/
results.py
2533 lines (2241 loc) · 110 KB
/
results.py
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"""This file is part of DINGO, the DIstribution Network GeneratOr.
DINGO is a tool to generate synthetic medium and low voltage power
distribution grids based on open data.
It is developed in the project open_eGo: https://openegoproject.wordpress.com
DING0 lives at github: https://github.com/openego/ding0/
The documentation is available on RTD: http://ding0.readthedocs.io"""
__copyright__ = "Reiner Lemoine Institut gGmbH"
__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__url__ = "https://github.com/openego/ding0/blob/master/LICENSE"
__author__ = "nesnoj, gplssm"
import pickle
import numpy as np
import pandas as pd
import time
import os
import re
from matplotlib import pyplot as plt
import seaborn as sns
from egoio.tools import db
from ding0.core import NetworkDing0
from ding0.core import GeneratorDing0
from ding0.core import LVCableDistributorDing0, MVCableDistributorDing0
from ding0.core import MVStationDing0, LVStationDing0
from ding0.core import CircuitBreakerDing0
from ding0.core.network.loads import LVLoadDing0
from ding0.core import LVLoadAreaCentreDing0
import pyproj
from functools import partial
from geoalchemy2.shape import from_shape
from sqlalchemy.orm import sessionmaker
import multiprocessing as mp
from math import floor, pi
from ding0.flexopt.check_tech_constraints import get_critical_line_loading, \
get_critical_voltage_at_nodes
from ding0.tools import config as cfg_ding0
import networkx as nx
if not 'READTHEDOCS' in os.environ:
from shapely.ops import transform
from shapely.geometry import LineString
from shapely.wkt import dumps as wkt_dumps
#############################################
plt.close('all')
cfg_ding0.load_config('config_db_tables.cfg')
cfg_ding0.load_config('config_calc.cfg')
cfg_ding0.load_config('config_files.cfg')
cfg_ding0.load_config('config_misc.cfg')
def lv_grid_generators_bus_bar(nd):
"""
Calculate statistics about generators at bus bar in LV grids
Parameters
----------
nd : ding0.NetworkDing0
Network container object
Returns
-------
lv_stats : dict
Dict with keys of LV grid repr() on first level. Each of the grids has
a set of statistical information about its topology
"""
lv_stats = {}
for la in nd._mv_grid_districts[0].lv_load_areas():
for lvgd in la.lv_grid_districts():
station_neighbors = list(lvgd.lv_grid._graph[
lvgd.lv_grid._station].keys())
# check if nodes of a statio are members of list generators
station_generators = [x for x in station_neighbors
if x in lvgd.lv_grid.generators()]
lv_stats[repr(lvgd.lv_grid._station)] = station_generators
return lv_stats
def save_nd_to_pickle(nd, path='', filename=None):
"""Use pickle to save the whole nd-object to disc
The network instance is entirely pickled to a file.
Parameters
----------
nd : NetworkDing0
Ding0 grid container object
path : :obj:`str`
Absolute or relative path where pickle should be saved. Default is ''
which means pickle is save to PWD
"""
abs_path = os.path.abspath(path)
if len(nd._mv_grid_districts) > 1:
name_extension = '_{number}-{number2}'.format(
number=nd._mv_grid_districts[0].id_db,
number2=nd._mv_grid_districts[-1].id_db)
else:
name_extension = '_{number}'.format(number=nd._mv_grid_districts[0].id_db)
if filename is None:
filename = "ding0_grids_{ext}.pkl".format(
ext=name_extension)
# delete attributes of `nd` in order to make pickling work
# del nd._config
del nd._orm
pickle.dump(nd, open(os.path.join(abs_path, filename), "wb"))
def load_nd_from_pickle(filename=None, path=''):
"""
Use pickle to save the whole nd-object to disc
Parameters
----------
filename : :obj:`str`
Filename of nd pickle
path : :obj:`str`
Absolute or relative path where pickle should be saved. Default is ''
which means pickle is save to PWD
Returns
-------
nd : NetworkDing0
Ding0 grid container object
"""
abs_path = os.path.abspath(path)
if filename is None:
raise NotImplementedError
return pickle.load(open(os.path.join(abs_path, filename), "rb"))
def plot_cable_length(stats, plotpath):
"""
Cable length per MV grid district
"""
# cable and line kilometer distribution
f, axarr = plt.subplots(2, 2, sharex=True)
stats.hist(column=['Length of MV overhead lines'], bins=5, alpha=0.5, ax=axarr[0, 0])
stats.hist(column=['Length of MV underground cables'], bins=5, alpha=0.5, ax=axarr[0, 1])
stats.hist(column=['Length of LV overhead lines'], bins=5, alpha=0.5, ax=axarr[1, 0])
