 1
node_from = line_outputnode
for surplus_node in surplus_nodes:
# create a new line without any resistance on the basis of
# the old line:
line_name = line+'s{}'.format(i) # new nam
# make a copy of existing line
new_line = self.xmls['line'].loc[line].copy()
new_line['Element_ID'] = line_name
new_line['r'] = '0.0001'
new_line['r0'] = '0.0001'
new_line['x'] = '0.0001'
new_line['x0'] = '0.0001'
new_line['l'] = '1'
new_line['c'] = '0.00002'
new_line.name = line_name
self.xmls['line'].loc[line_name] = new_line
# duplicate also the entry in the element-dataframe:
new_element = self.xmls['element'].loc[line].copy()
new_element.name = line_name
new_element['Element_ID'] = line_name
self.xmls['element'].loc[line_name] = new_element
# get the terminal entry
choose_nodes = self.xmls['terminal'][self.xmls['terminal']['Node_ID']==surplus_node]
term_id = choose_nodes['Terminal_ID'].loc[choose_nodes['Element_ID'] ==line].values[0]
# rewrite the terminal entry
self.xmls['terminal'].loc[term_id, 'Element_ID'] = line_name
if self.xmls['terminal'].loc[term_id, 'TerminalNo'] == '1':
print('oh')
self.xmls['terminal'].loc[term_id, 'TerminalNo'] = '2'
# append a new terminal entry
new_term_id = term_id+'s'+str(i)
# duplicate entry
terminal_entry = self.xmls['terminal'].loc[term_id]
# rewrite entry
terminal_entry.name = new_term_id
self.xmls['terminal'].loc[new_term_id] = terminal_entry
self.xmls['terminal'].loc[new_term_id, 'Terminal_ID'] = new_term_id # maybe not necessary??
self.xmls['terminal'].loc[new_term_id, 'TerminalNo'] = '1'
self.xmls['terminal'].loc[new_term_id, 'Node_ID'] = node_from
node_from = surplus_node
i = i+1
print('repaired line {} of {}'.format(line_num,
brokenlines.index.size))
line_num += 1
print('finished repairing lines')
def dfstocomponents(self, set_net_voltage='0', with_breaker=True):
"""
Converts and filters the dataframes from the raw xml-data
into pypsa readable dataframes.
Parameters
----------
:set_net_voltage (String):
with this parameter you can set the net-operating-voltage. If not
set it will be taken from Sincal. In some cases this helps to
include Eco-Stations as generators or loads. In 0.4 kV
networks, declared as general stations will be translated to
generators. In 10 kV networks they will be translated to loads.
:with_breaker (boolean):
if this is set to false, breaker will not effect the network. If
set to True, lines, that are separated by breakers will be deleted.
Set it to false to avoid this.
+++
TODO: rewrite the function in a way, that an API can be used to
automately translocate the values and set up the dataframes?
- clean the code.
- avoid int/float bus-names! write a bus- in front...
