/
results_branch.py
721 lines (610 loc) · 32.3 KB
/
results_branch.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2024 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.
import numpy as np
import pandas as pd
from pandapower.auxiliary import _sum_by_group, I_from_SV_elementwise, sequence_to_phase, S_from_VI_elementwise
from pandapower.pypower.idx_brch import F_BUS, T_BUS, PF, QF, PT, QT, BR_R
from pandapower.pypower.idx_brch_tdpf import TDPF
from pandapower.pypower.idx_bus import BASE_KV, VM, VA
from pandapower.pypower.idx_tcsc import TCSC_THYRISTOR_FIRING_ANGLE, TCSC_X_PU, TCSC_PF, TCSC_PT, TCSC_QF, TCSC_QT, \
TCSC_IF, TCSC_IT
def _get_branch_results(net, ppc, bus_lookup_aranged, pq_buses, suffix=None):
"""
Extract the branch results and writes it in the Dataframe net.res_line, net.res_trafo, net.res_trafo3w...
INPUT:
**net** - pandapower net
**ppc** (np.array) - ppc structure
**bus_lookup_aranged** - consecutive aranged bus lookup
**pq_buses** - the PQ type buses in ppc
OPTIONAL:
**suffix** (str, None) - a suffix like "_sc" to write to res_line_sc for example (short circuit)
"""
i_ft, s_ft = _get_branch_flows(ppc)
_get_line_results(net, ppc, i_ft, suffix=suffix)
_get_trafo_results(net, ppc, s_ft, i_ft, suffix=suffix)
_get_trafo3w_results(net, ppc, s_ft, i_ft, suffix=suffix)
_get_impedance_results(net, ppc, i_ft, suffix=suffix)
_get_xward_branch_results(net, ppc, bus_lookup_aranged, pq_buses, suffix=suffix)
_get_switch_results(net, ppc, i_ft, suffix=suffix)
_get_tcsc_results(net, ppc, suffix=suffix)
def _get_branch_results_3ph(net, ppc0, ppc1, ppc2, bus_lookup_aranged, pq_buses):
"""
Extract the bus results and writes it in the Dataframe net.res_line and net.res_trafo.
INPUT:
**results** - the result of runpf loadflow calculation
**p** - the dict to dump the "res_line" and "res_trafo" Dataframe
"""
I012_f, S012_f, V012_f, I012_t, S012_t, V012_t = _get_branch_flows_3ph(ppc0, ppc1, ppc2)
_get_line_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t, V012_t)
_get_trafo_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t, V012_t)
# _get_trafo3w_results(net, ppc, s_ft, i_ft)
# _get_impedance_results(net, ppc, i_ft)
# _get_xward_branch_results(net, ppc, bus_lookup_aranged, pq_buses)
# _get_switch_results(net, i_ft)
def _get_branch_flows(ppc):
br_idx = ppc["branch"][:, (F_BUS, T_BUS)].real.astype(np.int64)
vm_ft = ppc["bus"][br_idx, VM] * ppc["bus"][br_idx, BASE_KV]
s_ft = np.sqrt(ppc["branch"][:, (PF, PT)].real ** 2 +
ppc["branch"][:, (QF, QT)].real ** 2)
i_ft = s_ft / vm_ft / np.sqrt(3)
return i_ft, s_ft
def _get_branch_flows_3ph(ppc0, ppc1, ppc2):
br_from_idx = ppc1["branch"][:, F_BUS].real.astype(np.int64)
br_to_idx = ppc1["branch"][:, T_BUS].real.astype(np.int64)
V012_f = np.array([(ppc["bus"][br_from_idx, VM] * ppc["bus"][br_from_idx, BASE_KV] *
np.exp(1j * np.deg2rad(ppc["bus"][br_from_idx, VA]))).flatten() for ppc in [ppc0, ppc1, ppc2]])
V012_t = np.array([(ppc["bus"][br_to_idx, VM] * ppc["bus"][br_to_idx, BASE_KV] *
np.exp(1j * np.deg2rad(ppc["bus"][br_to_idx, VA]))).flatten() for ppc in [ppc0, ppc1, ppc2]])
S012_f = np.array([((ppc["branch"][:, PF].real +
1j * ppc["branch"][:, QF].real))
for ppc in [ppc0, ppc1, ppc2]])
S012_t = np.array([((ppc["branch"][:, PT].real +
1j * ppc["branch"][:, QT].real))
for ppc in [ppc0, ppc1, ppc2]])
I012_f = I_from_SV_elementwise(S012_f, V012_f / np.sqrt(3))
I012_t = I_from_SV_elementwise(S012_t, V012_t / np.sqrt(3))
return I012_f, S012_f, V012_f, I012_t, S012_t, V012_t
def _get_line_results(net, ppc, i_ft, suffix=None):
# create res_line_vals which are written to the pandas dataframe
if "line" not in net._pd2ppc_lookups["branch"]:
return