stats.hist(column=['Length of LV underground cables'], bins=5, alpha=0.5, ax=axarr[1, 1])
plt.savefig(os.path.join(plotpath,
'Histogram_cable_line_length.pdf'))
def plot_generation_over_load(stats, plotpath):
"""
Plot of generation over load
"""
# Generation capacity vs. peak load
sns.set_context("paper", font_scale=1.1)
sns.set_style("ticks")
# reformat to MW
gen_cap_indexes = ["Gen. Cap. of MV at v_level 4",
"Gen. Cap. of MV at v_level 5",
"Gen. Cap. of LV at v_level 6",
"Gen. Cap. of LV at v_level 7"]
peak_load_index = ["LA Total LV Peak Load total"]
stats['generation_capacity'] = stats[gen_cap_indexes].sum(axis=1) / 1e3
stats['peak_load'] = stats[peak_load_index] / 1e3
sns.lmplot('generation_capacity', 'peak_load',
data=stats,
fit_reg=False,
# hue='v_nom',
# hue='Voltage level',
scatter_kws={"marker": "D",
"s": 100},
aspect=2)
plt.title('Peak load vs. generation capacity')
plt.xlabel('Generation capacity in MW')
plt.ylabel('Peak load in MW')
plt.savefig(os.path.join(plotpath,
'Scatter_generation_load.pdf'))
def plot_km_cable_vs_line(stats, plotpath):
"""
:param stats:
:param plotpath:
:return:
"""
# Cable vs. line kilometer scatter
sns.lmplot('km_cable', 'km_line',
data=stats,
fit_reg=False,
hue='v_nom',
# hue='Voltage level',
scatter_kws={"marker": "D",
"s": 100},
aspect=2)
plt.title('Kilometer of cable/line')
plt.xlabel('Km of cables')
plt.ylabel('Km of overhead lines')
plt.savefig(os.path.join(plotpath,
'Scatter_cables_lines.pdf'))
def concat_nd_pickles(self, mv_grid_districts):
"""
Read multiple pickles, join nd objects and save to file
Parameters
----------
mv_grid_districts : :obj:`list`
Ints describing MV grid districts
"""
pickle_name = cfg_ding0.get('output', 'nd_pickle')
# self.nd = self.read_pickles_from_files(pickle_name)
# TODO: instead of passing a list of mvgd's, pass list of filenames plus optionally a basth_path
for mvgd in mv_grid_districts[1:]:
filename = os.path.join(
self.base_path,
'results', pickle_name.format(mvgd))
if os.path.isfile(filename):
mvgd_pickle = pickle.load(open(filename, 'rb'))
if mvgd_pickle._mv_grid_districts:
mvgd.add_mv_grid_district(mvgd_pickle._mv_grid_districts[0])
# save to concatenated pickle
pickle.dump(mvgd,
open(os.path.join(
self.base_path,
'results',
"ding0_grids_{0}-{1}.pkl".format(
mv_grid_districts[0],
mv_grid_districts[-1])),
"wb"))
# save stats (edges and nodes data) to csv
nodes, edges = mvgd.to_dataframe()
nodes.to_csv(os.path.join(
self.base_path,
'results', 'mvgd_nodes_stats_{0}-{1}.csv'.format(
mv_grid_districts[0], mv_grid_districts[-1])),
index=False)
edges.to_csv(os.path.join(
self.base_path,
'results', 'mvgd_edges_stats_{0}-{1}.csv'.format(
mv_grid_districts[0], mv_grid_districts[-1])),
index=False)
####################################################
def calculate_lvgd_stats(nw):
"""
LV Statistics for an arbitrary network
Parameters
----------
nw: :obj:`list` of NetworkDing0
The MV grid(s) to be studied
Returns
-------
lvgd_stats : :pandas:`pandas.DataFrame<dataframe>`
Dataframe containing several statistical numbers about the LVGD
"""
##############################
# ETRS (equidistant) to WGS84 (conformal) projection
proj = partial(
pyproj.transform,
# pyproj.Proj(init='epsg:3035'), # source coordinate system
# pyproj.Proj(init='epsg:4326')) # destination coordinate system
pyproj.Proj(init='epsg:4326'), # source coordinate system
pyproj.Proj(init='epsg:3035')) # destination coordinate system
##############################
# close circuit breakers
nw.control_circuit_breakers(mode='close')
##############################
lv_dist_idx = 0
lv_dist_dict = {}
lv_gen_idx = 0
lv_gen_dict = {}
lv_load_idx = 0
lv_load_dict = {}
branch_idx = 0
branches_dict = {}
trafos_idx = 0
trafos_dict = {}
for mv_district in nw.mv_grid_districts():
for LA in mv_district.lv_load_areas():
for lv_district in LA.lv_grid_districts():
lv_dist_idx += 1
branches_from_station = len(lv_district.lv_grid.graph_branches_from_node(lv_district.lv_grid.station()))
lv_dist_dict[lv_dist_idx] = {
'MV_grid_id': mv_district.mv_grid.id_db,
'LV_grid_id': lv_district.lv_grid.id_db,
'Load Area ID': LA.id_db,
'Population': lv_district.population,
'Peak Load Residential': lv_district.peak_load_residential,
'Peak Load Retail': lv_district.peak_load_retail,
'Peak Load Industrial': lv_district.peak_load_industrial,
'Peak Load Agricultural': lv_district.peak_load_agricultural,
'Number of Sector Residential': lv_district.sector_count_residential,
