From 25e4f029fb5ce9d66b5676d587e8bb8f46c853db Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 12:50:19 -0800 Subject: [PATCH 01/10] add opendss (from PingThingsIO/kinesis) --- .../Models/13Bus/IEEE13Nodeckt.dss | 177 ++++++ .../Models/13Bus/IEEELineCodes.DSS | 213 +++++++ btrdbextras/opendss_ingest/README.md | 10 + btrdbextras/opendss_ingest/__init__.py | 0 .../opendss_ingest/opendss_ingestor.py | 161 ++++++ btrdbextras/opendss_ingest/requirements.txt | 1 + .../opendss_ingest/simulation_utils.py | 540 ++++++++++++++++++ 7 files changed, 1102 insertions(+) create mode 100644 btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss create mode 100644 btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS create mode 100644 btrdbextras/opendss_ingest/README.md create mode 100644 btrdbextras/opendss_ingest/__init__.py create mode 100644 btrdbextras/opendss_ingest/opendss_ingestor.py create mode 100644 btrdbextras/opendss_ingest/requirements.txt create mode 100644 btrdbextras/opendss_ingest/simulation_utils.py diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss new file mode 100644 index 0000000..6be3991 --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Nodeckt.dss @@ -0,0 +1,177 @@ +Clear +Set DefaultBaseFrequency=60 + +! +! This script is based on a script developed by Tennessee Tech Univ students +! Tyler Patton, Jon Wood, and David Woods, April 2009 +! + +new circuit.IEEE13Nodeckt +~ basekv=115 pu=1.0001 phases=3 bus1=SourceBus +~ Angle=30 ! advance angle 30 deg so result agree with published angle +~ MVAsc3=20000 MVASC1=21000 ! stiffen the source to approximate inf source + + + +!SUB TRANSFORMER DEFINITION +! Although this data was given, it does not appear to be used in the test case results +! The published test case starts at 1.0 per unit at Bus 650. To make this happen, we will change the impedance +! on the transformer to something tiny by dividing by 1000 using the DSS in-line RPN math +New Transformer.Sub Phases=3 Windings=2 XHL=(8 1000 /) +~ wdg=1 bus=SourceBus conn=delta kv=115 kva=5000 %r=(.5 1000 /) +~ wdg=2 bus=650 conn=wye kv=4.16 kva=5000 %r=(.5 1000 /) + +! FEEDER 1-PHASE VOLTAGE REGULATORS +! Define low-impedance 2-wdg transformer + +New Transformer.Reg1 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.1 RG60.1] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg1 transformer=Reg1 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + +New Transformer.Reg2 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.2 RG60.2] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg2 transformer=Reg2 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + +New Transformer.Reg3 phases=1 bank=reg1 XHL=0.01 kVAs=[1666 1666] +~ Buses=[650.3 RG60.3] kVs=[2.4 2.4] %LoadLoss=0.01 +new regcontrol.Reg3 transformer=Reg3 winding=2 vreg=122 band=2 ptratio=20 ctprim=700 R=3 X=9 + + +!TRANSFORMER DEFINITION +New Transformer.XFM1 Phases=3 Windings=2 XHL=2 +~ wdg=1 bus=633 conn=Wye kv=4.16 kva=500 %r=.55 XHT=1 +~ wdg=2 bus=634 conn=Wye kv=0.480 kva=500 %r=.55 XLT=1 + + +!LINE CODES +redirect IEEELineCodes.dss + +// these are local matrix line codes +// corrected 9-14-2011 +New linecode.mtx601 nphases=3 BaseFreq=60 +~ rmatrix = (0.3465 | 0.1560 0.3375 | 0.1580 0.1535 0.3414 ) +~ xmatrix = (1.0179 | 0.5017 1.0478 | 0.4236 0.3849 1.0348 ) +~ units=mi +New linecode.mtx602 nphases=3 BaseFreq=60 +~ rmatrix = (0.7526 | 0.1580 0.7475 | 0.1560 0.1535 0.7436 ) +~ xmatrix = (1.1814 | 0.4236 1.1983 | 0.5017 0.3849 1.2112 ) +~ units=mi +New linecode.mtx603 nphases=2 BaseFreq=60 +~ rmatrix = (1.3238 | 0.2066 1.3294 ) +~ xmatrix = (1.3569 | 0.4591 1.3471 ) +~ units=mi +New linecode.mtx604 nphases=2 BaseFreq=60 +~ rmatrix = (1.3238 | 0.2066 1.3294 ) +~ xmatrix = (1.3569 | 0.4591 1.3471 ) +~ units=mi +New linecode.mtx605 nphases=1 BaseFreq=60 +~ rmatrix = (1.3292 ) +~ xmatrix = (1.3475 ) +~ units=mi + +/*********** Original 606 Linecode ********************* + +You have to use this to match Kersting's results: + +New linecode.mtx606 nphases=3 BaseFreq=60 +~ rmatrix = (0.7982 | 0.3192 0.7891 | 0.2849 0.3192 0.7982 ) +~ xmatrix = (0.4463 | 0.0328 0.4041 | -0.0143 0.0328 0.4463 ) +~ Cmatrix = [257 | 0 257 | 0 0 257] ! <--- This is too low by 1.5 +~ units=mi + +Corrected mtx606 Feb 3 2016 by RDugan + +The new LineCode.606 is computed using the following CN cable definition and +LineGeometry definition: + +New CNDATA.250_1/3 k=13 DiaStrand=0.064 Rstrand=2.816666667 epsR=2.3 +~ InsLayer=0.220 DiaIns=1.06 DiaCable=1.16 Rac=0.076705 GMRac=0.20568 diam=0.573 +~ Runits=kft Radunits=in GMRunits=in + +New LineGeometry.606 nconds=3 nphases=3 units=ft +~ cond=1 cncable=250_1/3 x=-0.5 h= -4 +~ cond=2 cncable=250_1/3 x=0 h= -4 +~ cond=3 cncable=250_1/3 x=0.5 h= -4 + +****End Comment******/ + +New Linecode.mtx606 nphases=3 Units=mi +~ Rmatrix=[0.791721 |0.318476 0.781649 |0.28345 0.318476 0.791721 ] +~ Xmatrix=[0.438352 |0.0276838 0.396697 |-0.0184204 0.0276838 0.438352 ] +~ Cmatrix=[383.948 |0 383.948 |0 0 383.948 ] +New linecode.mtx607 nphases=1 BaseFreq=60 +~ rmatrix = (1.3425 ) +~ xmatrix = (0.5124 ) +~ cmatrix = [236] +~ units=mi + + +!LOAD DEFINITIONS +New Load.671 Bus1=671.1.2.3 Phases=3 Conn=Delta Model=1 kV=4.16 kW=1155 kvar=660 +New Load.634a Bus1=634.1 Phases=1 Conn=Wye Model=1 kV=0.277 kW=160 kvar=110 +New Load.634b Bus1=634.2 Phases=1 Conn=Wye Model=1 kV=0.277 kW=120 kvar=90 +New Load.634c Bus1=634.3 Phases=1 Conn=Wye Model=1 kV=0.277 kW=120 kvar=90 +New Load.645 Bus1=645.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=170 kvar=125 +New Load.646 Bus1=646.2.3 Phases=1 Conn=Delta Model=2 kV=4.16 kW=230 kvar=132 +New Load.692 Bus1=692.3.1 Phases=1 Conn=Delta Model=5 kV=4.16 kW=170 kvar=151 +New Load.675a Bus1=675.1 Phases=1 Conn=Wye Model=1 kV=2.4 kW=485 kvar=190 +New Load.675b Bus1=675.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=68 kvar=60 +New Load.675c Bus1=675.3 Phases=1 Conn=Wye Model=1 kV=2.4 kW=290 kvar=212 +New Load.611 Bus1=611.3 Phases=1 Conn=Wye Model=5 kV=2.4 kW=170 kvar=80 +New Load.652 Bus1=652.1 Phases=1 Conn=Wye Model=2 kV=2.4 kW=128 kvar=86 +New Load.670a Bus1=670.1 Phases=1 Conn=Wye Model=1 kV=2.4 kW=17 kvar=10 +New Load.670b Bus1=670.2 Phases=1 Conn=Wye Model=1 kV=2.4 kW=66 kvar=38 +New Load.670c Bus1=670.3 Phases=1 Conn=Wye Model=1 kV=2.4 kW=117 kvar=68 + +!CAPACITOR DEFINITIONS +New Capacitor.Cap1 Bus1=675 phases=3 kVAR=600 kV=4.16 +New Capacitor.Cap2 Bus1=611.3 phases=1 kVAR=100 kV=2.4 + +!Bus 670 is the concentrated point load of the distributed load on line 632 to 671 located at 1/3 the distance from node 632 + +!LINE DEFINITIONS +New Line.650632 Phases=3 Bus1=RG60.1.2.3 Bus2=632.1.2.3 LineCode=mtx601 Length=2000 units=ft +New Line.632670 Phases=3 Bus1=632.1.2.3 Bus2=670.1.2.3 LineCode=mtx601 Length=667 units=ft +New Line.670671 Phases=3 Bus1=670.1.2.3 Bus2=671.1.2.3 LineCode=mtx601 Length=1333 units=ft +New Line.671680 Phases=3 Bus1=671.1.2.3 Bus2=680.1.2.3 LineCode=mtx601 Length=1000 units=ft +New Line.632633 Phases=3 Bus1=632.1.2.3 Bus2=633.1.2.3 LineCode=mtx602 Length=500 units=ft +New Line.632645 Phases=2 Bus1=632.3.2 Bus2=645.3.2 LineCode=mtx603 Length=500 units=ft +New Line.645646 Phases=2 Bus1=645.3.2 Bus2=646.3.2 LineCode=mtx603 Length=300 units=ft +New Line.692675 Phases=3 Bus1=692.1.2.3 Bus2=675.1.2.3 LineCode=mtx606 Length=500 units=ft +New Line.671684 Phases=2 Bus1=671.1.3 Bus2=684.1.3 LineCode=mtx604 Length=300 units=ft +New Line.684611 Phases=1 Bus1=684.3 Bus2=611.3 LineCode=mtx605 Length=300 units=ft +New Line.684652 Phases=1 Bus1=684.1 Bus2=652.1 LineCode=mtx607 Length=800 units=ft + + +!SWITCH DEFINITIONS +New Line.671692 Phases=3 Bus1=671 Bus2=692 Switch=y r1=1e-4 r0=1e-4 x1=0.000 x0=0.000 c1=0.000 c0=0.000 + +Set Voltagebases=[115, 4.16, .48] +calcv +Solve +BusCoords IEEE13Node_BusXY.csv + +!--------------------------------------------------------------------------------------------------------------------------------------------------- +!----------------Show some Results ----------------------------------------------------------------------------------------------------------------- +!--------------------------------------------------------------------------------------------------------------------------------------------------- + + +// Show Voltages LN Nodes +// Show Currents Elem +// Show Powers kVA Elem +// Show Losses +// Show Taps + +!--------------------------------------------------------------------------------------------------------------------------------------------------- +!