import numpy as np
from pydgrid.pydgrid import grid

CIGRE LV European System

grid_1 = grid()
grid_1.read('cigre_europe_residential.json')  # Load data
grid_1.pf()

Graph with obtained results

from pydgrid.plot_bokeh import plot_results
plot_results(grid_1)

Loading BokehJS ...

Figure(

id = '1310', …)

above = [],

aspect_scale = 1,

background_fill_alpha = {'value': 1.0},

background_fill_color = {'value': '#ffffff'},

below = [LinearAxis(id='1320', ...)],

border_fill_alpha = {'value': 1.0},

border_fill_color = {'value': '#ffffff'},

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

h_symmetry = True,

height = None,

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='1325', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

match_aspect = False,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

name = None,

outline_line_alpha = {'value': 1.0},

outline_line_cap = 'butt',

outline_line_color = {'value': '#e5e5e5'},

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = {'value': 1},

output_backend = 'canvas',

plot_height = 400,

plot_width = 600,

renderers = [LinearAxis(id='1320', ...), Grid(id='1324', ...), LinearAxis(id='1325', ...), Grid(id='1329', ...), BoxAnnotation(id='1338', ...), GlyphRenderer(id='1349', ...), GlyphRenderer(id='1355', ...), GlyphRenderer(id='1361', ...)],

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1309', ...),

title_location = 'above',

toolbar = Toolbar(id='1336', ...),

toolbar_location = 'right',

toolbar_sticky = True,

v_symmetry = False,

width = None,

x_range = DataRange1d(id='1312', ...),

x_scale = LinearScale(id='1316', ...),

y_range = DataRange1d(id='1314', ...),

y_scale = LinearScale(id='1318', ...))

Analysis using Pandas

import pandas as pd

df = pd.DataFrame()
df['nodes'] = sys1.nodes
df['I_node_m'] = np.abs(sys1.I_node)
df['V_node_m'] = np.abs(sys1.V_node)
#df['phi'] = np.angle(sys1.I_node, deg=True) - np.angle(sys1.V_node, deg=True)
s  = np.conjugate(sys1.I_node)*sys1.V_node
df['p_kW']  = s.real/1000
df['q_kVA'] = s.imag/1000

df

	nodes	I_node_m	V_node_m	p_kW	q_kVA
0	R0.1	12.279686	11547.000000	133.228235	48.534983
1	R0.2	12.194776	11547.000000	132.224879	48.424213
2	R0.3	12.219433	11547.000000	132.822486	47.610666
3	R1.1	294.305597	226.183736	-63.206394	-20.883865
4	R1.2	294.318022	226.377454	-63.338571	-20.673117
5	R1.3	294.268246	227.024469	-63.455017	-20.892937
6	R1.4	0.129777	0.488038	-0.000018	-0.000061
7	R11.1	22.619924	221.082177	-4.751117	-1.560611
8	R11.2	22.542696	221.818706	-4.749990	-1.562532
9	R11.3	22.431808	222.842141	-4.748898	-1.560605
10	R11.4	0.111885	0.056907	0.000006	-0.000001
11	R15.1	82.678522	210.089330	-16.512848	-5.388732
12	R15.2	82.063130	211.381109	-16.463661	-5.463720
13	R15.3	81.216693	212.814945	-16.424028	-5.384451
14	R15.4	0.869274	0.627272	0.000537	-0.000093
15	R16.1	85.643246	214.560691	-17.470587	-5.695963
16	R16.2	84.972116	215.951166	-17.414043	-5.785090
17	R16.3	84.050216	217.432385	-17.366033	-5.692562
18	R16.4	0.947507	0.712773	0.000663	-0.000129
19	R17.1	55.050752	212.626217	-11.132419	-3.616867
20	R17.2	54.506010	214.317220	-11.080836	-3.697878
21	R17.3	53.767616	216.004660	-11.037499	-3.613848
22	R17.4	0.762900	1.009420	0.000755	-0.000153
23	R18.1	74.293759	211.633036	-14.954995	-4.853998
24	R18.2	73.514087	213.405120	-14.879548	-4.972049
25	R18.3	72.461012	215.150835	-14.816623	-4.849461
26	R18.4	1.089448	1.091264	0.001165	-0.000237
27	R2.1	0.000000	223.960103	0.000000	-0.000000
28	R2.2	0.000000	224.415535	-0.000000	0.000000
29	R2.3	0.000000	225.243290	0.000000	0.000000
...	...	...	...	...	...
45	R6.3	0.000000	219.425766	0.000000	0.000000
46	R6.4	0.000000	0.634712	0.000000	-0.000000
47	R7.1	0.000000	215.787875	0.000000	-0.000000
48	R7.2	0.000000	217.212013	-0.000000	0.000000
49	R7.3	0.000000	218.710282	0.000000	0.000000
50	R7.4	0.000000	0.741778	0.000000	-0.000000
51	R8.1	0.000000	214.889967	0.000000	-0.000000
52	R8.2	0.000000	216.422120	-0.000000	0.000000
53	R8.3	0.000000	217.994913	0.000000	0.000000
54	R8.4	0.000000	0.848844	0.000000	-0.000000
55	R9.1	0.000000	213.992505	0.000000	-0.000000
56	R9.2	0.000000	215.631806	-0.000000	0.000000
57	R9.3	0.000000	217.279749	0.000000	0.000000
58	R9.4	0.000000	0.956068	0.000000	-0.000000
59	R10.1	0.000000	213.476942	0.000000	-0.000000
60	R10.2	0.000000	215.178180	-0.000000	0.000000
61	R10.3	0.000000	216.869265	0.000000	0.000000
62	R10.4	0.000000	1.017707	0.000000	-0.000000
63	R12.1	0.000000	217.273533	0.000000	-0.000000
64	R12.2	0.000000	218.317615	-0.000000	0.000000
65	R12.3	0.000000	219.559931	0.000000	0.000000
66	R12.4	0.000000	0.367154	0.000000	-0.000000
67	R13.1	0.000000	214.877482	0.000000	-0.000000
68	R13.2	0.000000	216.004358	-0.000000	0.000000
69	R13.3	0.000000	217.310579	0.000000	0.000000
70	R13.4	0.000000	0.453609	0.000000	-0.000000
71	R14.1	0.000000	212.482806	0.000000	-0.000000
72	R14.2	0.000000	213.692088	-0.000000	0.000000
73	R14.3	0.000000	215.062258	0.000000	0.000000
74	R14.4	0.000000	0.540432	0.000000	-0.000000

75 rows × 5 columns