'Number of Sector Retail': lv_district.sector_count_retail,
'Number of Sector Industrial': lv_district.sector_count_industrial,
'Number of Sector Agricultural': lv_district.sector_count_agricultural,
'Accum. Consumption Residential': lv_district.sector_consumption_residential,
'Accum. Consumption Retail': lv_district.sector_consumption_retail,
'Accum. Consumption Industrial': lv_district.sector_consumption_industrial,
'Accum. Consumption Agricultural': lv_district.sector_consumption_agricultural,
'Number of branches from LV Station': branches_from_station,
'Load Area is Aggregated': LA.is_aggregated,
'Load Area is Satellite': LA.is_satellite,
}
# generation capacity
for g in lv_district.lv_grid.generators():
lv_gen_idx += 1
subtype = g.subtype
if subtype == None:
subtype = 'other'
type = g.type
if type == None:
type = 'other'
lv_gen_dict[lv_gen_idx] = {
'LV_grid_id': lv_district.lv_grid.id_db,
'v_level': g.v_level,
'subtype': type + '/' + subtype,
'GenCap': g.capacity,
}
# nodes bzw. LV loads
for node in lv_district.lv_grid.graph_nodes_sorted():
if isinstance(node, LVLoadDing0):
lv_load_idx += 1
if 'agricultural' in node.consumption:
tipo = 'agricultural'
elif 'industrial' in node.consumption:
tipo = 'ind_ret'
elif 'residential' in node.consumption:
tipo = 'residential'
else:
tipo = 'none'
lv_load_dict[lv_load_idx] = {
'LV_grid_id': lv_district.lv_grid.id_db,
'load_type': tipo,
}
# branches
for branch in lv_district.lv_grid.graph_edges():
branch_idx += 1
branches_dict[branch_idx] = {
'LV_grid_id': lv_district.lv_grid.id_db,
'length': branch['branch'].length / 1e3,
'type_name': branch['branch'].type.to_frame().columns[0],
'type_kind': branch['branch'].kind,
}
# Transformers
for trafo in lv_district.lv_grid.station().transformers():
trafos_idx += 1
trafos_dict[trafos_idx] = {
'LV_grid_id': lv_district.lv_grid.id_db,
's_max_a': trafo.s_max_a,
}
# geographic
district_geo = transform(proj, lv_district.geo_data)
lv_dist_dict[lv_dist_idx].update({'Area': district_geo.area})
lvgd_stats = pd.DataFrame.from_dict(lv_dist_dict, orient='index').set_index('LV_grid_id')
# generate partial dataframes
gen_df = pd.DataFrame.from_dict(lv_gen_dict, orient='index')
load_df = pd.DataFrame.from_dict(lv_load_dict, orient='index')
branch_df = pd.DataFrame.from_dict(branches_dict, orient='index')
trafos_df = pd.DataFrame.from_dict(trafos_dict, orient='index')
# resque desired data
if not gen_df.empty:
# generation by voltage level
lv_generation = gen_df.groupby(['LV_grid_id', 'v_level'])['GenCap'].sum().to_frame().unstack(level=-1)
lv_generation.columns = ['Gen. Cap. v_level ' + str(_[1]) if isinstance(_, tuple) else str(_) for _ in
lv_generation.columns]
lvgd_stats = pd.concat([lvgd_stats, lv_generation], axis=1)
# generation by type/subtype
lv_generation = gen_df.groupby(['LV_grid_id', 'subtype'])['GenCap'].sum().to_frame().unstack(level=-1)
lv_generation.columns = ['Gen. Cap. type ' + str(_[1]) if isinstance(_, tuple) else str(_) for _ in
lv_generation.columns]
lvgd_stats = pd.concat([lvgd_stats, lv_generation], axis=1)
if not load_df.empty:
# number of residential loads
lv_loads = load_df[load_df['load_type'] == 'residential'].groupby(['LV_grid_id'])[
'load_type'].count().to_frame() # .unstack(level=-1)
lv_loads.columns = ['Number of loads residential']
lvgd_stats = pd.concat([lvgd_stats, lv_loads], axis=1)
# number of agricultural loads
lv_loads = load_df[load_df['load_type'] == 'agricultural'].groupby(['LV_grid_id'])[
'load_type'].count().to_frame() # .unstack(level=-1)
lv_loads.columns = ['Number of loads agricultural']
lvgd_stats = pd.concat([lvgd_stats, lv_loads], axis=1)
# number of mixed industrial / retail loads
lv_loads = load_df[load_df['load_type'] == 'ind_ret'].groupby(['LV_grid_id'])[
'load_type'].count().to_frame() # .unstack(level=-1)
lv_loads.columns = ['Number of loads mixed industrial/retail']
lvgd_stats = pd.concat([lvgd_stats, lv_loads], axis=1)
if not branch_df.empty:
# branches by type name
lv_branches = branch_df.groupby(['LV_grid_id', 'type_name'])['length'].sum().to_frame().unstack(level=-1)
lv_branches.columns = ['Length Type ' + _[1] if isinstance(_, tuple) else _ for _ in lv_branches.columns]
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
# branches by kind
lv_branches = branch_df[branch_df['type_kind'] == 'line'].groupby(['LV_grid_id'])['length'].sum().to_frame()
lv_branches.columns = ['Length of overhead lines']
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