+++
"""
if set_net_voltage == '0':
self.net_voltage = self.xmls['calcParameter']['Uref'].values[0]
else:
self.net_voltage = set_net_voltage
try:
import utm
except ImportError:
raise ImportError('<no module named utm found>')
# create buses dataframe:
self.buses = pd.DataFrame()
self.buses['v_nom'] = self.xmls['node']['VoltLevel_ID']
# for name in self.buses.index:
# new_name = 'b'+str(name)
# self.buses['name'].loc[name] = new_name
# self.buses.index = self.buses['name']
self.buses.index.name = 'name'
# transform utm values to latlong
rw_list = self.xmls['graphicNode']['NodeStartX']
hw_list = self.xmls['graphicNode']['NodeStartY']
lat_list = rw_list.copy()
lat_list[:] = 0.0
lat_list[:] = 0.0 # for some reason this needs to be done, to make this a float
long_list = hw_list.copy()
long_list[:] = 0.0
long_list[:] = 0.0
for ind in rw_list.index:
rw = float(rw_list[ind])
hw = float(hw_list[ind])
lat, long = utm.to_latlon(easting=rw,
northing=hw,
zone_number=32,
northern=True)
lat_list[ind] = lat
long_list[ind] = long
self.buses['x'] = lat_list
self.buses['y'] = long_list
self.buses['carrier'] = 'AC'
self.buses['frequency'] = int(self.xmls['calcParameter']['f'].values[0])
# create lines dataframe:
self.lines = pd.DataFrame()
lineterminal = self.xmls['terminal']
lineterminal['breaker_state'] = '1'
# filter lines, that are not connected due to breakers:
if not self.xmls['breaker'].empty:
breaker_state = self.xmls['breaker']['Flag_State']
for term_id in breaker_state.index:
state = breaker_state[term_id]
if len(state) == 1:
lineterminal['breaker_state'].loc[term_id] = state
else:
# TODO: if one of the states is 0, pass that one!
lineterminal['breaker_state'].loc[term_id] = state.ix[0]
lineterminal.index = lineterminal['Element_ID']
lineterminal = lineterminal[self.xmls['element']['Type'] == 'Line']
# gather all lines to be deleted here:
self.line_del = lineterminal['Element_ID'][lineterminal['breaker_state'] == '0']
if not self.line_del.empty:
print('deleting the following lines, due to breakers in the grid:')
print(self.line_del.index)
self.lines['bus0'] = lineterminal[lineterminal['TerminalNo']=='1']['Node_ID']
self.lines['bus1'] = lineterminal[lineterminal['TerminalNo']=='2']['Node_ID']
# TODO: can this be written less time consuming???:
# for line in self.lines.index:
# self.lines['bus0'].loc[line] = 'b'+str(self.lines['bus0'].loc[line])
# self.lines['bus1'].loc[line] = 'b'+str(self.lines['bus1'].loc[line])
self.lines.index.name = 'name'
# Filtern der benötigten Daten:
line_fl = self.xmls['line'][['Ith',
'Un',
'c',
'fn',
'l',
'q',
'r',
'x']].astype(float)
# Multiplication of l and r to get the overall r
self.lines['r'] = line_fl['l'].multiply(line_fl['r'])
# Multiplication of l and x to get the overall x
self.lines['x'] = line_fl['l'].multiply(line_fl['x'])
# Multiplication of l and c, and radial frequency
self.lines['b'] = line_fl['l'].multiply(line_fl['c']*2*math.pi*50*10e-9)
self.lines['s_nom'] = line_fl['Ith'].multiply(line_fl['Un']) # TODO: imply real formula!
# delete lines, that are not connected
if with_breaker == True:
self.lines = self.lines.drop(self.line_del)
# create slack generators dataframe (for given Infeed-nodes):
self.generators = pd.DataFrame()
self.generators['name'] = self.xmls['element'][self.xmls['element']['Type']=='Infeeder']['Element_ID']
self.generators.index = self.generators['name']
self.generators = self.generators.drop(columns='name')
self.generators['control'] = 'slack' # Only for Infeeders!