--------------------------------------------------------------------------------------------------------------------------------------------------- +! Alternate Solution Script +! To force the taps to be same as published results, set the transformer taps manually and disable the controls +!--------------------------------------------------------------------------------------------------------------------------------------------------- +/* +Transformer.Reg1.Taps=[1.0 1.0625] +Transformer.Reg2.Taps=[1.0 1.0500] +Transformer.Reg3.Taps=[1.0 1.06875] +Set Controlmode=OFF + +Solve +*/ diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS b/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS new file mode 100644 index 0000000..1ca26b6 --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS @@ -0,0 +1,213 @@ +! this file was corrected 9/16/2010 to match the values in Kersting's files + + + +! These line codes are used in the 123-bus circuit + +New linecode.1 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0312137 0.0901946 | 0.0306264 0.0316143 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0935314 0.200783 | 0.0760312 0.0855879 0.204877 ) +!!!~ cmatrix = (2.90301 | -0.679335 3.15896 | -0.22313 -0.481416 2.8965 ) +~ rmatrix = [0.086666667 | 0.029545455 0.088371212 | 0.02907197 0.029924242 0.087405303] +~ xmatrix = [0.204166667 | 0.095018939 0.198522727 | 0.072897727 0.080227273 0.201723485] +~ cmatrix = [2.851710072 | -0.920293787 3.004631862 | -0.350755566 -0.585011253 2.71134756] + +New linecode.2 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0901946 | 0.0316143 0.0889665 | 0.0312137 0.0306264 0.088205 ) +!!!~ xmatrix = (0.200783 | 0.0855879 0.204877 | 0.0935314 0.0760312 0.20744 ) +!!!~ cmatrix = (3.15896 | -0.481416 2.8965 | -0.679335 -0.22313 2.90301 ) +~ rmatrix = [0.088371212 | 0.02992424 0.087405303 | 0.029545455 0.02907197 0.086666667] +~ xmatrix = [0.198522727 | 0.080227273 0.201723485 | 0.095018939 0.072897727 0.204166667] +~ cmatrix = [3.004631862 | -0.585011253 2.71134756 | -0.920293787 -0.350755566 2.851710072] + +New linecode.3 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0889665 | 0.0306264 0.088205 | 0.0316143 0.0312137 0.0901946 ) +!!!~ xmatrix = (0.204877 | 0.0760312 0.20744 | 0.0855879 0.0935314 0.200783 ) +!!!~ cmatrix = (2.8965 | -0.22313 2.90301 | -0.481416 -0.679335 3.15896 ) + +~ rmatrix = [0.087405303 | 0.02907197 0.086666667 | 0.029924242 0.029545455 0.088371212] +~ xmatrix = [0.201723485 | 0.072897727 0.204166667 | 0.080227273 0.095018939 0.198522727] +~ cmatrix = [2.71134756 | -0.350755566 2.851710072 | -0.585011253 -0.920293787 3.004631862] + +New linecode.4 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0889665 | 0.0316143 0.0901946 | 0.0306264 0.0312137 0.088205 ) +!!!~ xmatrix = (0.204877 | 0.0855879 0.200783 | 0.0760312 0.0935314 0.20744 ) +!!!~ cmatrix = (2.8965 | -0.481416 3.15896 | -0.22313 -0.679335 2.90301 ) +~ rmatrix = [0.087405303 | 0.029924242 0.088371212 | 0.02907197 0.029545455 0.086666667] +~ xmatrix = [0.201723485 | 0.080227273 0.198522727 | 0.072897727 0.095018939 0.204166667] +~ cmatrix = [2.71134756 | 0.585011253 3.004631862 | -0.350755566 -0.920293787 2.851710072] + +New linecode.5 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0901946 | 0.0312137 0.088205 | 0.0316143 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.200783 | 0.0935314 0.20744 | 0.0855879 0.0760312 0.204877 ) +!!!~ cmatrix = (3.15896 | -0.679335 2.90301 | -0.481416 -0.22313 2.8965 ) + +~ rmatrix = [0.088371212 | 0.029545455 0.086666667 | 0.029924242 0.02907197 0.087405303] +~ xmatrix = [0.198522727 | 0.095018939 0.204166667 | 0.080227273 0.072897727 0.201723485] +~ cmatrix = [3.004631862 | -0.920293787 2.851710072 | -0.585011253 -0.350755566 2.71134756] + +New linecode.6 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 | 0.0312137 0.0316143 0.0901946 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 | 0.0935314 0.0855879 0.200783 ) +!!!~ cmatrix = (2.90301 | -0.22313 2.8965 | -0.679335 -0.481416 3.15896 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303 | 0.029545455 0.029924242 0.088371212] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485 | 0.095018939 0.080227273 0.198522727] +~ cmatrix = [2.851710072 | -0.350755566 2.71134756 | -0.920293787 -0.585011253 3.004631862] +New linecode.7 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 ) +!!!~ cmatrix = (2.75692 | -0.326659 2.82313 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485] +~ cmatrix = [2.569829596 | -0.52995137 2.597460011] +New linecode.8 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.088205 | 0.0306264 0.0889665 ) +!!!~ xmatrix = (0.20744 | 0.0760312 0.204877 ) +!!!~ cmatrix = (2.75692 | -0.326659 2.82313 ) +~ rmatrix = [0.086666667 | 0.02907197 0.087405303] +~ xmatrix = [0.204166667 | 0.072897727 0.201723485] +~ cmatrix = [2.569829596 | -0.52995137 2.597460011] +New linecode.9 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.10 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.11 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.12 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.291814 | 0.101656 0.294012 | 0.096494 0.101656 0.291814 ) +!!!~ xmatrix = (0.141848 | 0.0517936 0.13483 | 0.0401881 0.0517936 0.141848 ) +!!!~ cmatrix = (53.4924 | 0 53.4924 | 0 0 53.4924 ) +~ rmatrix = [0.288049242 | 0.09844697 0.29032197 | 0.093257576 0.09844697 0.288049242] +~ xmatrix = [0.142443182 | 0.052556818 0.135643939 | 0.040852273 0.052556818 0.142443182] +~ cmatrix = [33.77150149 | 0 33.77150149 | 0 0 33.77150149] + +! These line codes are used in the 34-node test feeder + +New linecode.300 nphases=3 basefreq=60 ! ohms per 1000ft Corrected 11/30/05 +~ rmatrix = [0.253181818 | 0.039791667 0.250719697 | 0.040340909 0.039128788 0.251780303] !ABC ORDER +~ xmatrix = [0.252708333 | 0.109450758 0.256988636 | 0.094981061 0.086950758 0.255132576] +~ CMATRIX = [2.680150309 | -0.769281006 2.5610381 | -0.499507676 -0.312072984 2.455590387] +New linecode.301 nphases=3 basefreq=60 +~ rmatrix = [0.365530303 | 0.04407197 0.36282197 | 0.04467803 0.043333333 0.363996212] +~ xmatrix = [0.267329545 | 0.122007576 0.270473485 | 0.107784091 0.099204545 0.269109848] +~ cmatrix = [2.572492163 | -0.72160598 2.464381882 | -0.472329395 -0.298961096 2.368881119] +New linecode.302 nphases=1 basefreq=60 +~ rmatrix = (0.530208 ) +~ xmatrix = (0.281345 ) +~ cmatrix = (2.12257 ) +New linecode.303 nphases=1 basefreq=60 +~ rmatrix = (0.530208 ) +~ xmatrix = (0.281345 ) +~ cmatrix = (2.12257 ) +New linecode.304 nphases=1 basefreq=60 +~ rmatrix = (0.363958 ) +~ xmatrix = (0.269167 ) +~ cmatrix = (2.1922 ) + + +! This may be for the 4-node test feeder, but is not actually referenced. +! instead, the 4Bus*.dss files all use the wiredata and linegeometry inputs +! to calculate these matrices from physical data. + +New linecode.400 nphases=3 BaseFreq=60 +~ rmatrix = (0.088205 | 0.0312137 0.0901946 | 0.0306264 0.0316143 0.0889665 ) +~ xmatrix = (0.20744 | 0.0935314 0.200783 | 0.0760312 0.0855879 0.204877 ) +~ cmatrix = (2.90301 | -0.679335 3.15896 | -0.22313 -0.481416 2.8965 ) + +! These are for the 13-node test feeder + +New linecode.601 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0674673 | 0.0312137 0.0654777 | 0.0316143 0.0306264 0.0662392 ) +!!!~ xmatrix = (0.195204 | 0.0935314 0.201861 | 0.0855879 0.0760312 0.199298 ) +!!!~ cmatrix = (3.32591 | -0.743055 3.04217 | -0.525237 -0.238111 3.03116 ) +~ rmatrix = [0.065625 | 0.029545455 0.063920455 | 0.029924242 0.02907197 0.064659091] +~ xmatrix = [0.192784091 | 0.095018939 0.19844697 | 0.080227273 0.072897727 0.195984848] +~ cmatrix = [3.164838036 | -1.002632425 2.993981593 | -0.632736516 -0.372608713 2.832670203] +New linecode.602 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.144361 | 0.0316143 0.143133 | 0.0312137 0.0306264 0.142372 ) +!!!~ xmatrix = (0.226028 | 0.0855879 0.230122 | 0.0935314 0.0760312 0.232686 ) +!!!~ cmatrix = (3.01091 | -0.443561 2.77543 | -0.624494 -0.209615 2.77847 ) +~ rmatrix = [0.142537879 | 0.029924242 0.14157197 | 0.029545455 0.02907197 0.140833333] +~ xmatrix = [0.22375 | 0.080227273 0.226950758 | 0.095018939 0.072897727 0.229393939] +~ cmatrix = [2.863013423 | -0.543414918 2.602031589 | -0.8492585 -0.330962141 2.725162768] +New linecode.603 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.254472 | 0.0417943 0.253371 ) +!!!~ xmatrix = (0.259467 | 0.0912376 0.261431 ) +!!!