ac = net["_options"]["ac"]
f, t = net._pd2ppc_lookups["branch"]["line"]
pf_mw = ppc["branch"][f:t, PF].real
q_from_mvar = ppc["branch"][f:t, QF].real
p_from_mw = pf_mw
pt_mw = ppc["branch"][f:t, PT].real
q_to_mvar = ppc["branch"][f:t, QT].real
p_to_mw = pt_mw
if ac:
pl_mw = pf_mw + pt_mw
ql_mvar = q_from_mvar + q_to_mvar
else:
pl_mw = np.zeros_like(pf_mw)
ql_mvar = np.zeros_like(q_from_mvar)
with np.errstate(invalid='ignore'):
i_ka = np.max(i_ft[f:t], axis=1)
i_from_ka = i_ft[f:t][:, 0]
i_to_ka = i_ft[f:t][:, 1]
line_df = net["line"]
i_max = line_df["max_i_ka"].values * line_df["df"].values * line_df["parallel"].values
from_bus = ppc["branch"][f:t, F_BUS].real.astype(np.int64)
to_bus = ppc["branch"][f:t, T_BUS].real.astype(np.int64)
# write to line
res_line_df = net["res_line"] if suffix is None else net["res_line%s" % suffix]
res_line_df["p_from_mw"].values[:] = p_from_mw
res_line_df["q_from_mvar"].values[:] = q_from_mvar
res_line_df["p_to_mw"].values[:] = p_to_mw
res_line_df["q_to_mvar"].values[:] = q_to_mvar
res_line_df["pl_mw"].values[:] = pl_mw
res_line_df["ql_mvar"].values[:] = ql_mvar
res_line_df["i_from_ka"].values[:] = i_from_ka
res_line_df["i_to_ka"].values[:] = i_to_ka
res_line_df["i_ka"].values[:] = i_ka
res_line_df["vm_from_pu"].values[:] = ppc["bus"][from_bus, VM]
res_line_df["va_from_degree"].values[:] = ppc["bus"][from_bus, VA]
res_line_df["vm_to_pu"].values[:] = ppc["bus"][to_bus, VM]
res_line_df["va_to_degree"].values[:] = ppc["bus"][to_bus, VA]
loading = np.full_like(i_ka, fill_value=np.inf, dtype=np.float64)
np.divide(i_ka, i_max, where=i_max != 0, out=loading, dtype=np.float64)
res_line_df["loading_percent"].values[:] = loading * 100
# if consider_line_temperature, add resulting r_ohm_per_km to net.res_line
if net["_options"]["consider_line_temperature"] or net["_options"].get("tdpf", False):
base_kv = ppc["bus"][from_bus, BASE_KV]
baseR = np.square(base_kv) / net.sn_mva
length_km = line_df.length_km.values
parallel = line_df.parallel.values
res_line_df["r_ohm_per_km"] = ppc["branch"][f:t, BR_R].real / length_km * baseR * parallel
if net["_options"].get("tdpf", False):
tdpf_lines = ppc["internal"]['branch_is'][f:t] & np.nan_to_num(ppc['branch'][f:t, TDPF]).real.astype(bool)
res_line_df.loc[tdpf_lines, "r_theta_kelvin_per_mw"] = ppc["internal"]["r_theta_kelvin_per_mw"]
no_tdpf_t = line_df.loc[~tdpf_lines].get("temperature_degree_celsius", default=20.)
res_line_df.loc[tdpf_lines, "temperature_degree_celsius"] = ppc["internal"]["T"]
res_line_df.loc[~tdpf_lines, "temperature_degree_celsius"] = no_tdpf_t
def _get_line_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t, V012_t):
# create res_line_vals which are written to the pandas dataframe
ac = net["_options"]["ac"]
if not "line" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["line"]
I012_from_ka = I012_f[:, f:t]
I012_to_ka = I012_t[:, f:t]
line_df = net["line"]
i_max_phase = line_df["max_i_ka"].values * line_df["df"].values * line_df["parallel"].values
Vabc_f, Vabc_t, Iabc_f, Iabc_t = [sequence_to_phase(X012) for X012 in
[V012_f[:, f:t], V012_t[:, f:t], I012_f[:, f:t], I012_t[:, f:t]]]
Sabc_f, Sabc_t = [S_from_VI_elementwise(*Xabc_tup) / np.sqrt(3) for Xabc_tup in
[(Vabc_f, Iabc_f), (Vabc_t, Iabc_t)]]
# Todo: Check why the sqrt(3) is necessary in the previous line as opposed to _get_line_results()
Pabcf_mw = Sabc_f.real
Qabcf_mvar = Sabc_f.imag
Pabct_mw = Sabc_t.real
Qabct_mvar = Sabc_t.imag
if ac:
Pabcl_mw = Pabcf_mw + Pabct_mw
Qabcl_mvar = Qabcf_mvar + Qabct_mvar
else:
Pabcl_mw = np.zeros_like(Pabcf_mw)
Qabcl_mvar = np.zeros_like(Qabct_mvar)
# getting complex values of the sequence current line
Iabc_f_ka_complex = sequence_to_phase(I012_from_ka)