lv_branches = branch_df[branch_df['type_kind'] == 'cable'].groupby(['LV_grid_id'])['length'].sum().to_frame()
lv_branches.columns = ['Length of underground cables']
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
# N°of branches
lv_branches = branch_df.groupby(['LV_grid_id', 'type_name'])['length'].count().to_frame().unstack(level=-1)
lv_branches.columns = ['Number of branches Type ' + _[1] if isinstance(_, tuple) else _ for _ in
lv_branches.columns]
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
lv_branches = branch_df[branch_df['type_kind'] == 'line'].groupby(['LV_grid_id'])['length'].count().to_frame()
lv_branches.columns = ['Number of branches overhead lines']
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
lv_branches = branch_df[branch_df['type_kind'] == 'cable'].groupby(['LV_grid_id'])['length'].count().to_frame()
lv_branches.columns = ['Number of branches underground cables']
lvgd_stats = pd.concat([lvgd_stats, lv_branches], axis=1)
if not trafos_df.empty:
# N of trafos
lv_trafos = trafos_df.groupby(['LV_grid_id'])['s_max_a'].count().to_frame()
lv_trafos.columns = ['Number of MV/LV Trafos']
lvgd_stats = pd.concat([lvgd_stats, lv_trafos], axis=1)
# Capacity of trafos
lv_trafos = trafos_df.groupby(['LV_grid_id'])['s_max_a'].sum().to_frame()
lv_trafos.columns = ['Accumulated s_max_a in MVLV trafos']
lvgd_stats = pd.concat([lvgd_stats, lv_trafos], axis=1)
lvgd_stats = lvgd_stats.fillna(0)
lvgd_stats = lvgd_stats[sorted(lvgd_stats.columns.tolist())]
return lvgd_stats
####################################################
def calculate_mvgd_stats(nw):
"""
MV Statistics for an arbitrary network
Parameters
----------
nw: :obj:`list` of NetworkDing0
The MV grid(s) to be studied
Returns
-------
mvgd_stats : :pandas:`pandas.DataFrame<dataframe>`
Dataframe containing several statistical numbers about the MVGD
"""
##############################
freq = cfg_ding0.get('assumptions', 'frequency')
omega = 2 * pi * freq
# close circuit breakers
nw.control_circuit_breakers(mode='close')
##############################
# Collect info from nw into dataframes
# define dictionaries for collection
trafos_dict = {}
generators_dict = {}
branches_dict = {}
ring_dict = {}
LA_dict = {}
other_nodes_dict = {}
lv_branches_dict = {}
# initiate indexes
trafos_idx = 0
gen_idx = 0
branch_idx = 0
ring_idx = 0
LA_idx = 0
lv_branches_idx = 0
# loop over MV grid districts
for district in nw.mv_grid_districts():
# node of MV station
root = district.mv_grid.station()
###################################
# get impedance of path to each terminal node
# and get thermal capacity of first segment of path to each terminal node
# store properties of terminal nodes in dictionaries
# properties are e.g. impedance of path, length of path, thermal limit of first segment of path
mv_impedances = {}
mvlv_impedances = {}
mv_path_lengths = {}
mvlv_path_lengths = {}
mv_thermal_limits = {} # I_max of first segment on MV for each MV path
lv_thermal_limits = {} # I_max of first segment on LV for each LV path
mvlv_thermal_limits = {} # I_max of first segment on MV for each MVLV path
n_outgoing_LV = 0
n_stations_LV = 0
n_outgoing_MV = 0
G = district.mv_grid._graph
for node in G.nodes():
if isinstance(node, MVStationDing0):
n_outgoing_MV += len(list(G.neighbors(node)))
continue
mv_impedance = 0
mv_path_length = 0
if not isinstance(node, MVCableDistributorDing0) and not isinstance(node, CircuitBreakerDing0):
if not nx.has_path(G, root, node):
continue
#print(node, node.lv_load_area.is_aggregated) # only debug
else:
path = nx.shortest_path(G, root, node)
for i in range(len(path) - 1):
mv_impedance += (G.adj[path[i]][path[i + 1]]['branch'].type[
'L_per_km'] * 1e-3 * omega * \
G.adj[path[i]][path[i + 1]][
'branch'].length) *1j + \
(G.adj[path[i]][path[i + 1]]['branch'].type[
'R_per_km'] * \
G.adj[path[i]][path[i + 1]][
'branch'].length)
mv_path_length += G.adj[path[i]][path[i + 1]][
'branch'].length
mv_impedances[node] = abs(mv_impedance)
mv_path_lengths[node] = mv_path_length
mv_thermal_limit = G.adj[path[0]][path[1]]['branch'].type['I_max_th']
mv_thermal_limits[node] = mv_thermal_limit
if isinstance(node, LVStationDing0):
# add impedance of transformers in LV station
lvstation_impedance = 0.
for trafo in node.transformers():
lvstation_impedance += 1. / trafo.z() # transformers operating in parallel
if lvstation_impedance > 0.: # avoid dividing by zero
lvstation_impedance = 1. / lvstation_impedance
else:
lvstation_impedance = 0.