~ cmatrix = (2.54676 | -0.28882 2.49502 ) +~ rmatrix = [0.251780303 | 0.039128788 0.250719697] +~ xmatrix = [0.255132576 | 0.086950758 0.256988636] +~ cmatrix = [2.366017603 | -0.452083836 2.343963508] +New linecode.604 nphases=2 BaseFreq=60 +!!!~ rmatrix = (0.253371 | 0.0417943 0.254472 ) +!!!~ xmatrix = (0.261431 | 0.0912376 0.259467 ) +!!!~ cmatrix = (2.49502 | -0.28882 2.54676 ) +~ rmatrix = [0.250719697 | 0.039128788 0.251780303] +~ xmatrix = [0.256988636 | 0.086950758 0.255132576] +~ cmatrix = [2.343963508 | -0.452083836 2.366017603] +New linecode.605 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.254428 ) +!!!~ xmatrix = (0.259546 ) +!!!~ cmatrix = (2.50575 ) +~ rmatrix = [0.251742424] +~ xmatrix = [0.255208333] +~ cmatrix = [2.270366128] +New linecode.606 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.152193 | 0.0611362 0.15035 | 0.0546992 0.0611362 0.152193 ) +!!!~ xmatrix = (0.0825685 | 0.00548281 0.0745027 | -0.00339824 0.00548281 0.0825685 ) +!!!~ cmatrix = (72.7203 | 0 72.7203 | 0 0 72.7203 ) +~ rmatrix = [0.151174242 | 0.060454545 0.149450758 | 0.053958333 0.060454545 0.151174242] +~ xmatrix = [0.084526515 | 0.006212121 0.076534091 | -0.002708333 0.006212121 0.084526515] +~ cmatrix = [48.67459408 | 0 48.67459408 | 0 0 48.67459408] +New linecode.607 nphases=1 BaseFreq=60 +!!!~ rmatrix = (0.255799 ) +!!!~ xmatrix = (0.092284 ) +!!!~ cmatrix = (50.7067 ) +~ rmatrix = [0.254261364] +~ xmatrix = [0.097045455] +~ cmatrix = [44.70661522] + +! These are for the 37-node test feeder, all underground + +New linecode.721 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0554906 | 0.0127467 0.0501597 | 0.00640446 0.0127467 0.0554906 ) +!!!~ xmatrix = (0.0372331 | -0.00704588 0.0358645 | -0.00796424 -0.00704588 0.0372331 ) +!!!~ cmatrix = (124.851 | 0 124.851 | 0 0 124.851 ) +~ rmatrix = [0.055416667 | 0.012746212 0.050113636 | 0.006382576 0.012746212 0.055416667] +~ xmatrix = [0.037367424 | -0.006969697 0.035984848 | -0.007897727 -0.006969697 0.037367424] +~ cmatrix = [80.27484728 | 0 80.27484728 | 0 0 80.27484728] +New linecode.722 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.0902251 | 0.0309584 0.0851482 | 0.0234946 0.0309584 0.0902251 ) +!!!~ xmatrix = (0.055991 | -0.00646552 0.0504025 | -0.0117669 -0.00646552 0.055991 ) +!!!~ cmatrix = (93.4896 | 0 93.4896 | 0 0 93.4896 ) +~ rmatrix = [0.089981061 | 0.030852273 0.085 | 0.023371212 0.030852273 0.089981061] +~ xmatrix = [0.056306818 | -0.006174242 0.050719697 | -0.011496212 -0.006174242 0.056306818] +~ cmatrix = [64.2184109 | 0 64.2184109 | 0 0 64.2184109] +New linecode.723 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.247572 | 0.0947678 0.249104 | 0.0893782 0.0947678 0.247572 ) +!!!~ xmatrix = (0.126339 | 0.0390337 0.118816 | 0.0279344 0.0390337 0.126339 ) +!!!~ cmatrix = (58.108 | 0 58.108 | 0 0 58.108 ) +~ rmatrix = [0.245 | 0.092253788 0.246628788 | 0.086837121 0.092253788 0.245] +~ xmatrix = [0.127140152 | 0.039981061 0.119810606 | 0.028806818 0.039981061 0.127140152] +~ cmatrix = [37.5977112 | 0 37.5977112 | 0 0 37.5977112] +New linecode.724 nphases=3 BaseFreq=60 +!!!~ rmatrix = (0.399883 | 0.101765 0.402011 | 0.0965199 0.101765 0.399883 ) +!!!~ xmatrix = (0.146325 | 0.0510963 0.139305 | 0.0395402 0.0510963 0.146325 ) +!!!~ cmatrix = (46.9685 | 0 46.9685 | 0 0 46.9685 ) +~ rmatrix = [0.396818182 | 0.098560606 0.399015152 | 0.093295455 0.098560606 0.396818182] +~ xmatrix = [0.146931818 | 0.051856061 0.140113636 | 0.040208333 0.051856061 0.146931818] +~ cmatrix = [30.26701029 | 0 30.26701029 | 0 0 30.26701029] diff --git a/btrdbextras/opendss_ingest/README.md b/btrdbextras/opendss_ingest/README.md new file mode 100644 index 0000000..ace6d4b --- /dev/null +++ b/btrdbextras/opendss_ingest/README.md @@ -0,0 +1,10 @@ +# OpenDSS Simulation + +## What is OpenDSS? +[OpenDSS](https://www.epri.com/pages/sa/opendss) is an open source tool for simulating electrical distribution networks. For us, it may be useful for generating realistic grid data from particular physical contexts---for example, measurements on two ends of a line, or two ends of a transformer---without any data privacy concerns. In this sense, it is especially useful for the Dominion Apps project, for which we would otherwise need to prototype and test on Dominion's PMU data, access to which is restricted. + +## This Repo +This repo contains notebooks demonstrating how to run simulations (ie powerflow solutions) and retrieve data via OpenDSS's Python API, called [`OpenDSSDirect`](https://dss-extensions.org/OpenDSSDirect.py/index.html). The notebooks work with IEEE network models that come prepackaged with OpenDSS and have also been committed to this repo under the `Models` folder. + +### Getting Started +To get started, install OpenDSS on your local machine from [here](https://sourceforge.net/projects/electricdss/files/). Next, install `OpenDSSDirect` following the instructions [here](https://dss-extensions.org/OpenDSSDirect.py/notebooks/Installation.html). If installation has been successful, you should be able to run the notebooks in this repo. It is recommended you begin with `Intro to Simulation with OpenDSS` which will introduce the main steps in any simulation, and demonstrate how to obtain the resulting data. diff --git a/btrdbextras/opendss_ingest/__init__.py b/btrdbextras/opendss_ingest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/btrdbextras/opendss_ingest/opendss_ingestor.py b/btrdbextras/opendss_ingest/opendss_ingestor.py new file mode 100644 index 0000000..aa2f610 --- /dev/null +++ b/btrdbextras/opendss_ingest/opendss_ingestor.py @@ -0,0 +1,161 @@ +#%% +import numpy as np +import pandas as pd +import opendssdirect as dss +import matplotlib.pyplot as plt +from tqdm.notebook import tqdm_notebook, tqdm +import btrdb as db +import uuid +from btrdb.utils.timez import datetime_to_ns +import simulation_utils as sims +from datetime import datetime, timedelta + +%matplotlib inline + +import importlib + +importlib.reload(sims); +#%% +# Connect to the database +conn = db.connect(profile="collab") +#%% +model_loc = "./Models/13Bus/IEEE13Nodeckt.dss" +dss.run_command("Redirect " + model_loc); +#%% md +# # Create output streams +# The following cells create the output streams or retrieve them if they have already been created. +#%% +prefix = "simulated/ieee13" +collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) +#%% +nstreams = len(collections) +print("Creating", nstreams, "streams") +for i in range(nstreams): + print(collections[i] + "/" + names[i]) +#%% +streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) +#%% md +# ## Generate simulated measurements +# The following cell generates data that will be converted into streams. +# For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. +#%% +# The number of samples to generate +start_time = datetime(2022, 1, 1, 0, 0, 0) +end_time = datetime(2022, 1, 1, 0, 1, 0) +fs = 30 # Hz +T = int(((end_time - start_time).total_seconds()) * fs) +print("We will generate", T, "samples.") + +# Generate the nanosecond timestamps for the data +start_ns = datetime_to_ns(start_time) +end_ns = datetime_to_ns(end_time) +timestamps = np.arange(start_ns, end_ns, 1e9 / 30, dtype="int") +#%% +# Get the original loads +load, load_names = sims.get_loads() +nloads = len(load_names) + +# Generate the randomized scaling factors +mu = 1.1 +sig = 0.1 +s = np.random.normal(loc=mu, scale=sig, size=[nloads, T]) + +# Generate the new load values +new_load = s * load[:, np.newaxis] +#%% +V, I = sims.simulate_network(new_load, load_names) +#%% +plt.plot(timestamps, V["646/VCM"]) +plt.plot(timestamps, V["646/VBM"]) + +plt.figure() +plt.plot(timestamps, V["646/VCA"]) +plt.plot(timestamps, V["646/VBA"]) +#%% +print("Number of streams simulated:") +print(len(V.keys()) + len(I.keys())) +#%% md +# # Push data to streams +#%% +# Put voltage data into the corresponding stream +sims.add_all_data(timestamps, V, streams_dict, prefix) +# Put current data into the corresponding stream +sims.add_all_data(timestamps, I, streams_dict, prefix) +#%% md +# # Add long period of data +# The following cell runs a loop which will generate and push data over a much longer period. +# The purpose of the loop is to avoid generating all the data at once - instead, the full time period is divided into smaller time chunks, with data generated and inserted for each chunk sequentially. +# +# The code is divided into two cells below - the first is the initialization step. The second runs the loop. If there is an error during the