Iabc_t_ka_complex = sequence_to_phase(I012_to_ka)
Iabc_f_ka = np.abs(Iabc_f_ka_complex)
Iabc_t_ka = np.abs(Iabc_t_ka_complex)
Iabc_ka = np.maximum.reduce([Iabc_t_ka, Iabc_f_ka])
In_f_ka_complex = Iabc_f_ka_complex.sum(axis=0)
In_f_ka = np.abs(In_f_ka_complex)
# In_f_ia_n_degree = np.angle(In_f_ka_complex).flatten()*180/np.pi
In_t_ka_complex = Iabc_t_ka_complex.sum(axis=0)
# In_t_ia_n_degree = np.angle(In_t_ka_complex).flatten()*180/np.pi
In_t_ka = np.abs(In_t_ka_complex)
In_ka = np.maximum.reduce([In_t_ka, In_f_ka])
# write to line
net["res_line_3ph"]["p_a_from_mw"] = Pabcf_mw[0, :].flatten()
net["res_line_3ph"]["p_b_from_mw"] = Pabcf_mw[1, :].flatten()
net["res_line_3ph"]["p_c_from_mw"] = Pabcf_mw[2, :].flatten()
net["res_line_3ph"]["q_a_from_mvar"] = Qabcf_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_from_mvar"] = Qabcf_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_from_mvar"] = Qabcf_mvar[2, :].flatten()
net["res_line_3ph"]["p_a_to_mw"] = Pabct_mw[0, :].flatten()
net["res_line_3ph"]["p_b_to_mw"] = Pabct_mw[1, :].flatten()
net["res_line_3ph"]["p_c_to_mw"] = Pabct_mw[2, :].flatten()
net["res_line_3ph"]["q_a_to_mvar"] = Qabct_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_to_mvar"] = Qabct_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_to_mvar"] = Qabct_mvar[2, :].flatten()
net["res_line_3ph"]["p_a_l_mw"] = Pabcl_mw[0, :].flatten()
net["res_line_3ph"]["p_b_l_mw"] = Pabcl_mw[1, :].flatten()
net["res_line_3ph"]["p_c_l_mw"] = Pabcl_mw[2, :].flatten()
net["res_line_3ph"]["q_a_l_mvar"] = Qabcl_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_l_mvar"] = Qabcl_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_l_mvar"] = Qabcl_mvar[2, :].flatten()
net["res_line_3ph"]["i_a_from_ka"] = Iabc_f_ka[0, :].flatten()
net["res_line_3ph"]["i_b_from_ka"] = Iabc_f_ka[1, :].flatten()
net["res_line_3ph"]["i_c_from_ka"] = Iabc_f_ka[2, :].flatten()
net["res_line_3ph"]["i_a_to_ka"] = Iabc_t_ka[0, :].flatten()
net["res_line_3ph"]["i_b_to_ka"] = Iabc_t_ka[1, :].flatten()
net["res_line_3ph"]["i_c_to_ka"] = Iabc_t_ka[2, :].flatten()
net["res_line_3ph"]["i_a_ka"] = Iabc_ka[0, :]
net["res_line_3ph"]["i_b_ka"] = Iabc_ka[1, :]
net["res_line_3ph"]["i_c_ka"] = Iabc_ka[2, :]
net["res_line_3ph"]["i_n_from_ka"] = In_f_ka
net["res_line_3ph"]["i_n_to_ka"] = In_t_ka
net["res_line_3ph"]["i_n_ka"] = In_ka
net["res_line_3ph"]["loading_a_percent"] = Iabc_ka[0, :] / i_max_phase * 100
net["res_line_3ph"]["loading_b_percent"] = Iabc_ka[1, :] / i_max_phase * 100
net["res_line_3ph"]["loading_c_percent"] = Iabc_ka[2, :] / i_max_phase * 100
net["res_line_3ph"]["loading_percent"] = Iabc_ka.max(axis=0) / i_max_phase * 100
net["res_line_3ph"].index = net["line"].index
def _get_trafo_results(net, ppc, s_ft, i_ft, suffix=None):
if "trafo" not in net._pd2ppc_lookups["branch"]:
return
ac = net["_options"]["ac"]
trafo_loading = net["_options"]["trafo_loading"]
f, t = net._pd2ppc_lookups["branch"]["trafo"]
p_hv_mw = ppc["branch"][f:t, PF].real
p_lv_mw = ppc["branch"][f:t, PT].real
if ac:
q_hv_mvar = ppc["branch"][f:t, QF].real
q_lv_mvar = ppc["branch"][f:t, QT].real
pl_mw = p_hv_mw + p_lv_mw
ql_mvar = q_hv_mvar + q_lv_mvar
else:
q_hv_mvar = np.zeros_like(p_hv_mw)
q_lv_mvar = np.zeros_like(p_lv_mw)
pl_mw = np.zeros_like(p_lv_mw)
ql_mvar = np.zeros_like(p_lv_mw)
i_hv_ka = i_ft[:, 0][f:t]
i_lv_ka = i_ft[:, 1][f:t]
if trafo_loading == "current":
# calculate loading with rated current
trafo_df = net["trafo"]
vns = np.vstack([trafo_df["vn_hv_kv"].values, trafo_df["vn_lv_kv"].values]).T
lds_trafo = i_ft[f:t] * vns * np.sqrt(3) / trafo_df["sn_mva"].values[:, np.newaxis] * 100.