# identify LV nodes belonging to LV station
G_lv = node.grid._graph
# loop over all LV terminal nodes belonging to LV station
for lv_node in G_lv.nodes():
if isinstance(lv_node, GeneratorDing0) or isinstance(lv_node, LVLoadDing0):
lv_path = nx.shortest_path(G_lv, node, lv_node)
lv_impedance = lvstation_impedance
lv_path_length = 0.
for i in range(len(lv_path)-1):
lv_impedance += (G_lv.adj[lv_path[i]][lv_path[i+1]]['branch'].type['L_per_km'] * 1e-3 * omega * \
G_lv.adj[lv_path[i]][lv_path[i+1]]['branch'].length) *1j + \
(G_lv.adj[lv_path[i]][lv_path[i+1]]['branch'].type['R_per_km'] * \
G_lv.adj[lv_path[i]][lv_path[i+1]]['branch'].length)
lv_path_length += G_lv.adj[lv_path[i]][lv_path[i+1]]['branch'].length
lv_thermal_limit = G_lv.adj[lv_path[0]][lv_path[1]]['branch'].type['I_max_th']
mvlv_impedances[lv_node] = abs( mv_impedance + lv_impedance )
mvlv_path_lengths[lv_node] = mv_path_length + lv_path_length
lv_thermal_limits[lv_node] = lv_thermal_limit
mvlv_thermal_limits[lv_node] = mv_thermal_limit
elif isinstance(lv_node, LVStationDing0):
n_outgoing_LV += len(list(G_lv.neighbors(lv_node)))
n_stations_LV += 1
# compute mean values by looping over terminal nodes
sum_impedances = 0.
sum_thermal_limits = 0.
sum_path_lengths = 0.
n_terminal_nodes_MV = 0
# terminal nodes on MV
for terminal_node in mv_impedances.keys(): # neglect LVStations here because already part of MVLV paths below
if not isinstance(terminal_node, LVStationDing0) and not isinstance(terminal_node, MVStationDing0):
sum_impedances += mv_impedances[terminal_node]
sum_thermal_limits += mv_thermal_limits[terminal_node]
sum_path_lengths += mv_path_lengths[terminal_node]
n_terminal_nodes_MV += 1
sum_thermal_limits_LV = 0.
n_terminal_nodes_LV = 0
# terminal nodes on LV
for terminal_node in mvlv_impedances.keys():
sum_impedances += mvlv_impedances[terminal_node]
sum_thermal_limits += mvlv_thermal_limits[terminal_node]
sum_thermal_limits_LV += lv_thermal_limits[terminal_node]
sum_path_lengths += mvlv_path_lengths[terminal_node]
n_terminal_nodes_LV += 1
print(n_terminal_nodes_LV)
print(n_terminal_nodes_MV)
n_terminal_nodes = n_terminal_nodes_MV + n_terminal_nodes_LV
if n_terminal_nodes < 1:
mean_impedance = np.nan
mean_thermal_limit = np.nan
mean_path_length = np.nan
else:
mean_impedance = sum_impedances / n_terminal_nodes
mean_thermal_limit = sum_thermal_limits / n_terminal_nodes
mean_path_length = sum_path_lengths / n_terminal_nodes
if n_terminal_nodes_LV < 1:
mean_thermal_limit_LV = np.nan
else:
mean_thermal_limit_LV = sum_thermal_limits_LV / n_terminal_nodes_LV
number_outgoing_LV = n_outgoing_LV # / n_stations_LV
number_outgoing_MV = n_outgoing_MV
###################################
# compute path lengths (written by Miguel)
max_mv_path = 0
max_mvlv_path = 0
# rings
nodes_in_rings = []
branches_in_rings = []
for ring in district.mv_grid.rings_full_data():
ring_idx += 1
# generation cap
ring_gen = 0
for node in ring[2]:
nodes_in_rings.append(node)
if isinstance(node, GeneratorDing0):
ring_gen += node.capacity
# length
ring_length = 0
for branch in ring[1]:
branches_in_rings.append(branch)
ring_length += branch.length / 1e3
ring_dict[ring_idx] = {
'grid_id': district.mv_grid.id_db,
'ring_length': ring_length,
'ring_capacity': ring_gen,
}
# transformers in main station
for trafo in district.mv_grid.station().transformers():
trafos_idx += 1
trafos_dict[trafos_idx] = {
'grid_id': district.mv_grid.id_db,
's_max_a': trafo.s_max_a}
# Generators and other MV special nodes
cd_count = 0
LVs_count = 0
cb_count = 0
lv_trafo_count = 0
lv_trafo_cap = 0
for node in district.mv_grid._graph.nodes():
mv_path_length = 0
mvlv_path_length = 0
if isinstance(node, GeneratorDing0):
gen_idx += 1
isolation = not node in nodes_in_rings
subtype = node.subtype
if subtype == None:
subtype = 'other'
generators_dict[gen_idx] = {
'grid_id': district.mv_grid.id_db,
'type': node.type,
'sub_type': node.type + '/' + subtype,
'gen_cap': node.capacity,
'v_level': node.v_level,
'isolation': isolation,
}