loop, the second cell can be re-run and will pick up where it left off. +#%% +# Collection prefix in which data will be added +prefix = "simulated/ieee13" +# Get the desired output streams to which data will be pushed +collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) +# If the desired streams exist, retrieve them. Otherwise create them. +streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) + + +# Get the original loads +load, load_names = sims.get_loads() +nloads = len(load_names) + +# Simulation time window - the FULL time range over which we want to generate data +start_time = datetime(2022, 1, 2, 0, 0, 0) +end_time = datetime(2022, 1, 3, 0, 0, 0) +fs = 30 # Hz +Ttotal = int(((end_time - start_time).total_seconds()) * fs) +print("We will generate", Ttotal, "samples.") + +# The simulation time step - this is the amount of data we insert at once. +step = timedelta(minutes=5) +nsteps = int((end_time - start_time) / step) + +# Create progress bar +pbar = tqdm(total=nsteps, desc="Adding simulated data") + +t0 = start_time +#%% +remaining_steps = int((end_time - t0) / step) +pbar = tqdm(total=remaining_steps, desc="Adding simulated data") +while t0 < end_time: + # Generate the nanosecond timestamps for the data + t0_ns = datetime_to_ns(t0) + t1_ns = datetime_to_ns(t0 + step) + timestamps = np.arange(t0_ns, t1_ns, 1e9 / fs, dtype="int") + + # The number of samples to be generated in this iteration + T = len(timestamps) + # Generate the randomized scaling factors + mu = 1.1 + sig = 0.1 + s = np.random.normal(loc=mu, scale=sig, size=[nloads, T]) + # Generate the new load values + new_load = s * load[:, np.newaxis] + # Simulate + V, I = sims.simulate_network(new_load, load_names) + + # Push data to database + # Put voltage data into the corresponding stream + sims.add_all_data(timestamps, V, streams_dict, prefix) + # Put current data into the corresponding stream + sims.add_all_data(timestamps, I, streams_dict, prefix) + + # Increment time + t0 = t0 + step + pbar.update(1) +pbar.close() +#%% md +# # DELETING streams +# **DO NOT RUN** unless you are certain you want to delete. +#%% +prefix = "simulated/ieee13" +# Get the desired output streams to which data will be pushed +collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) +# If the desired streams exist, retrieve them. Otherwise create them. +streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) + +for key, val in streams_dict.items(): + print("Deleting", key) + ### val.obliterate() ## UNCOMMENT IF YOU WANT TO DELETE +#%% diff --git a/btrdbextras/opendss_ingest/requirements.txt b/btrdbextras/opendss_ingest/requirements.txt new file mode 100644 index 0000000..feb7b38 --- /dev/null +++ b/btrdbextras/opendss_ingest/requirements.txt @@ -0,0 +1 @@ +OpenDSSDirect.py[extras] diff --git a/btrdbextras/opendss_ingest/simulation_utils.py b/btrdbextras/opendss_ingest/simulation_utils.py new file mode 100644 index 0000000..236963b --- /dev/null +++ b/btrdbextras/opendss_ingest/simulation_utils.py @@ -0,0 +1,540 @@ +import uuid + +import numpy as np +import opendssdirect as dss +import pandas as pd +from tqdm.notebook import tqdm, tqdm_notebook + +phase_letters = ["A", "B", "C"] + + +def v2dict(bus_names): + # Returns the voltage data on each phase of the + # buses in bus_names + # + # Inputs + # ------ + # bus_names : list of strings + # Buses for which to return voltage data + # + # Returns + # ------- + # V : dictionary of real values + # The keys are the stream collection/name for the data. + # The collection / stream name encodes the bus, phase, and quantity + # which are formatted by the method get_voltage_stream_colname + + # Instantiate the dict of results + V = {} + + # Iterate through the buses + for bus in bus_names: + # Set the current bus to be "active" + dss.Circuit.SetActiveBus(bus) + + # Get the phases at this bus + phases = dss.Bus.Nodes() + nphases = len(phases) + + # Get all voltages at this bus + # This is a real array of size nphases * 2 - + # each pair is the re & imag part of the voltage + busvolt = dss.Bus.Voltages() + + # Get the voltage at each phase + for pidx in range(nphases): + # We don't want to save any phase 0 data + if phases[pidx] == 0: + continue + + voltage = busvolt[pidx * 2] + 1j * busvolt[pidx * 2 + 1] + + # Save the magnitude data + col, name = get_voltage_stream_colname( + bus, phase_letters[phases[pidx] - 1], True + ) + V[col + "/" + name] = np.abs(voltage) + + # Save the angle data + col, name = get_voltage_stream_colname( + bus, phase_letters[phases[pidx] - 1], False + ) + V[col + "/" + name] = np.angle(voltage, deg=True) + + return V + + +def i2dict(con_names): + # Returns the complex currents on each phase at each end of each connector + # The order of results matches the input names. + # + # Inputs + # ------ + # con_names : list of strings + # Connectors for which to return current data + # + # Returns + # ------- + # I - dictionary of real values + # The keys are the stream collection/name for the data. + # The collection / stream name encodes the connector, end, phase, and quantity + # which are formatted by the method get_lineflow_stream_colname + + ncons = len(con_names) + # Get the ends of each connector + con_ends = get_conn_ends(con_names) + + # Instantiate the dict of results + I = {} + + for cidx in range(ncons): + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con_names[cidx]) + + # Get the phases on the connector + # this is the phases of each terminal at each end + phases = dss.CktElement.NodeOrder() + nphases = int(len(phases) / 2) + + # Get the currents on each phase at each end of the connector + # This is a real array of size nphases * 2 * 2 - + # each pair is the re & imag part of the current + coni = dss.CktElement.Currents() + + for end in range(len(con_ends[cidx])): + for pidx in range(nphases): + # We don't want to save any phase 0 information which corresponds to the grounding connection + if phases[pidx] == 0: + continue + # Construct the complex current. + current = ( + coni[2 * (end * nphases + pidx)] + + 1j * coni[2 * (end * nphases + pidx) + 1] + ) + # Save the magnitude data + col, name = get_lineflow_stream_colname( + con_names[cidx], + con_ends[cidx][end], + phase_letters[phases[pidx] - 1], + True, + ) + I[col + "/" + name] = np.abs(current) + # Save the angle data + col, name = get_lineflow_stream_colname( + con_names[cidx], + con_ends[cidx][end], + phase_letters[phases[pidx] - 1], + False, + ) + I[col + "/" + name] = np.angle(current, deg=True) + return I + + +def simulate_network(loads, load_names, contypes=["Line", "Transformer"]): + # Simulates the network for all the values of load in the input loads. + # Inputs + # ------ + # loads : n x T matrix of floats + # This is the load values to set and simulate + # + # load_names : n list of strings + # The names of the loads whose values are to be set to those in loads + # + # con_types : [optional] list of strings + # The connector types for which to return current data. + # + # Returns + # -------- + # V : dictionary of real length T arrys + # Values are voltage mag & angle time series generated by simulation. + # The keys are the stream collection/name for the data. + # The collection / stream name encodes the bus, phase, and quantity + # which are formatted by the method get_voltage_stream_colname + # + # I : dictionary of real length T arrys + # Values are current mag & angle time series generated by simulation. + # The keys are the stream collection/name for the data. + # The collection / stream name encodes the connector, end, phase, and quantity + # which are formatted by the method get_lineflow_stream_colname + + [n, T] = np.shape(loads) + + # Get the buses and connectors + bus_names = get_buses() + con_names = get_connectors(contypes) + con_ends = get_conn_ends(con_names) + + V = {} + I = {} + + # Run the first simulation to get the keys for the output dictionary + # Set the new load values + set_loads(loads[:, 0], load_names) + # Solve the power flow + dss.Solution.Solve() + # Get the data + vdata = v2dict(bus_names) + idata = i2dict(con_names) + # Save the voltage data + for key, val in vdata.items(): + V[key] = np.nan * np.ones(T) + V[key][0] = val + # Save the current data + for key, val in idata.items(): + I[key] = np.nan * np.ones(T) + I[key][0] = val + + # Iterate through rest of the times + for t in tqdm_notebook(range(1, T), desc="Running simulation", leave=False): + set_loads(loads[:, t], load_names) + dss.Solution.Solve() + vdata = v2dict(bus_names) + idata = i2dict(con_names) + + for key, val in vdata.items(): + V[key][t] = val + for key, val in idata.items(): + I[key][t] = val + + return V, I + + +######################################################## +### Methods related to streams that we will create & ### +### push data to. ### +######################################################## + + +def get_stream_info(base_col="simulated"): + # Returns collection names, tags, and annotations + # for all the streams we want to create to hold + # voltage and current data across the network. + # + # Input + # ----- + # base_col : string + # The base collection level under which we want all the + # simulated streams to be organized. + # + # Returns + # ------- + # collections: list of strings + # names : list of strings + # tags : list of dicts + # annotations : list of dicts + + phases = ["A", "B", "C"] + + # The lists to store all results + collections = [] + names = [] + tags = [] + annotations = [] + + ## Get information for streams of bus voltages + # Get the names of all buses + bus_names = dss.Circuit.AllBusNames() + # Iterate over all buses and determine streams for each + for bus in bus_names: + # Set the bus to "active" + dss.Circuit.SetActiveBus(bus) + # Get the basekV of this bus + basekV = dss.Bus.kVBase() + # Get phases at this bus + busphases = dss.Bus.Nodes() + for p in busphases: + # We don't want to save any phase 0 information + if p == 0: + continue + # Magnitude stream + cM, nM, tM, aM = get_voltage_stream_info(bus, phases[p - 1], True, basekV) + # Angle stream + cA, nA, tA, aA = get_voltage_stream_info(bus, phases[p - 1], False, basekV) + # Save results + collections.append(base_col + "/" + cM) + collections.append(base_col + "/" + cA) + names.append(nM) + names.append(nA) + tags.append(tM) + tags.append(tA) + annotations.append(aM) + annotations.append(aA) + + ## Get information for the streams of connection currents + # Get the names of all connectors + con_names = get_connectors() + for con in con_names: + + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con) + + # Get the buses that this connector connects + # (the split removes terminals indicating the phases at each end + # so three phase busX.1.2.3 becomes busX) + to = dss.CktElement.BusNames()[0].split(".")[0] + frm = dss.CktElement.BusNames()[1].split(".")[0] + # Check that to and frm are different (these can be the same for capacitors) + if to == frm: + ends = [to] + else: + ends = [to, frm] + + # Get the phases on the line + # this is the phases of each terminal at each end + conphases = dss.CktElement.NodeOrder() + nphases = int(len(conphases) / 2) + + for end in ends: + + for p in conphases[0:nphases]: + + # We don't want to save any phase 0 information + if p == 0: + continue + + # Magnitude stream + cM, nM, tM, aM = get_lineflow_stream_info(con, end, phases[p - 1], True) + # Angle stream + cA, nA, tA, aA = get_lineflow_stream_info( + con, end, phases[p - 1], False + ) + # Save results + collections.append(base_col + "/" + cM) + collections.append(base_col + "/" + cA) + names.append(nM) + names.append(nA) + tags.append(tM) + tags.append(tA) + annotations.append(aM) + annotations.append(aA) + + return collections, names, tags, annotations + + +def get_existing_streams(col_prefix, conn): + # Get the existing streams under the base collection col_prefix + streams = conn.streams_in_collection(col_prefix) + # Build the dictionary of the streams + streams_dict = {} + for stream in streams: + streams_dict[stream.collection + "/" + stream.name] = stream + print("Found", len(streams_dict.keys()), "streams under", col_prefix) + return streams_dict + + +def create_streams( + col_prefix, collections, names, tags, annotations, conn, verbose=False +): + # Given a set of collections, names, tags, and annotations for intended streams, check if + # they exist. If not, create them. + # + # Returns + # ------- + # existing : dict of streams + # A dictionary capturing all the intended streams. Keys are the collection/name of the stream, + # values are stream objects. + + existing = get_existing_streams(col_prefix, conn) + + # Iterate through the desired streams and check if they exist already. If not + # create them. + nstreams = len(collections) + nexisting = 0 + ncreated = 0 + for i in range(nstreams): + stream_info = collections[i] + "/" + names[i] + if stream_info in existing: + if verbose: + print(stream_info, "already exists.") + nexisting += 1 + pass + else: + stream_id = uuid.uuid4() + + stream = conn.create( + uuid=stream_id, + collection=collections[i], + tags=tags[i], + annotations=annotations[i], + ) + + existing[stream_info] = stream + if verbose: + print("Created", stream_info, ", uuid:", stream_id) + ncreated += 1 + print("Found", nexisting, "streams. Created", ncreated, "streams.") + return existing + + +def get_lineflow_stream_info(line_name, line_end, phase, ismag): + if ismag: + unit = "amps" + else: + unit = "degrees" + collection, name = get_lineflow_stream_colname(line_name, line_end, phase, ismag) + + tags = {"name": name, "unit": unit} + annotations = {"phase": phase} + + return collection, name, tags, annotations + + +def get_lineflow_stream_colname(line_name, line_end, phase, ismag): + collection = line_name + "/" + line_end + if ismag: + lastltr = "M" + else: + lastltr = "A" + + name = "I" + phase + lastltr + return collection, name + + +def get_voltage_stream_colname(bus_name, phase, ismag): + collection = bus_name + if ismag: + lastltr = "M" + else: + lastltr = "A" + + name = "V" + phase + lastltr + return collection, name + + +def get_voltage_stream_info(bus_name, phase, ismag, basekV): + if ismag: + unit = "volts" + else: + unit = "degrees" + collection, name = get_voltage_stream_colname(bus_name, phase, ismag) + tags = {"name": name, "unit": unit} + annotations = {"phase": phase, "basekV": str(basekV)} + + return collection, name, tags, annotations + + +def add_all_data(times, data_dict, streams_dict, base_col): + # Add data to each stream. + # Inputs + # ------ + # times : list of ints + # The timestamps for the data to be added (one set of times for all data) + # + # data_dict : dict of arrays + # The dictionary containing data to be added. Keys are the collection/name of + # the stream to which data is to be added. Values are arrays of floats to add. + # + # streams_dict : dict of stream objects + # keys are the collection/name of each stream. values are the stream objects. + # + # base_col : string + # base collection prefix under which all streams can be found. + + # Create progress bar + nstreams = len(data_dict.keys()) + pbar = tqdm(total=nstreams, desc="Pushing data to streams", leave=False) + + for key in data_dict: + stream_info = base_col + "/" + key + if stream_info in streams_dict: + add_to_stream(streams_dict[stream_info], times, data_dict[key]) + else: + print("WARNING", stream_info, "not found") + pbar.update(1) + pbar.close() + + +def add_to_stream(stream, times, values): + # Given times and values, put them in the required tuple format and + # add them to the stream. + + payload = [] + + if len(times) != len(values): + print("WARNING: times & values not same size") + for i in range(len(times)): + payload.append((times[i], values[i])) + + stream.insert(payload, merge="replace") + + +####################################################### +### Convenient wrappers to obtain model information ### +####################################################### + + +def get_buses(): + # Convenient wrapper to retrieve all buses in the system. + return dss.Circuit.AllBusNames() + + +def get_connectors(qualified=["Line", "Transformer"]): + # This method returns all connection elements of the "qualified" types + connectors = [] + pds = dss.PDElements.AllNames() + for pd in pds: + # Need to split the name to get the element type + if pd.split(".")[0] in qualified: + connectors.append(pd) + return connectors + + +def get_conn_ends(con_names): + # The list of lists with names of connectors ends + con_ends = [] + for con in con_names: + # Set the current connector to be "active" + dss.Circuit.SetActiveElement(con) + # Get the buses that this connector connects + # (the split removes terminals indicating the phases at each end + # so three phase busX.1.2.3 becomes busX) + to = dss.CktElement.BusNames()[0].split(".")[0] + frm = dss.CktElement.BusNames()[1].split(".")