with np.errstate(invalid='ignore'):
ld_trafo = np.max(lds_trafo, axis=1)
elif trafo_loading == "power":
# calculate loading with rated loading
ld_trafo = np.max(s_ft[f:t] / net["trafo"]["sn_mva"].values[:, np.newaxis] * 100., axis=1)
else:
raise ValueError(
"Unknown transformer loading parameter %s - choose 'current' or 'power'" % trafo_loading)
if any(net["trafo"]["df"].values <= 0):
raise UserWarning('Transformer rating factor df must be positive. Transformers with false '
'rating factors: %s' % net["trafo"].query('df<=0').index.tolist())
loading_percent = ld_trafo / net["trafo"]["parallel"].values / net["trafo"]["df"].values
hv_buses = ppc["branch"][f:t, F_BUS].real.astype(np.int64)
lv_buses = ppc["branch"][f:t, T_BUS].real.astype(np.int64)
# write results to trafo dataframe
res_trafo_df = net["res_trafo"] if suffix is None else net["res_trafo%s" % suffix]
res_trafo_df["p_hv_mw"].values[:] = p_hv_mw
res_trafo_df["q_hv_mvar"].values[:] = q_hv_mvar
res_trafo_df["p_lv_mw"].values[:] = p_lv_mw
res_trafo_df["q_lv_mvar"].values[:] = q_lv_mvar
res_trafo_df["pl_mw"].values[:] = pl_mw
res_trafo_df["ql_mvar"].values[:] = ql_mvar
res_trafo_df["i_hv_ka"].values[:] = i_hv_ka
res_trafo_df["i_lv_ka"].values[:] = i_lv_ka
res_trafo_df["vm_hv_pu"].values[:] = ppc["bus"][hv_buses, VM]
res_trafo_df["va_hv_degree"].values[:] = ppc["bus"][hv_buses, VA]
res_trafo_df["vm_lv_pu"].values[:] = ppc["bus"][lv_buses, VM]
res_trafo_df["va_lv_degree"].values[:] = ppc["bus"][lv_buses, VA]
res_trafo_df["loading_percent"].values[:] = loading_percent
def _get_trafo_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t, V012_t):
ac = net["_options"]["ac"]
trafo_loading = net["_options"]["trafo_loading"]
if not "trafo" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["trafo"]
I012_hv_ka = I012_f[:, f:t]
I012_lv_ka = I012_t[:, f:t]
trafo_df = net["trafo"]
Vabc_hv, Vabc_lv, Iabc_hv, Iabc_lv = [sequence_to_phase(X012) for X012 in
[V012_f[:, f:t], V012_t[:, f:t], I012_f[:, f:t], I012_t[:, f:t]]]
Sabc_hv, Sabc_lv = [S_from_VI_elementwise(*Xabc_tup) / np.sqrt(3) for Xabc_tup in
[(Vabc_hv, Iabc_hv), (Vabc_lv, Iabc_lv)]]
# Todo: Check why the sqrt(3) is necessary in the previous line as opposed to _get_line_results()
Pabc_hv_mw = Sabc_hv.real
Qabc_hv_mvar = Sabc_hv.imag
Pabc_lv_mw = Sabc_lv.real
Qabc_lv_mvar = Sabc_lv.imag
if ac:
Pabcl_mw = Pabc_hv_mw + Pabc_lv_mw
Qabcl_mvar = Qabc_hv_mvar + Qabc_lv_mvar
else:
Pabcl_mw = np.zeros_like(Pabc_hv_mw)
Qabcl_mvar = np.zeros_like(Qabc_lv_mvar)
Iabc_hv_ka = np.abs(sequence_to_phase(I012_hv_ka))
Iabc_lv_ka = np.abs(sequence_to_phase(I012_lv_ka))
# current calculation for trafo lv side for vector groups with zero seq. gap (Dyn, Yzn)
# in this case, the currents of elemnts that go out from the trafo are summed and the sum applied to the trafo lv side
gap_trafo_index = np.where(I012_lv_ka[0] == 0)[0]
if len(gap_trafo_index > 0):
for i_trafo in gap_trafo_index:
Iabc_sum = [0, 0, 0]
lv_bus = net.trafo.lv_bus.iat[i_trafo]
V_bus_abc = np.array([[net.res_bus_3ph['vm_a_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]],
[net.res_bus_3ph['vm_b_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]],
[net.res_bus_3ph['vm_c_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]]])