self.generators['bus'] = 'nan' # create a dummy
# TODO: is this also possible without the for loop?
# connect the generator to a bus
for gname in self.generators.index:
gen_bus = self.xmls['terminal'].loc[self.xmls['terminal']['Element_ID']==gname,'Node_ID']
self.generators.loc[gname, 'bus'] = 'b'+str(gen_bus[0])
# create slack generators dataframe (for given EcoStations):
slacknodes = self.xmls['node']['EcoStation_ID'].astype(float)
# get the type of the ecostations:
# 1 = Netstation
# 2 = Umspannstation
# 3 = Schaltstation
# 4 = Allgemeine Station
# 5 = Verteilnetzstation
if self.net_voltage == '10':
lo = ['4']
gen = ['1', '2']
if self.net_voltage == '0.4':
gen = ['1', '2', '4']
ecost_type = self.xmls['ecoStation']['Flag_Typ']
slacknodes = slacknodes[slacknodes > 0]
slacknodes = slacknodes.astype(int)
for node_id in slacknodes.index:
ecost = slacknodes[node_id]
ecotp = ecost_type[str(ecost)]
if ecotp in gen:
gen_name = self.xmls['node'].loc[node_id, 'Name'].strip()
generator = pd.Series()
generator.name = gen_name
i = 0
new_gen_name = gen_name
while new_gen_name in self.generators.index:
new_gen_name = gen_name+'_'+str(i)
i += 1
gen_name = new_gen_name
generator['control'] = 'slack'
# generator['bus'] = 'b'+str(node_id)
generator['bus'] = node_id
self.generators.loc[gen_name] = generator
elif ecotp in lo:
pass # TODO! needs to be written!
if not self.xmls['load'].empty:
self.loads = pd.DataFrame()
# eap = jahreswirkverbrauch in kwh
self.loads['p_set'] = self.xmls['load']['Eap'].astype(int) / 1000
# self.loads['q_set'] = self.xmls['load']['Eap'].astype(int)
self.loads['bus'] = self.xmls['load']['Element_ID'] # TODO: is this true?? Not Node_ID??? and the b in front!!
names = []
for name in self.xmls['load']['Element_ID']:
names += ['l'+name]
self.loads['name'] = names
self.loads = self.loads.reset_index()
# for some nodes, several loads may exist; count them:
counts = self.loads['name'].value_counts()
for i in counts.index:
val = counts[i]
if val > 1:
add = 0
group = self.loads['name'][self.loads['name'] == i]
for load in group.index:
new_name = i+'_'+str(add)
add += 1
self.loads.loc[load, 'name'] = new_name
self.loads.index = self.loads['name']
self.loads = self.loads.drop(columns='name')
def transform_gen_toTKN(self):
"""
In low-voltage grids, isolation boxes may be mistaken for
generators. In german they are referred to as "Trennkästen",
with the abbreviation TKN. In this function they are filtered and
disconnected. In a second step sub_networks are searched for,
which are not fed by any generator and possess an isolation box.
These are then reconnected to the grid with generator.
Parameters:
----------
None
"""
if hasattr(self, 'network'):
print('transforming gens to TKN')
tkn = pd.DataFrame(columns=self.network.generators.columns)
for name in self.network.generators.index:
if 'TKN' in name:
tkn.loc[name] = self.network.generators.loc[name]
# lines with TKN are split and new buses for each line created
line_list = {}
for line in self.lines.index:
if self.lines.loc[line].bus0 in tkn.bus.values:
bus_name = self.lines.loc[line].bus0
x = self.network.buses.loc[bus_name].x
y = self.network.buses.loc[bus_name].y
f = self.network.buses.loc[bus_name].frequency
v_nom = self.network.buses.loc[bus_name].v_nom
bus_name = bus_name + '_tkn'
while bus_name in line_list:
bus_name = bus_name + 'i'
self.network.lines.bus0[line] = bus_name
self.network.add('Bus',
bus_name,
x=x,
y=y,
v_nom=v_nom)
line_list[bus_name] = line
print('processing line {}'.format(line))
if self.lines.loc[line].bus1 in tkn.bus.values:
bus_name = self.lines.loc[line].bus1
x = self.network.buses.loc[bus_name].x
y = self.network.buses.loc[bus_name].y
f = self.network.buses.loc[bus_name].frequency
v_nom = self.network.buses.loc[bus_name].v_nom
bus_name = bus_name + '_tkn'
while bus_name in line_list:
bus_name = bus_name + 'i'
self.network.lines.bus1[line] = bus_name
self.network.add('Bus',
bus_name,
x=x,
y=y,
v_nom=v_nom)
line_list[bus_name] = line
print('processing line {}'.format(line))
# delete all tkn generators and their (no longer connected) buses from the network:
for gen in tkn.index:
self.network.remove('Bus', tkn.loc[gen].bus)
self.network.remove('Generator', gen)
def refresh_lists(self):
func_list = []
non_func_list = []
print('Determining network topology')
self.network.determine_network_topology()
#self.network.lpf() # TODO: check if also determine-function is sufficient!