[0] + # Check for to and frm being identical - can happen with capacitors + if to == frm: + ends = [to] + else: + ends = [to, frm] + con_ends.append(ends) + return con_ends + + +def get_loads(): + # Get all the loads in the network + load_names = dss.Loads.AllNames() + nloads = len(load_names) + + load = np.zeros([nloads]) + # Get the initial loads + for i in range(nloads): + dss.Loads.Name(load_names[i]) + load[i] = dss.Loads.kW() + + return load, load_names + + +def set_loads(load, load_names): + # Set the value of load load_names[i] to load[i] + nloads = len(load_names) + for i in range(nloads): + # Set this load to be active + dss.Loads.Name(load_names[i]) + # Set the load + dss.Loads.kW(load[i]) + + +# Get the number of various elements in the network +def get_nbuses(): + return len(dss.Circuit.AllBusNames()) + + +def get_nlines(): + return len(dss.Lines.AllNames()) + + +def get_nconnectors(contypes=["Line", "Transformer"]): + return len(get_connectors(qualified=contypes)) + + +def get_nloads(): + return len(dss.Loads.AllNames()) From 3b1a6fef91a5a429926bd57be1cd7da35fc0fa23 Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 12:52:11 -0800 Subject: [PATCH 02/10] add opendss_ingestor.py --- btrdbextras/opendss_ingest/README.md | 2 +- btrdbextras/opendss_ingest/opendss_ingestor.py | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/btrdbextras/opendss_ingest/README.md b/btrdbextras/opendss_ingest/README.md index ace6d4b..e9704bf 100644 --- a/btrdbextras/opendss_ingest/README.md +++ b/btrdbextras/opendss_ingest/README.md @@ -1,4 +1,4 @@ -# OpenDSS Simulation +# OpenDSS Simulation Ingestor ## What is OpenDSS? [OpenDSS](https://www.epri.com/pages/sa/opendss) is an open source tool for simulating electrical distribution networks. For us, it may be useful for generating realistic grid data from particular physical contexts---for example, measurements on two ends of a line, or two ends of a transformer---without any data privacy concerns. In this sense, it is especially useful for the Dominion Apps project, for which we would otherwise need to prototype and test on Dominion's PMU data, access to which is restricted. diff --git a/btrdbextras/opendss_ingest/opendss_ingestor.py b/btrdbextras/opendss_ingest/opendss_ingestor.py index aa2f610..d6ae589 100644 --- a/btrdbextras/opendss_ingest/opendss_ingestor.py +++ b/btrdbextras/opendss_ingest/opendss_ingestor.py @@ -10,8 +10,6 @@ import simulation_utils as sims from datetime import datetime, timedelta -%matplotlib inline - import importlib importlib.reload(sims); @@ -39,7 +37,7 @@ # The following cell generates data that will be converted into streams. # For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. #%% -# The number of samples to generate +# Samples to generate start_time = datetime(2022, 1, 1, 0, 0, 0) end_time = datetime(2022, 1, 1, 0, 1, 0) fs = 30 # Hz From d5481bca07e66e70f20733cf329013223b6776e9 Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 17:11:41 -0800 Subject: [PATCH 03/10] reformat for docstrings --- .../opendss_ingest/simulation_utils.py | 320 +++++++++--------- 1 file changed, 167 insertions(+), 153 deletions(-) diff --git a/btrdbextras/opendss_ingest/simulation_utils.py b/btrdbextras/opendss_ingest/simulation_utils.py index 236963b..445dbb6 100644 --- a/btrdbextras/opendss_ingest/simulation_utils.py +++ b/btrdbextras/opendss_ingest/simulation_utils.py @@ -1,29 +1,30 @@ import uuid +from typing import Dict, List, Optional, Tuple, Union import numpy as np import opendssdirect as dss -import pandas as pd -from tqdm.notebook import tqdm, tqdm_notebook - -phase_letters = ["A", "B", "C"] - - -def v2dict(bus_names): - # Returns the voltage data on each phase of the - # buses in bus_names - # - # Inputs - # ------ - # bus_names : list of strings - # Buses for which to return voltage data - # - # Returns - # ------- - # V : dictionary of real values - # The keys are the stream collection/name for the data. - # The collection / stream name encodes the bus, phase, and quantity - # which are formatted by the method get_voltage_stream_colname +from btrdb import BTrDB +from tqdm.auto import tqdm +PHASE_LETTERS = ["A", "B", "C"] + + +def v2dict(bus_names: List[str]) -> Dict[str, float]: + """ + Returns the voltage data on each phase of the buses in bus_names. + + Parameters + ---------- + bus_names : List[str] + Buses for which to return voltage data. + + Returns + ------- + Dict[str, float] + V : dictionary of real values. The keys are the stream collection/name for the data. + The collection / stream name encodes the bus, phase, and quantity + which are formatted by the method get_voltage_stream_colname. + """ # Instantiate the dict of results V = {} @@ -51,34 +52,36 @@ def v2dict(bus_names): # Save the magnitude data col, name = get_voltage_stream_colname( - bus, phase_letters[phases[pidx] - 1], True + bus, PHASE_LETTERS[phases[pidx] - 1], True ) V[col + "/" + name] = np.abs(voltage) # Save the angle data col, name = get_voltage_stream_colname( - bus, phase_letters[phases[pidx] - 1], False + bus, PHASE_LETTERS[phases[pidx] - 1], False ) V[col + "/" + name] = np.angle(voltage, deg=True) return V -def i2dict(con_names): - # Returns the complex currents on each phase at each end of each connector - # The order of results matches the input names. - # - # Inputs - # ------ - # con_names : list of strings - # Connectors for which to return current data - # - # Returns - # ------- - # I - dictionary of real values - # The keys are the stream collection/name for the data. - # The collection / stream name encodes the connector, end, phase, and quantity - # which are formatted by the method get_lineflow_stream_colname +def i2dict(con_names: List[str]) -> Dict[str, float]: + """ + Returns the complex currents on each phase at each end of each connector. + The order of results matches the input names. + + Parameters + ---------- + con_names : List[str] + Connectors for which to return current data + + Returns + ------- + Dict[str, float] + I - dictionary of real values. The keys are the stream collection/name for the data. + The collection / stream name encodes the connector, end, phase, and quantity + which are formatted by the method get_lineflow_stream_colname. + """ ncons = len(con_names) # Get the ends of each connector @@ -107,61 +110,58 @@ def i2dict(con_names): if phases[pidx] == 0: continue # Construct the complex current. - current = ( - coni[2 * (end * nphases + pidx)] - + 1j * coni[2 * (end * nphases + pidx) + 1] - ) + current = (coni[2 * (end * nphases + pidx)] + 1j * coni[ + 2 * (end * nphases + pidx) + 1]) # Save the magnitude data col, name = get_lineflow_stream_colname( - con_names[cidx], - con_ends[cidx][end], - phase_letters[phases[pidx] - 1], - True, - ) + con_names[cidx], con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], True, ) I[col + "/" + name] = np.abs(current) # Save the angle data col, name = get_lineflow_stream_colname( - con_names[cidx], - con_ends[cidx][end], - phase_letters[phases[pidx] - 1], - False, - ) + con_names[cidx], con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], False, ) I[col + "/" + name] = np.angle(current, deg=True) return I -def simulate_network(loads, load_names, contypes=["Line", "Transformer"]): - # Simulates the network for all the values of load in the input loads. - # Inputs - # ------ - # loads : n x T matrix of floats - # This is the load values to set and simulate - # - # load_names : n list of strings - # The names of the loads whose values are to be set to those in loads - # - # con_types : [optional] list of strings - # The connector types for which to return current data. - # - # Returns - # -------- - # V : dictionary of real length T arrys - # Values are voltage mag & angle time series generated by simulation. - # The keys are the stream collection/name for the data. - # The collection / stream name encodes the bus, phase, and quantity - # which are formatted by the method get_voltage_stream_colname - # - # I : dictionary of real length T arrys - # Values are current mag & angle time series generated by simulation. - # The keys are the stream collection/name for the data. - # The collection / stream name encodes the connector, end, phase, and quantity - # which are formatted by the method get_lineflow_stream_colname - +def simulate_network( + loads: np.ndarray, load_names: List[str], + con_types: Optional[List[str]] = None + ) -> Dict[str, np.ndarray]: + """ + Simulates the network for all the values of load in the input loads. + + Parameters + ---------- + loads : np.ndarray + n x T matrix of