# Branch Elements
i_branch = np.concatenate((np.where(ppc0['branch'][:, F_BUS] == lv_bus)[0],
np.where(ppc0['branch'][:, T_BUS] == lv_bus)[0]))
i_branch = np.delete(i_branch, np.where(i_branch == i_trafo + f)) # delete the trafo itself from the list
if len(i_branch > 0):
I_branch_012 = I012_f[:, i_branch]
I_branch_abc = sequence_to_phase(I_branch_012)
for x in range(len(I_branch_abc[0])):
Iabc_sum += abs(I_branch_abc[:, x])
# Loads
load_index = np.where(net.asymmetric_load['bus'] == lv_bus)[0]
if len(load_index > 0):
S_load_abc = abs(np.array([
np.array(net.res_asymmetric_load_3ph['p_a_mw'][load_index]
+ (1j * net.res_asymmetric_load_3ph['q_a_mvar'][load_index])),
np.array(net.res_asymmetric_load_3ph['p_b_mw'][load_index]
+ (1j * net.res_asymmetric_load_3ph['q_b_mvar'][load_index])),
np.array(net.res_asymmetric_load_3ph['p_c_mw'][load_index]
+ (1j * net.res_asymmetric_load_3ph['q_c_mvar'][load_index]))]))
I_load_abc = S_load_abc / (V_bus_abc / np.sqrt(3))
for x in range(len(I_load_abc[0])):
Iabc_sum += I_load_abc[:, x]
# Sgens
sgen_bus_index = np.where(net.asymmetric_sgen['bus'] == lv_bus)[0]
if len(sgen_bus_index > 0):
S_sgen_abc = abs(np.array([
np.array(net.res_asymmetric_sgen_3ph['p_a_mw'][sgen_bus_index]
+ (1j * net.res_asymmetric_sgen_3ph['q_a_mvar'][sgen_bus_index])),
np.array(net.res_asymmetric_sgen_3ph['p_b_mw'][sgen_bus_index]
+ (1j * net.res_asymmetric_sgen_3ph['q_b_mvar'][sgen_bus_index])),
np.array(net.res_asymmetric_sgen_3ph['p_c_mw'][sgen_bus_index]
+ (1j * net.res_asymmetric_sgen_3ph['q_c_mvar'][sgen_bus_index]))]))
I_sgen_abc = S_sgen_abc / (V_bus_abc / np.sqrt(3))
for x in range(len(I_sgen_abc[0])):
Iabc_sum -= I_sgen_abc[:, x]
Iabc_lv_ka[:, i_trafo] = Iabc_sum
# geting complex values of the sequence current
# Iabc_hv_ka_complex = sequence_to_phase(I012_hv_ka)
# Iabc_lv_ka_complex = sequence_to_phase(I012_lv_ka)
#
# Iabc_hv_ka = np.abs(Iabc_hv_ka_complex)
# Iabc_lv_ka = np.abs(Iabc_lv_ka_complex)
#
# In_hv_ka_complex = Iabc_hv_ka_complex.sum(axis=0)
# In_hv_ka = np.abs(In_hv_ka_complex)
# In_hv_ia_n_degree = np.angle(In_hv_ka_complex).flatten()*180/np.pi
# In_lv_ka_complex = Iabc_lv_ka_complex.sum(axis=0)
# In_lv_ka = np.abs(In_lv_ka_complex)
# In_lv_ia_n_degree = np.angle(In_lv_ka_complex).flatten()*180/np.pi
if trafo_loading == "current":
trafo_df = net["trafo"]
vns = np.vstack([trafo_df["vn_hv_kv"].values, trafo_df["vn_lv_kv"].values]).T
ld_trafo = np.maximum.reduce([np.asarray(Iabc_hv_ka) * vns[:, 0], np.asarray(Iabc_lv_ka) * vns[:, 1]])
ld_trafo = ld_trafo * np.sqrt(3) / trafo_df["sn_mva"].values * 100.
elif trafo_loading == "power":
ld_trafo = np.maximum.reduce([np.abs(Sabc_hv), np.abs(Sabc_lv)])
ld_trafo = ld_trafo / net["trafo"]["sn_mva"].values[:, np.newaxis] * 3. * 100.
else:
raise ValueError(
"Unknown transformer loading parameter %s - choose 'current' or 'power'" % trafo_loading)
if any(net["trafo"]["df"].values <= 0):
raise UserWarning('Transformer rating factor df must be positive. Transformers with false '
'rating factors: %s' % net["trafo"].query('df<=0').index.tolist())
loading_percent = ld_trafo / net["trafo"]["parallel"].values / net["trafo"]["df"].values
# write results to trafo dataframe
res_trafo_df = net["res_trafo_3ph"]
res_trafo_df["p_a_hv_mw"] = Pabc_hv_mw[0, :].flatten()