mv_path_length = district.mv_grid.graph_path_length(
node_source=root,
node_target=node)
elif isinstance(node, MVCableDistributorDing0):
cd_count += 1
elif isinstance(node, LVStationDing0):
LVs_count += 1
lv_trafo_count += len([trafo for trafo in node.transformers()])
lv_trafo_cap += np.sum([trafo.s_max_a for trafo in node.transformers()])
if not node.lv_load_area.is_aggregated:
mv_path_length = district.mv_grid.graph_path_length(
node_source=root,
node_target=node)
max_lv_path = 0
for lv_LA in district.lv_load_areas():
for lv_dist in lv_LA.lv_grid_districts():
if lv_dist.lv_grid._station == node:
for lv_node in lv_dist.lv_grid._graph.nodes():
lv_path_length = lv_dist.lv_grid.graph_path_length(
node_source=node,
node_target=lv_node)
max_lv_path = max(max_lv_path, lv_path_length)
mvlv_path_length = mv_path_length + max_lv_path
elif isinstance(node, CircuitBreakerDing0):
cb_count += 1
max_mv_path = max(max_mv_path, mv_path_length / 1e3)
max_mvlv_path = max(max_mvlv_path, mvlv_path_length / 1e3)
other_nodes_dict[district.mv_grid.id_db] = {
'CD_count': cd_count,
'LV_count': LVs_count,
'CB_count': cb_count,
'MVLV_trafo_count': lv_trafo_count,
'MVLV_trafo_cap': lv_trafo_cap,
'max_mv_path': max_mv_path,
'max_mvlv_path': max_mvlv_path,
'mean_impedance': mean_impedance,
'mean_thermal_limit': mean_thermal_limit,
'mean_thermal_limit_LV': mean_thermal_limit_LV,
'mean_path_length': mean_path_length / 1.e3,
'number_outgoing_LV': number_outgoing_LV,
'number_outgoing_MV': number_outgoing_MV
}
# branches
for branch in district.mv_grid.graph_edges():
branch_idx += 1
br_in_ring = branch['branch'] in branches_in_rings
branches_dict[branch_idx] = {
'grid_id': district.mv_grid.id_db,
'length': branch['branch'].length / 1e3,
'type_name': branch['branch'].type['name'],
'type_kind': branch['branch'].kind,
'in_ring': br_in_ring,
}
# Load Areas
for LA in district.lv_load_areas():
LA_idx += 1
LA_dict[LA_idx] = {
'grid_id': district.mv_grid.id_db,
'is_agg': LA.is_aggregated,
'is_sat': LA.is_satellite,
# 'peak_gen':LA.peak_generation,
}
LA_pop = 0
residential_peak_load = 0
retail_peak_load = 0
industrial_peak_load = 0
agricultural_peak_load = 0
lv_gen_level_6 = 0
lv_gen_level_7 = 0
for lv_district in LA.lv_grid_districts():
LA_pop = + lv_district.population
residential_peak_load += lv_district.peak_load_residential
retail_peak_load += lv_district.peak_load_retail
industrial_peak_load += lv_district.peak_load_industrial
agricultural_peak_load += lv_district.peak_load_agricultural
# generation capacity
for g in lv_district.lv_grid.generators():
if g.v_level == 6:
lv_gen_level_6 += g.capacity
elif g.v_level == 7:
lv_gen_level_7 += g.capacity
# branches lengths
for br in lv_district.lv_grid.graph_edges():
lv_branches_idx += 1
lv_branches_dict[lv_branches_idx] = {
'grid_id': district.mv_grid.id_db,
'length': br['branch'].length / 1e3,
'type_name': br['branch'].type.to_frame().columns[0], # why is it different as for MV grids? can be replaced by br['branch'].type.name
'type_kind': br['branch'].kind,
}
LA_dict[LA_idx].update({
'population': LA_pop,
'residential_peak_load': residential_peak_load,
'retail_peak_load': retail_peak_load,
'industrial_peak_load': industrial_peak_load,
'agricultural_peak_load': agricultural_peak_load,
'total_peak_load': residential_peak_load + retail_peak_load + \
industrial_peak_load + agricultural_peak_load,
'lv_generation': lv_gen_level_6 + lv_gen_level_7,
'lv_gens_lvl_6': lv_gen_level_6,
'lv_gens_lvl_7': lv_gen_level_7,
})
# geographic
# ETRS (equidistant) to WGS84 (conformal) projection
proj = partial(
pyproj.transform,
# pyproj.Proj(init='epsg:3035'), # source coordinate system
# pyproj.Proj(init='epsg:4326')) # destination coordinate system
pyproj.Proj(init='epsg:4326'), # source coordinate system
pyproj.Proj(init='epsg:3035')) # destination coordinate system
district_geo = transform(proj, district.geo_data)
other_nodes_dict[district.mv_grid.id_db].update({'Dist_area': district_geo.area})
mvgd_stats = pd.DataFrame.from_dict({}, orient='index')