floats. This is the load values to set and simulate. + load_names : List[str] + The names of the loads whose values are to be set to those in loads. + con_types : Optional[List[str]] + The connector types for which to return current data. + + Returns + ------- + Dict[str, np.ndarray] + V : Dictionary of real length T array. Values are voltage magnitude + & angle time series generated by simulation. + The keys are the stream collection/name for the data. The collection + / stream name encodes the bus, + phase, and quantity which is formatted by the method get_voltage_stream_colname. + + I : Dictionary of real length T array. Values are current magnitude + & angle time series generated by simulation. + The keys are the stream collection/name for the data. The collection + / stream name encodes the + connector, end, phase, and quantity which is formatted by the method + get_lineflow_stream_colname. + """ [n, T] = np.shape(loads) # Get the buses and connectors bus_names = get_buses() - con_names = get_connectors(contypes) + con_names = get_connectors(con_types) con_ends = get_conn_ends(con_names) V = {} @@ -185,7 +185,7 @@ def simulate_network(loads, load_names, contypes=["Line", "Transformer"]): I[key][0] = val # Iterate through rest of the times - for t in tqdm_notebook(range(1, T), desc="Running simulation", leave=False): + for t in tqdm(range(1, T), desc="Running simulation", leave=False): set_loads(loads[:, t], load_names) dss.Solution.Solve() vdata = v2dict(bus_names) @@ -199,31 +199,31 @@ def simulate_network(loads, load_names, contypes=["Line", "Transformer"]): return V, I -######################################################## -### Methods related to streams that we will create & ### -### push data to. ### -######################################################## - - -def get_stream_info(base_col="simulated"): - # Returns collection names, tags, and annotations - # for all the streams we want to create to hold - # voltage and current data across the network. - # - # Input - # ----- - # base_col : string - # The base collection level under which we want all the - # simulated streams to be organized. - # - # Returns - # ------- - # collections: list of strings - # names : list of strings - # tags : list of dicts - # annotations : list of dicts - - phases = ["A", "B", "C"] +############################################################################### +# Methods related to streams that we will create & push data to. +############################################################################### + + +def get_stream_info(base_col="simulated") -> Tuple[ + List[str], List[str], List[Dict[str, str]], List[Dict[str, str]]]: + """ + Returns collection names, tags, and annotations + for all the streams we want to create to hold + voltage and current data across the network. + + Parameters + ---------- + base_col : str + The base collection level under which we want all the simulated streams to be organized. + + Returns + ------- + collections: List[str] + names : List[str] + tags : List[Dict[str, str]] + annotations : List[Dict[str, str]] + """ + phases = PHASE_LETTERS # The lists to store all results collections = [] @@ -247,9 +247,13 @@ def get_stream_info(base_col="simulated"): if p == 0: continue # Magnitude stream - cM, nM, tM, aM = get_voltage_stream_info(bus, phases[p - 1], True, basekV) + cM, nM, tM, aM = get_voltage_stream_info( + bus, phases[p - 1], True, basekV + ) # Angle stream - cA, nA, tA, aA = get_voltage_stream_info(bus, phases[p - 1], False, basekV) + cA, nA, tA, aA = get_voltage_stream_info( + bus, phases[p - 1], False, basekV + ) # Save results collections.append(base_col + "/" + cM) collections.append(base_col + "/" + cA) @@ -293,7 +297,9 @@ def get_stream_info(base_col="simulated"): continue # Magnitude stream - cM, nM, tM, aM = get_lineflow_stream_info(con, end, phases[p - 1], True) + cM, nM, tM, aM = get_lineflow_stream_info( + con, end, phases[p - 1], True + ) # Angle stream cA, nA, tA, aA = get_lineflow_stream_info( con, end, phases[p - 1], False @@ -312,7 +318,7 @@ def get_stream_info(base_col="simulated"): def get_existing_streams(col_prefix, conn): - # Get the existing streams under the base collection col_prefix + """ Get the existing streams under the base collection col_prefix """ streams = conn.streams_in_collection(col_prefix) # Build the dictionary of the streams streams_dict = {} @@ -323,16 +329,19 @@ def get_existing_streams(col_prefix, conn): def create_streams( - col_prefix, collections, names, tags, annotations, conn, verbose=False -): - # Given a set of collections, names, tags, and annotations for intended streams, check if - # they exist. If not, create them. - # - # Returns - # ------- - # existing : dict of streams - # A dictionary capturing all the intended streams. Keys are the collection/name of the stream, - # values are stream objects. + col_prefix: str, collections: List, names, tags, annotations, conn: BTrDB, + verbose: bool = False + ): + """ + Given a set of collections, names, tags, and annotations for intended streams, check if + they exist. If not, create them. + + Returns + ------- + existing : dict + A dictionary capturing all the intended streams. Keys are the collection/name of the stream, + values are stream objects. + """ existing = get_existing_streams(col_prefix, conn) @@ -352,11 +361,8 @@ def create_streams( stream_id = uuid.uuid4() stream = conn.create( - uuid=stream_id, - collection=collections[i], - tags=tags[i], - annotations=annotations[i], - ) + uuid=stream_id, collection=collections[i], tags=tags[i], + annotations=annotations[i], ) existing[stream_info] = stream if verbose: @@ -371,7 +377,9 @@ def get_lineflow_stream_info(line_name, line_end, phase, ismag): unit = "amps" else: unit = "degrees" - collection, name = get_lineflow_stream_colname(line_name, line_end, phase, ismag) + collection, name = get_lineflow_stream_colname( + line_name, line_end, phase, ismag + ) tags = {"name": name, "unit": unit} annotations = {"phase": phase} @@ -414,22 +422,24 @@ def get_voltage_stream_info(bus_name, phase, ismag, basekV): def add_all_data(times, data_dict, streams_dict, base_col): - # Add data to each stream. - # Inputs - # ------ - # times : list of ints - # The timestamps for the data to be added (one set of times for all data) - # - # data_dict : dict of arrays - # The dictionary containing data to be added. Keys are the collection/name of - # the stream to which data is to be added. Values are arrays of floats to add. - # - # streams_dict : dict of stream objects - # keys are the collection/name of each stream. values are the stream objects. - # - # base_col : string - # base collection prefix under which all streams can be found. + """ + Add data to each stream. + + Parameters + ---------- + times : list of ints + The timestamps for the data to be added (one set of times for all data) + + data_dict : dict of arrays + The dictionary containing data to be added. Keys are the collection/name of + the stream to which data is to be added. Values are arrays of floats to add. + streams_dict : dict of stream objects + keys are the collection/name of each stream. values are the stream objects. + + base_col : string + base collection prefix under which all streams can be found. + """ # Create progress bar nstreams = len(data_dict.keys()) pbar = tqdm(total=nstreams, desc="Pushing data to streams", leave=False) @@ -445,9 +455,10 @@ def add_all_data(times, data_dict, streams_dict, base_col): def add_to_stream(stream, times, values): - # Given times and values, put them in the required tuple format and - # add them to the stream. - + """ + Given times and values, put them in the required tuple format and + add them to the stream. + """ payload = [] if len(times) != len(values): @@ -458,18 +469,18 @@ def add_to_stream(stream, times, values): stream.insert(payload, merge="replace") -####################################################### -### Convenient wrappers to obtain model information ### -####################################################### +############################################################################### +# Convenient wrappers to get model information +############################################################################### def get_buses(): - # Convenient wrapper to retrieve all buses in the system. + """A convenient wrapper to retrieve all buses in the system.""" return dss.Circuit.AllBusNames() def get_connectors(qualified=["Line", "Transformer"]): - # This