res_trafo_df["p_b_hv_mw"] = Pabc_hv_mw[1, :].flatten()
res_trafo_df["p_c_hv_mw"] = Pabc_hv_mw[2, :].flatten()
res_trafo_df["q_a_hv_mvar"] = Qabc_hv_mvar[0, :].flatten()
res_trafo_df["q_b_hv_mvar"] = Qabc_hv_mvar[1, :].flatten()
res_trafo_df["q_c_hv_mvar"] = Qabc_hv_mvar[2, :].flatten()
res_trafo_df["p_a_lv_mw"] = Pabc_lv_mw[0, :].flatten()
res_trafo_df["p_b_lv_mw"] = Pabc_lv_mw[1, :].flatten()
res_trafo_df["p_c_lv_mw"] = Pabc_lv_mw[2, :].flatten()
res_trafo_df["q_a_lv_mvar"] = Qabc_lv_mvar[0, :].flatten()
res_trafo_df["q_b_lv_mvar"] = Qabc_lv_mvar[1, :].flatten()
res_trafo_df["q_c_lv_mvar"] = Qabc_lv_mvar[2, :].flatten()
res_trafo_df["p_a_l_mw"] = Pabcl_mw[0, :].flatten()
res_trafo_df["p_b_l_mw"] = Pabcl_mw[1, :].flatten()
res_trafo_df["p_c_l_mw"] = Pabcl_mw[2, :].flatten()
res_trafo_df["q_a_l_mvar"] = Qabcl_mvar[0, :].flatten()
res_trafo_df["q_b_l_mvar"] = Qabcl_mvar[1, :].flatten()
res_trafo_df["q_c_l_mvar"] = Qabcl_mvar[2, :].flatten()
res_trafo_df["i_a_hv_ka"] = Iabc_hv_ka[0, :].flatten()
res_trafo_df["i_b_hv_ka"] = Iabc_hv_ka[1, :].flatten()
res_trafo_df["i_c_hv_ka"] = Iabc_hv_ka[2, :].flatten()
# res_trafo_df["i_n_hv_ka"] = In_hv_ka.flatten()
res_trafo_df["i_a_lv_ka"] = Iabc_lv_ka[0, :].flatten()
res_trafo_df["i_b_lv_ka"] = Iabc_lv_ka[1, :].flatten()
res_trafo_df["i_c_lv_ka"] = Iabc_lv_ka[2, :].flatten()
# res_trafo_df["i_n_lv_ka"] = In_lv_ka.flatten()
res_trafo_df["loading_a_percent"] = loading_percent[0, :]
res_trafo_df["loading_b_percent"] = loading_percent[1, :]
res_trafo_df["loading_c_percent"] = loading_percent[2, :]
res_trafo_df["loading_percent"] = loading_percent.max(axis=0)
res_trafo_df.index = net["trafo"].index.values
def _get_trafo3w_lookups(net):
f, t = net._pd2ppc_lookups["branch"]["trafo3w"]
hv = int(f + (t - f) / 3)
mv = int(f + 2 * (t - f) / 3)
lv = t
return f, hv, mv, lv
def _get_trafo3w_results(net, ppc, s_ft, i_ft, suffix=None):
if "trafo3w" not in net._pd2ppc_lookups["branch"]:
return
trafo_loading = net["_options"]["trafo_loading"]
ac = net["_options"]["ac"]
f, hv, mv, lv = _get_trafo3w_lookups(net)
phv_mw = ppc["branch"][f:hv, PF].real
pmv_mw = ppc["branch"][hv:mv, PT].real
plv_mw = ppc["branch"][mv:lv, PT].real
p_hv_mw = phv_mw
p_mv_mw = pmv_mw
p_lv_mw = plv_mw
if ac:
q_hv_mvar = ppc["branch"][f:hv, QF].real
q_mv_mvar = ppc["branch"][hv:mv, QT].real
q_lv_mvar = ppc["branch"][mv:lv, QT].real
pl_mw = phv_mw + pmv_mw + plv_mw
ql_mvar = q_hv_mvar + q_mv_mvar + q_lv_mvar
else:
zeros_ = np.zeros_like(phv_mw)
q_hv_mvar = zeros_
q_mv_mvar = zeros_
q_lv_mvar = zeros_
pl_mw = zeros_
ql_mvar = zeros_
i_h = i_ft[:, 0][f:hv]
i_m = i_ft[:, 1][hv:mv]
i_l = i_ft[:, 1][mv:lv]
t3 = net["trafo3w"]
if trafo_loading == "current":
ld_h = i_h * t3["vn_hv_kv"].values * np.sqrt(3) / t3["sn_hv_mva"].values * 100
ld_m = i_m * t3["vn_mv_kv"].values * np.sqrt(3) / t3["sn_mv_mva"].values * 100
ld_l = i_l * t3["vn_lv_kv"].values * np.sqrt(3) / t3["sn_lv_mva"].values * 100
with np.errstate(invalid='ignore'):
ld_trafo = np.max(np.vstack([ld_h, ld_m, ld_l]), axis=0)
elif trafo_loading == "power":
ld_h = s_ft[:, 0][f:hv] / t3["sn_hv_mva"].values * 100.
ld_m = s_ft[:, 1][hv:mv] / t3["sn_mv_mva"].values * 100.
ld_l = s_ft[:, 1][mv:lv] / t3["sn_lv_mva"].values * 100.