print('Reconnecting sub_networks without gen, with TKN')
for sub_network in self.network.sub_networks.obj:
gens = sub_network.generators()
has_gen = True
if len(gens) == 0:
# print('sub {} has no generator'.format(sub_network))
has_gen = False
has_tkn = False
tkn_bus = []
# HIER IST NOCH EIN FEHLER DRIN:
for bus in sub_network.buses().index:
if 'tkn' in bus:
has_tkn = True
tkn_bus += [bus]
if has_tkn and has_gen:
print('functional sub_net with {}'.format(tkn_bus))
func_list += [tkn_bus]
if has_tkn and not has_gen:
non_func_list +=[tkn_bus]
return func_list, non_func_list
func_list, non_func_list = refresh_lists(self)
con_accum = True
while non_func_list and con_accum:
con_accum = False
for tkn in non_func_list:
while con_accum is False and tkn:
tkn_bus = tkn.pop()
tkn_bus_org = tkn_bus
while 'i' in tkn_bus:
tkn_bus = tkn_bus[0:-1]
con_bus = False
for func_buses in func_list:
for func_bus in func_buses:
if tkn_bus in func_bus:
con_bus = func_bus
if con_bus is not False:
con_accum = True
line_name = con_bus+tkn_bus_org
self.network.add('Line',
line_name,
bus0=tkn_bus_org,
bus1=con_bus,
b=0.0000002,
r=0.0001,
x=0.0001)
# get rid of empty lists
non_func_list = [x for x in non_func_list if x != []]
func_list, non_func_list = refresh_lists(self)
# print(non_func_list)
#
def check_busbars(self):
"""
Check, if nodes are connected via busbars, which in PSS-Sincal is
given by the InclName argument.
Returns the InclName row with all relevant entries.
Returns
-------
inc_names: pd.Series()
"""
# store the InclName row
inc_names = self.xmls['node']['InclName']
# drop nans
inc_names = inc_names.dropna()
# drop whitespaces
for node_id in inc_names.index:
inc_names[node_id] = inc_names[node_id].strip()
# drop empty entries
inc_names_notempty = inc_names != ''
inc_names = inc_names[inc_names_notempty]
# print out the list of all entries, to be able to check them
print('The following InclNames were found:')
print(inc_names)
return inc_names
def connect_busbars(self, inc_names, keys=False):
"""
Takes the series of InclName and checks them for the given keys,
then connects all components with the given keys.
Parameters
----------
inc_names : pd.Series()
all relevant InclNames from the PSS-Sincal node dataframe
keys : list
the keys allow to check for tags inside the inc_names list, which
identify the common busbar and thus, which elements should be
connected
+++
TODO: check first, if the pypsa components already exist - if not,
it is not possible to connect the components!!
+++
"""
if hasattr(self, 'network'):
connect_dict = {}
# if no key for the connection is given, all nodes are connected
# with each other
if keys is False:
for node_id in inc_names.index:
connect_dict[node_id] += [node_id]
i = -1
for number in connect_dict:
self.network.add("Line",
"busbar" + str(number),
bus0=buses_list[i],
bus1=buses_list[i+1],
b=0.0000002,
r=0.0001,
x=0.0001)
i += 1
# else only those containing the key are connected to each other
else:
for key in keys:
connect_dict[key] = []
for node_id in inc_names.index:
if key in inc_names[node_id]:
connect_dict[key] += [node_id]
# print(connect_dict)
for key in keys:
buses_list = connect_dict[key]
i = -1
for number in buses_list:
self.network.add("Line",
"busbar" + str(number),
bus0=buses_list[i],
bus1=buses_list[i+1],
b=0.0000002,
r=0.0001,
x=0.0001)
i += 1
def connect_stationstolines(self,
bindings,
b=0.0000002,
r=0.0001,
x=0.0001):
"""
Takes a dataframe of line and node ID's to be connected and connects
them with a dummy line
Parameters
----------
bindings : pd.DataFrame()
line and node ID's to be connected to each other.