###################################
# built dataframes from dictionaries
trafos_df = pd.DataFrame.from_dict(trafos_dict, orient='index')
generators_df = pd.DataFrame.from_dict(generators_dict, orient='index')
other_nodes_df = pd.DataFrame.from_dict(other_nodes_dict, orient='index')
branches_df = pd.DataFrame.from_dict(branches_dict, orient='index')
lv_branches_df = pd.DataFrame.from_dict(lv_branches_dict, orient='index')
ring_df = pd.DataFrame.from_dict(ring_dict, orient='index')
LA_df = pd.DataFrame.from_dict(LA_dict, orient='index')
###################################
# Aggregated data HV/MV Trafos
if not trafos_df.empty:
mvgd_stats = pd.concat([mvgd_stats, trafos_df.groupby('grid_id').count()['s_max_a']], axis=1)
mvgd_stats = pd.concat([mvgd_stats, trafos_df.groupby('grid_id').sum()[['s_max_a']]], axis=1)
mvgd_stats.columns = ['Number of HV/MV Trafos', 'Trafos HV/MV Acc s_max_a']
###################################
# Aggregated data Generators
if not generators_df.empty:
# MV generation per sub_type
mv_generation = generators_df.groupby(['grid_id', 'sub_type'])['gen_cap'].sum().to_frame().unstack(level=-1)
mv_generation.columns = ['Gen. Cap. of MV ' + _[1] if isinstance(_, tuple) else _
for _ in mv_generation.columns]
mvgd_stats = pd.concat([mvgd_stats, mv_generation], axis=1)
# MV generation at V levels
mv_generation = generators_df.groupby(
['grid_id', 'v_level'])['gen_cap'].sum().to_frame().unstack(level=-1)
mv_generation.columns = ['Gen. Cap. of MV at v_level ' + str(_[1])
if isinstance(_, tuple) else _
for _ in mv_generation.columns]
mvgd_stats = pd.concat([mvgd_stats, mv_generation], axis=1)
# Isolated generators
mv_generation = generators_df[generators_df['isolation']].groupby(
['grid_id'])['gen_cap'].count().to_frame() # .unstack(level=-1)
mv_generation.columns = ['Number of isolated MV Generators']
mvgd_stats = pd.concat([mvgd_stats, mv_generation], axis=1)
###################################
# Aggregated data of other nodes
if not other_nodes_df.empty:
# print(other_nodes_df['CD_count'].to_frame())
mvgd_stats['Number of Cable Distr'] = other_nodes_df['CD_count'].to_frame().astype(int)
mvgd_stats['Number of LV Stations'] = other_nodes_df['LV_count'].to_frame().astype(int)
mvgd_stats['Number of Circuit Breakers'] = other_nodes_df['CB_count'].to_frame().astype(int)
mvgd_stats['District Area'] = other_nodes_df['Dist_area'].to_frame()
mvgd_stats['Number of MV/LV Trafos'] = other_nodes_df['MVLV_trafo_count'].to_frame().astype(int)
mvgd_stats['Trafos MV/LV Acc s_max_a'] = other_nodes_df['MVLV_trafo_cap'].to_frame()
mvgd_stats['Length of MV max path'] = other_nodes_df['max_mv_path'].to_frame()
mvgd_stats['Length of MVLV max path'] = other_nodes_df['max_mvlv_path'].to_frame()
mvgd_stats['Impedance Z of path to terminal node (mean value)'] = \
other_nodes_df['mean_impedance'].to_frame()
mvgd_stats['I_max of first segment of path from MV station to terminal node (mean value)'] = \
other_nodes_df['mean_thermal_limit'].to_frame()
mvgd_stats['I_max of first segment of path from LV station to terminal node (mean value)'] = \
other_nodes_df['mean_thermal_limit_LV'].to_frame()
mvgd_stats['Length of path from MV station to terminal node (mean value)'] = \
other_nodes_df['mean_path_length'].to_frame()
mvgd_stats['Number of lines and cables going out from LV stations'] = \
other_nodes_df['number_outgoing_LV'].to_frame()
mvgd_stats['Number of lines and cables going out from MV stations'] = \
other_nodes_df['number_outgoing_MV'].to_frame()
###################################
# Aggregated data of MV Branches
if not branches_df.empty:
# km of underground cable
branches_data = branches_df[branches_df['type_kind'] == 'cable'].groupby(
['grid_id'])['length'].sum().to_frame()
branches_data.columns = ['Length of MV underground cables']
mvgd_stats = pd.concat([mvgd_stats, branches_data], axis=1)
# km of overhead lines
branches_data = branches_df[branches_df['type_kind'] == 'line'].groupby(
['grid_id'])['length'].sum().to_frame()