ld_trafo = np.max(np.vstack([ld_h, ld_m, ld_l]), axis=0)
else:
raise ValueError(
"Unknown transformer loading parameter %s - choose 'current' or 'power'" % trafo_loading)
loading_percent = ld_trafo
hv_buses = ppc["branch"][f:hv, F_BUS].real.astype(np.int64)
aux_buses = ppc["branch"][f:hv, T_BUS].real.astype(np.int64)
mv_buses = ppc["branch"][hv:mv, T_BUS].real.astype(np.int64)
lv_buses = ppc["branch"][mv:lv, T_BUS].real.astype(np.int64)
# write results to trafo3w dataframe
res_trafo3w_df = net["res_trafo3w"] if suffix is None else net["res_trafo3w%s" % suffix]
res_trafo3w_df["p_hv_mw"].values[:] = p_hv_mw
res_trafo3w_df["q_hv_mvar"].values[:] = q_hv_mvar
res_trafo3w_df["p_mv_mw"].values[:] = p_mv_mw
res_trafo3w_df["q_mv_mvar"].values[:] = q_mv_mvar
res_trafo3w_df["p_lv_mw"].values[:] = p_lv_mw
res_trafo3w_df["q_lv_mvar"].values[:] = q_lv_mvar
res_trafo3w_df["pl_mw"].values[:] = pl_mw
res_trafo3w_df["ql_mvar"].values[:] = ql_mvar
res_trafo3w_df["i_hv_ka"].values[:] = i_h
res_trafo3w_df["i_mv_ka"].values[:] = i_m
res_trafo3w_df["i_lv_ka"].values[:] = i_l
res_trafo3w_df["vm_hv_pu"].values[:] = ppc["bus"][hv_buses, VM]
res_trafo3w_df["va_hv_degree"].values[:] = ppc["bus"][hv_buses, VA]
res_trafo3w_df["vm_mv_pu"].values[:] = ppc["bus"][mv_buses, VM]
res_trafo3w_df["va_mv_degree"].values[:] = ppc["bus"][mv_buses, VA]
res_trafo3w_df["vm_lv_pu"].values[:] = ppc["bus"][lv_buses, VM]
res_trafo3w_df["va_lv_degree"].values[:] = ppc["bus"][lv_buses, VA]
res_trafo3w_df["va_internal_degree"].values[:] = ppc["bus"][aux_buses, VA]
res_trafo3w_df["vm_internal_pu"].values[:] = ppc["bus"][aux_buses, VM]
res_trafo3w_df["loading_percent"].values[:] = loading_percent
def _get_impedance_results(net, ppc, i_ft, suffix=None):
ac = net["_options"]["ac"]
if not "impedance" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["impedance"]
pf_mw = ppc["branch"][f:t, (PF)].real
pt_mw = ppc["branch"][f:t, (PT)].real
p_from_mw = pf_mw
p_to_mw = pt_mw
if ac:
q_from_mvar = ppc["branch"][f:t, (QF)].real
q_to_mvar = ppc["branch"][f:t, (QT)].real
ql_mvar = q_from_mvar + q_to_mvar
pl_mw = pf_mw + pt_mw
else:
zeros_ = np.zeros_like(p_from_mw)
# this looks like a pyramid
q_from_mvar = zeros_
q_to_mvar = zeros_
ql_mvar = zeros_
pl_mw = zeros_
# zeros_
i_from_ka = i_ft[f:t][:, 0]
i_to_ka = i_ft[f:t][:, 1]
# write to impedance
res_impedance_df = net["res_impedance"] if suffix is None else net["res_impedance%s" % suffix]
res_impedance_df["p_from_mw"].values[:] = p_from_mw
res_impedance_df["q_from_mvar"].values[:] = q_from_mvar
res_impedance_df["p_to_mw"].values[:] = p_to_mw
res_impedance_df["q_to_mvar"].values[:] = q_to_mvar
res_impedance_df["pl_mw"].values[:] = pl_mw
res_impedance_df["ql_mvar"].values[:] = ql_mvar
res_impedance_df["i_from_ka"].values[:] = i_from_ka
res_impedance_df["i_to_ka"].values[:] = i_to_ka
def _get_tcsc_results(net, ppc, suffix=None):
tcsc = net.get("tcsc") # todo: it used to be "if len(net.tcsc) == 0:" <- change back when pandamodels bug is fixed
if tcsc is None or len(tcsc) == 0:
return
ac = net["_options"]["ac"]
baseMVA = ppc["baseMVA"]
bus_lookup = net["_pd2ppc_lookups"]["bus"]
f = 0
t = len(net.tcsc)
f_bus = bus_lookup[net.tcsc["from_bus"].values]
t_bus = bus_lookup[net.tcsc["to_bus"].values]
baseZ = ppc["bus"][f_bus, BASE_KV] ** 2 / baseMVA
if ac:
p_from_mw = ppc["tcsc"][f:t, TCSC_PF].real
p_to_mw = ppc["tcsc"][f:t, TCSC_PT].real
q_from_mvar = ppc["tcsc"][f:t, TCSC_QF].real
q_to_mvar = ppc["tcsc"][f:t, TCSC_QT].real
i_from_ka = ppc["tcsc"][f:t, TCSC_IF].real
i_to_ka = ppc["tcsc"][f:t, TCSC_IT].real
# losses will be 0 because tcsc represents an ideal device but we can add resistance easily in the future
pl_mw = p_from_mw + p_to_mw
ql_mvar = q_from_mvar + q_to_mvar
else:
raise NotImplementedError("TCSC not implemented for algorithm != 'ac' - writing zeros to tcsc results. "
"Results for other elements are as if TCSC elements were open switches.")