+++
TODO: - rewrite the function in a way, that it is universally usables
- rename variable bindings to something understandable
+++
"""
if hasattr(self, 'network'):
# strip whitespaces from element-names
for element in self.xmls['element'].index:
el_name = self.xmls['element'].loc[element, 'Name'].strip()
self.xmls['element'].loc[element, 'Name'] = el_name
# run through all the connections to be made and connect them
for bin_id in bindings:
station_id = bindings.loc['ID_Station',bin_id]
eq_col = self.xmls['node']['Equipment_ID'] == station_id
ecol = self.xmls['node'][eq_col]
if not ecol.empty:
enode_id = ecol.index.values[0]
line_id = bindings.loc['ID_Kabel',bin_id]
line_id = 'ID_'+line_id
lin_col = self.xmls['element']['Name'] == line_id
lcol = self.xmls['element'][lin_col]
lelement_id = lcol.index.values[0]
term_id = self.xmls['terminal']['Node_ID'].loc[lelement_id].values
for node in term_id:
term_col = self.xmls['terminal']['Node_ID'] == node
check = self.xmls['terminal']['Node_ID'][term_col]
if len(check) == 1:
i = 0
newline_name = str(lelement_id)+str(i)
while newline_name in self.network.lines.index:
i += 1
newline_name = str(lelement_id)+str(i)
self.network.add("Line",
newline_name,
bus0=check.values[0],
bus1=enode_id,
b=b,
r=r,
x=x)
def importloadswithprofiles(self,
filename,
ltype='rh0',
feedin=False,
replacetime=False):
"""
This function imports loadprofiles and transforms them into a pypsa
readable dataframe.
Parameters
----------
:filename (str):
name of the load-profile file to be imported
this file should be a .csv with rows:
Time;bus;bus;...
YYYY/MM/DD HH:MM;1,3;2,1;...
meaning the bus, that the load is connected to.
P in kW
:ltype (str):
type of the load, to distinguish several loads at the same bus
:feedin (bool):
if the load is actually a feedin, this can be set here
:replacetime (bool):
if there should be a problem with summer and winter time, this can
be set to replace the datetimeindex
+++
TODO: - implement an option to include unique identifiers in the csv
- change lambda and use a def instead...
+++
"""
# to correctly import date and time, this function is defined:
dateparse = lambda x: pd.datetime.strptime(x, '%Y/%m/%d %H:%M')
loaddat = pd.read_csv(filename,
sep=';',
parse_dates=['Time'],
date_parser=dateparse)
loaddat.index = loaddat['Time']
loaddat = loaddat.drop(columns='Time')
# PyPSA has problems with summer and winter time. If necessary, they
# can be replaced here:
if replacetime is True:
start_time = loaddat.index[0]
end_time = loaddat.index[-1]
f = '15min'
dti = pd.date_range(start=start_time,
end=end_time,
freq=f)
loaddat.index = dti
# convert kW to MW
loaddat = loaddat/1000
# if it is a feedin, this is converted here:
if feedin is True:
loaddat = loaddat*(-1)
if not hasattr(self, 'loads'):
print('no loads until now. Implementing.')
self.loads = pd.DataFrame(columns=['bus', 'p_set'])
self.loads.index.name = 'name'
i = 0
for node in loaddat.columns:
new_node = node
if '.' in node:
# new_node = node[0:len(node)-3] # clean it from any float-numbers
print('WARNING: the load on bus {} may not be recognized!'.format(node),
'Please clean it from any float-numbers.',
'If a . is part of the name, ignore this warning.')