branches_data.columns = ['Length of MV overhead lines']
mvgd_stats = pd.concat([mvgd_stats, branches_data], axis=1)
# km of different wire types
branches_data = branches_df.groupby(
['grid_id', 'type_name'])['length'].sum().to_frame().unstack(level=-1)
branches_data.columns = ['Length of MV type ' + _[1] if isinstance(_, tuple) else _
for _ in branches_data.columns]
mvgd_stats = pd.concat([mvgd_stats, branches_data], axis=1)
# branches not in ring
total_br = branches_df.groupby(['grid_id'])['length'].count().to_frame()
ring_br = branches_df[branches_df['in_ring']].groupby(
['grid_id'])['length'].count().to_frame()
branches_data = total_br - ring_br
total_br.columns = ['Number of MV branches']
mvgd_stats = pd.concat([mvgd_stats, total_br], axis=1)
branches_data.columns = ['Number of MV branches not in a ring']
mvgd_stats = pd.concat([mvgd_stats, branches_data], axis=1)
###################################
# Aggregated data of LV Branches
if not lv_branches_df.empty:
# km of underground cable
lv_branches_data = lv_branches_df[lv_branches_df['type_kind'] == 'cable'].groupby(
['grid_id'])['length'].sum().to_frame()
lv_branches_data.columns = ['Length of LV underground cables']
mvgd_stats = pd.concat([mvgd_stats, lv_branches_data], axis=1)
# km of overhead lines
lv_branches_data = lv_branches_df[lv_branches_df['type_kind'] == 'line'].groupby(
['grid_id'])['length'].sum().to_frame()
lv_branches_data.columns = ['Length of LV overhead lines']
mvgd_stats = pd.concat([mvgd_stats, lv_branches_data], axis=1)
# km of different wire types
lv_branches_data = lv_branches_df.groupby(
['grid_id', 'type_name'])['length'].sum().to_frame().unstack(level=-1)
lv_branches_data.columns = ['Length of LV type ' + _[1] if isinstance(_, tuple) else _
for _ in lv_branches_data.columns]
mvgd_stats = pd.concat([mvgd_stats, lv_branches_data], axis=1)
# n° of branches
total_lv_br = lv_branches_df.groupby(['grid_id'])['length'].count().to_frame()
total_lv_br.columns = ['Number of LV branches']
mvgd_stats = pd.concat([mvgd_stats, total_lv_br], axis=1)
###################################
# Aggregated data of Rings
if not ring_df.empty:
# N° of rings
ring_data = ring_df.groupby(['grid_id'])['grid_id'].count().to_frame()
ring_data.columns = ['Number of MV Rings']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
# min,max,mean km of all rings
ring_data = ring_df.groupby(['grid_id'])['ring_length'].min().to_frame()
ring_data.columns = ['Length of MV Ring min']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
ring_data = ring_df.groupby(['grid_id'])['ring_length'].max().to_frame()
ring_data.columns = ['Length of MV Ring max']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
ring_data = ring_df.groupby(['grid_id'])['ring_length'].mean().to_frame()
ring_data.columns = ['Length of MV Ring mean']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
# km of all rings
ring_data = ring_df.groupby(['grid_id'])['ring_length'].sum().to_frame()
ring_data.columns = ['Length of MV Rings total']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
# km of non-ring
non_ring_data = branches_df.groupby(['grid_id'])['length'].sum().to_frame()
non_ring_data.columns = ['Length of MV Rings total']
ring_data = non_ring_data - ring_data
ring_data.columns = ['Length of MV Non-Rings total']
mvgd_stats = pd.concat([mvgd_stats, ring_data.round(1).abs()], axis=1)
# rings generation capacity
ring_data = ring_df.groupby(['grid_id'])['ring_capacity'].sum().to_frame()
ring_data.columns = ['Gen. Cap. Connected to MV Rings']
mvgd_stats = pd.concat([mvgd_stats, ring_data], axis=1)
###################################
# Aggregated data of Load Areas
if not LA_df.empty:
LA_data = LA_df.groupby(['grid_id'])['population'].count().to_frame()
LA_data.columns = ['Number of Load Areas']
mvgd_stats = pd.concat([mvgd_stats, LA_data], axis=1)
LA_data = LA_df.groupby(['grid_id'])['population',
'residential_peak_load',
'retail_peak_load',
'industrial_peak_load',
'agricultural_peak_load',
'total_peak_load',