zeros_ = np.zeros(t)
# this looks like a pyramid
p_from_mw = zeros_
p_to_mw = zeros_
q_from_mvar = zeros_
q_to_mvar = zeros_
ql_mvar = zeros_
pl_mw = zeros_
i_from_ka = zeros_
i_to_ka = zeros_
# zeros_
# write to impedance
# todo for suffix not None
res_tcsc_df = net["res_tcsc"] if suffix is None else net["res_tcsc%s" % suffix]
res_tcsc_df["thyristor_firing_angle_degree"].values[:] = np.rad2deg(ppc["tcsc"][f:t, TCSC_THYRISTOR_FIRING_ANGLE].real)
res_tcsc_df["x_ohm"].values[:] = ppc["tcsc"][f:t, TCSC_X_PU].real * baseZ
res_tcsc_df["p_from_mw"].values[:] = p_from_mw
res_tcsc_df["q_from_mvar"].values[:] = q_from_mvar
res_tcsc_df["p_to_mw"].values[:] = p_to_mw
res_tcsc_df["q_to_mvar"].values[:] = q_to_mvar
res_tcsc_df["i_ka"].values[:] = np.fmax(i_from_ka, i_to_ka)
# res_tcsc_df["i_from_ka"].values[:] = i_from_ka
# res_tcsc_df["i_to_ka"].values[:] = i_to_ka
res_tcsc_df["pl_mw"].values[:] = pl_mw
res_tcsc_df["ql_mvar"].values[:] = ql_mvar
res_tcsc_df["vm_from_pu"].values[:] = ppc["bus"][f_bus, VM]
res_tcsc_df["va_from_degree"].values[:] = ppc["bus"][f_bus, VA]
res_tcsc_df["vm_to_pu"].values[:] = ppc["bus"][t_bus, VM]
res_tcsc_df["va_to_degree"].values[:] = ppc["bus"][t_bus, VA]
def _get_xward_branch_results(net, ppc, bus_lookup_aranged, pq_buses, suffix=None):
ac = net["_options"]["ac"]
if not "xward" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["xward"]
p_branch_xward = ppc["branch"][f:t, PF].real
net["res_xward"]["p_mw"].values[:] = net["res_xward"]["p_mw"].values + p_branch_xward
if ac:
q_branch_xward = ppc["branch"][f:t, QF].real
net["res_xward"]["q_mvar"].values[:] = net["res_xward"]["q_mvar"].values + q_branch_xward
else:
q_branch_xward = np.zeros(len(p_branch_xward))
b_pp, p, q = _sum_by_group(net["xward"]["bus"].values, p_branch_xward, q_branch_xward)
b_ppc = bus_lookup_aranged[b_pp]
pq_buses[b_ppc, 0] += p
pq_buses[b_ppc, 1] += q
aux_buses = net["_pd2ppc_lookups"]["bus"][net["_pd2ppc_lookups"]["aux"]["xward"]]
res_xward_df = net["res_xward"] if suffix is None else net["res_xward%s" % suffix]
res_xward_df["va_internal_degree"].values[:] = ppc["bus"][aux_buses, VA]
res_xward_df["vm_internal_pu"].values[:] = ppc["bus"][aux_buses, VM]
res_xward_df.index = net["xward"].index
def _get_switch_results(net, ppc, i_ft, suffix=None):
if len(net.switch) == 0:
return
res_switch_df = "res_switch" if suffix is None else "res_switch%s" % suffix
if "switch" in net._pd2ppc_lookups["branch"]:
f, t = net._pd2ppc_lookups["branch"]["switch"]
with np.errstate(invalid='ignore'):
i_ka = np.max(i_ft[f:t], axis=1)
net[res_switch_df].loc[net._impedance_bb_switches, "i_ka"] = i_ka
p_from_mw = ppc["branch"][f:t, PF].real
q_from_mvar = ppc["branch"][f:t, QF].real
p_to_mw = ppc["branch"][f:t, PT].real
q_to_mvar = ppc["branch"][f:t, QT].real
net[res_switch_df].loc[net._impedance_bb_switches,"p_from_mw"] = p_from_mw
net[res_switch_df].loc[net._impedance_bb_switches,"q_from_mvar"] = q_from_mvar
net[res_switch_df].loc[net._impedance_bb_switches,"p_to_mw"] = p_to_mw
net[res_switch_df].loc[net._impedance_bb_switches,"q_to_mvar"] = q_to_mvar
_copy_switch_results_from_branches(net, suffix)
if "in_ka" in net.switch.columns:
net[res_switch_df]["loading_percent"] = net[res_switch_df]["i_ka"].values / net.switch["in_ka"].values * 100
def _copy_switch_results_from_branches(net, suffix=None, current_parameter="i_ka"):
res_switch_df = "res_switch" if suffix is None else "res_switch%s" % suffix
switch_lines = net.switch.element[net.switch.et=="l"]
if len(switch_lines) > 0:
res_line_df = "res_line" if suffix is None else "res_line%s" % suffix
net[res_switch_df].loc[switch_lines.index, current_parameter] = net[res_line_df].loc[switch_lines.values, current_parameter].values
switch_trafo = net.switch[net.switch.et.values=="t"]
if len(switch_trafo) > 0:
res_trafo_df = "res_trafo" if suffix is None else "res_trafo%s" % suffix
for side in ["hv", "lv"]:
buses = net.trafo["{}_bus".format(side)].loc[switch_trafo.element.values].values
side_switch_trafo = switch_trafo[switch_trafo.bus.values==buses]
switches = side_switch_trafo.index
trafos = side_switch_trafo.element.values
current, unit = current_parameter.split("_")
side_current = "{}_{}_{}".format(current, side, unit)
net[res_switch_df].loc[switches, current_parameter] = net[res_trafo_df].loc[trafos, side_current].values
open_switches = ~net.switch.closed.values
if any(open_switches):
net[res_switch_df].loc[open_switches, current_parameter] = 0