name = 'l'+new_node+ltype
if feedin is True:
p_set = loaddat[node].min()
else:
p_set = loaddat[node].max()
load = {'bus': new_node,
'p_set': p_set}
self.loads.loc[name] = load
i += 1
if not hasattr(self, 'snapshots'):
print('no loadprofile until now. Implementing.')
column = loaddat.columns[0]
self.snapshots = loaddat[column].copy()
self.snapshots.name = 'weighting'
self.snapshots[:] = 1
if not hasattr(self, 'loads_p_set'):
self.loads_p_set = loaddat.copy()
for column in self.loads_p_set.columns:
self.loads_p_set = self.loads_p_set.rename(columns={column: 'l'+column+ltype})
else:
loads_p_set_local = loaddat.copy()
for column in loads_p_set_local.columns:
new_columnname = 'l'+column+ltype
loads_p_set_local = loads_p_set_local.rename(columns={column: new_columnname})
self.loads_p_set[new_columnname] = loads_p_set_local[new_columnname]
def importnetwork(self):
"""
This function imports the converted dataframes into pypsa, checks the
network for consistency and prints out, if not connected subgraphs are
present.
+++
TODO:
- check if the dataframes are available. (partly done)
- rewrite the code with less lines...
- loads-p_set??? --> check for cls names!
- replace try with hasattr
+++
"""
self.network = pypsa.Network()
try:
self.network.import_components_from_dataframe(self.buses, 'Bus')
self.network.import_components_from_dataframe(self.lines, 'Line')
except:
print('ERROR: No buses or lines found! or problems with import')
if hasattr(self, 'loads'):
self.network.import_components_from_dataframe(self.loads, 'Load')
else:
print('no loads implemented')
if hasattr(self, 'generators'):
self.network.import_components_from_dataframe(self.generators,
'Generator')
else:
print('no generators implemented')
if hasattr(self, 'snapshots'):
print('implementing snapshots')
self.network.set_snapshots(self.snapshots.index)
if hasattr(self, 'loads_p_set'):
print('implementing load series')
self.network.import_series_from_dataframe(self.loads_p_set,
'Load',
'p_set')
self.network.consistency_check()
def check_connectivity(self, printdata=False):
"""
checks, if there are not connected graphs inside the network and
prints out additional information to each graph, if printdata is
set to True.
Parameters
----------
printdata: boolean (default: False)
if set to True, the function will print the nodes and edges of
each subgraph/-network
"""
g = self.network.graph()
if nx.number_connected_components(g) > 1:
print('The network consists of {} not connected subgraphs.'.format(nx.number_connected_components(g)))
if g.is_directed():
g = g.to_undirected()
sub_graphs = nx.connected_component_subgraphs(g)
if printdata is True:
for i, sg in enumerate(sub_graphs):
numon = sg.number_of_nodes()
print("subgraph {} has {} nodes".format(i, numon))
print("\tNodes:", sg.nodes(data=True))
print("\tEdges:", sg.edges())
def del_nogen_subs(self):
"""
function deletes subgraphs that have no (slack) generator.
Parameters
----------
None
Returns
-------
None
+++
TODO:
- find a faster way to define the subnetwork - this is too long!
+++
"""
for subnet in self.network.sub_networks.index:
sub = self.network[self.network.buses.sub_network == subnet]
if len(sub.generators) == 0:
sub_buses = sub.buses
sub_lines = sub.lines
sub_generators = sub.generators
sub_loads = sub.loads
for bus in sub_buses.index:
self.network.remove('Bus', name=bus)
for line in sub_lines.index:
self.network.remove('Line', name=line)
for generator in sub_generators.index:
self.network.remove('Generator', name=generator)