method returns all connection elements of the "qualified" types + """ This method returns all connection elements of the "qualified" types""" connectors = [] pds = dss.PDElements.AllNames() for pd in pds: @@ -480,7 +491,7 @@ def get_connectors(qualified=["Line", "Transformer"]): def get_conn_ends(con_names): - # The list of lists with names of connectors ends + """ The list of lists with names of connectors ends """ con_ends = [] for con in con_names: # Set the current connector to be "active" @@ -500,7 +511,7 @@ def get_conn_ends(con_names): def get_loads(): - # Get all the loads in the network + """ Get all the loads in the network """ load_names = dss.Loads.AllNames() nloads = len(load_names) @@ -514,7 +525,7 @@ def get_loads(): def set_loads(load, load_names): - # Set the value of load load_names[i] to load[i] + """ Set the value of load load_names[i] to load[i] """ nloads = len(load_names) for i in range(nloads): # Set this load to be active @@ -523,18 +534,21 @@ def set_loads(load, load_names): dss.Loads.kW(load[i]) -# Get the number of various elements in the network def get_nbuses(): + """Get the number of buses in the network""" return len(dss.Circuit.AllBusNames()) def get_nlines(): + """Get the number of lines in the network""" return len(dss.Lines.AllNames()) def get_nconnectors(contypes=["Line", "Transformer"]): + """Get the number of connectors in the network""" return len(get_connectors(qualified=contypes)) def get_nloads(): + """Get the number of loads in the network""" return len(dss.Loads.AllNames()) From b98c03117a732e983cf5ebd9527b8e117c638c7e Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 18:45:21 -0800 Subject: [PATCH 04/10] update files for running opendss --- .../Models/13Bus/IEEE13Node_BusXY.csv | 16 ++++++++++++++++ .../{IEEELineCodes.DSS => IEEELineCodes.dss} | 0 2 files changed, 16 insertions(+) create mode 100644 btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv rename btrdbextras/opendss_ingest/Models/13Bus/{IEEELineCodes.DSS => IEEELineCodes.dss} (100%) diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv new file mode 100644 index 0000000..ba14e1e --- /dev/null +++ b/btrdbextras/opendss_ingest/Models/13Bus/IEEE13Node_BusXY.csv @@ -0,0 +1,16 @@ +SourceBus, 200, 400 +650, 200, 350 +RG60, 200, 300 +646, 0, 250 +645, 100, 250 +632, 200, 250 +633, 350, 250 +634, 400, 250 +670, 200, 200 +611, 0, 100 +684, 100, 100 +671, 200, 100 +692, 250, 100 +675, 400, 100 +652, 100, 0 +680, 200, 0 diff --git a/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS b/btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.dss similarity index 100% rename from btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.DSS rename to btrdbextras/opendss_ingest/Models/13Bus/IEEELineCodes.dss From 7ec00be361540eb6b0252f1fc2a76bd173521b5a Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 18:47:11 -0800 Subject: [PATCH 05/10] add the notebooks --- .../IEEE_13_-_Create_Streams_Add_Data.ipynb | 5042 +++++++++++++++++ .../Intro_to_Simulation_with_OpenDSS.ipynb | 405 ++ 2 files changed, 5447 insertions(+) create mode 100644 btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb create mode 100644 btrdbextras/opendss_ingest/notebooks/Intro_to_Simulation_with_OpenDSS.ipynb diff --git a/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb b/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb new file mode 100644 index 0000000..f0ec190 --- /dev/null +++ b/btrdbextras/opendss_ingest/notebooks/IEEE_13_-_Create_Streams_Add_Data.ipynb @@ -0,0 +1,5042 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import opendssdirect as dss\n", + "import matplotlib.pyplot as plt\n", + "from tqdm.notebook import tqdm_notebook, tqdm\n", + "import btrdb as db\n", + "import uuid\n", + "from btrdb.utils.timez import datetime_to_ns\n", + "import simulation_utils as sims\n", + "from datetime import datetime, timedelta\n", + "\n", + "%matplotlib inline\n", + "\n", + "import importlib\n", + "\n", + "importlib.reload(sims);" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the database\n", + "conn = db.connect(profile=\"collab\")" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "model_loc = \"./Models/13Bus/IEEE13Nodeckt.dss\"\n", + "dss.run_command(\"Redirect \" + model_loc);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create output streams\n", + "The following cells create the output streams or retrieve them if they have already been created. " + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "prefix = \"simulated/ieee13\"\n", + "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nstreams = len(collections)\n", + "print(\"Creating\", nstreams, \"streams\")\n", + "for i in range(nstreams):\n", + " print(collections[i] + \"/\" + names[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 234 streams under simulated/ieee13\n", + "Found 234 streams. Created 0 streams.\n" + ] + } + ], + "source": [ + "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate simulated measurements\n", + "The following cell generates data that will be converted into streams. \n", + "For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. " + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We will generate 1800 samples.\n" + ] + } + ], + "source": [ + "# The number of samples to generate\n", + "start_time = datetime(2022, 1, 1, 0, 0, 0)\n", + "end_time = datetime(2022, 1, 1, 0, 1, 0)\n", + "fs = 30 # Hz\n", + "T = int(((end_time - start_time).total_seconds()) * fs)\n", + "print(\"We will generate\", T, \"samples.\")\n", + "\n", + "# Generate the nanosecond timestamps for the data\n", + "start_ns = datetime_to_ns(start_time)\n", + "end_ns = datetime_to_ns(end_time)\n", + "timestamps = np.arange(start_ns, end_ns, 1e9 / 30, dtype=\"int\")" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Generate the randomized scaling factors\n", + "mu = 1.1\n", + "sig = 0.1\n", + "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n", + "\n", + "# Generate the new load values\n", + "new_load = s * load[:, np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running simulation: 0%| | 0/1799 [00:00]" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wjpnN8QFeBdAOwHoAcwHcxswhaB2F6FKyFpHOI7kYmv5vEgEbCsk9X4J4AUXhxSHgo4tj7bmycmQuoCPP1f46hRsAorU3c4M3zBm5+Yh66f57XOw8xVfXSzJ3EPq5hfA0Kigrib0Hi0ZHPhu/GZOnaxw0coMPzokEtfrlSeDTy53PsRMuh2TamuQ6F4+OPeaZOEYIMs8Qv3NEMUJfr9dvzwLbjbZiugeUEysEZ7wbO9cj/m7Fkw2Bz66wT2PFKtFN2UV75BDw3+Njjxsj0VBICwpomHuX/KDtMbB0rDx/49pGnhvdyZpJwmjQtdAnoioAPgdwOzTNfSgA2TY8/QDMAtAIQGcArxJRNQ/lXEtE04lo+pYtdjv1eMDcmHJ0oW8Opwpo9vkXO0hWIrppKEIamZlGfHlX/2EhEDx2Lk8195be6kUjiv1tyn+jv0cN/fW0G2ZFFrDIMHz0RWa+b50+VIaYe2BopECk0zKeIZdpHd8j1SPzAkFhV08RuxdVXOHrWXFI0GTeY7X8nRcqix4h/aWbFHcJSlJMfJ4chK+j7FDk+G/PxJo57CakAWcNfeNczU3Wibh8942Fhau0zkt8vkt+BEaeZ6qnVyGeJkKfiPKhCfyRzPwFgJYAWgCYTUSrADQBMJOIGgC4AsAXrLEMwEoAbQGsA9BUyLaJfiwKZh7OzN2YuVvdunX9X5n1xUQ8RWTM1bdgMxqy0UDMk0FOmAOUmZEJQ7E8t9i9CJ9eERtEy5w+Sgszm35sNnUwzps1Enj5KJt0ZbFDYqmWrLN3kxBqQJqhnk7fCvCnByPhC4IOoWCYpkJl2vzBqEuANyTaX1T1JM/PEBTjH43dwtBVPVwIgkeqR+6zm9GfLzh6lGBM+trVj3KAXTGveTQ7VmkmtzH3e6uOORzJf4/XFsQ5EU+QPTeL98IB43x0Lumg6eu2+REAFjLz8wDAzHOZuR4zFzFzETRTTRdm3ghtUrePfm59AG0ArAAwBkBfIqqpT+D21Y8lH8p1kyj6azx72hqLN8QHmuOmDnEy3xwOGZIGrzfMA9tjh/12Ql+MZ1TqMKEqG1FZ4TSRKxOqRgftdf7ACSO/T4do8weLvtNW0X52lV0FYw8ZoR32bgK+lJnIJKyfFTvScsKYnwl6xGPAHNsmDuw03XeJpm8ZNpy1TvClTpo/vVdhLFt45Yo4NH1jXs61cpY410u/2Ki8YY4DMBjAXCKapR8byszfW6QfBuBdIpoLTXLey8xbAYCIhgEwAtk8xswOgWcShBvh8P5ZwLU2k1peeLoFcNk3wBfXCHWwEPq7HbQikcUuJ4QMQmWxL5Y4ufzTQ6b0NlqNl0mmINv9GpvNJra6WEnsBSJtA/mF30Yfn/eZ9TlO/vlOLqEvdATqtQWqidNdLrU/Ix692WZ8YCewY6W7POxY8HUkpITBmqnR7xOHor1zKAcosXA9DIUi8ZBW/pY8+RiPpv/OacBQlxE3zW1BdN92e04CcBT6zDwJDq1O1/aNz+uhafGydG8DSGIYw3DB0d/dDqGGnwjkVYx8H+PGy8eC/50Z/T3H4tZ78RP/6AJvdQiVxjZ48ZrWmPyQAxtqBtiQ96x3ThMUlCNf0WkFh/8z5SPz4bZg19/x7fYkmyT0Ou9jhTT8MkcrMD8/rv0zsIsvxGWR0WWOYPtPNPEI1uI92ijMdScqPHtXex+kgdDPTDwIM9F0YTfrDkTszG7IScFi6LISxDQq0X/YjB/b8Fc3xR6bahOrPdFsmA0MP8nfua7NRXp7mvWB3GXzkHAf/YR5SHd8LwwUJoYp158GbhVjyor3zoh/DwKnXe7CCO/a7y+6PCXxQj9LwjCIN1KyBD8o7HzlveB18YrrfEskL5bNC+tnE5dZkhWdS1MzdQNA0678DueJgI1znNMZcwpufPT91CWd47Mz+3+fQqWRtp6T68+V1I3rrcHSscFtOuMGUew4xe+XnpQYskTom0iHl8hqRSdgv4tRPITKIlFDs4GSA8CcT/yf71aYyRbPWeFHsE35P3fp3uztPe+4iUPosxDLJyfP20jZYN1092l9BemLg01zve/vrDT9gIi5kWkg9O0IclWnSFlJbATNzR43obYj2S+VE6/39DZHYiaokZvIhlnez9m+3HmPZyDaXz5ZMFs7JThRvC9yP/zm4YXpiduYxBIvnmsAojT9RpJ4XgGQnTb9RJl3gsJPIDg3bHMhOPzySrfE5u+HRHWeqeDVbqmugTV+R85fXBeZ6PUTltoK2W5z5QVRQW3lcmc1j6S59EsQ6WDesSMelzI7vnBYMBYP6SbwFUmC/b9Pce0cZoPb3ebSETEuf4LW8mSJ0Bf9pA9oGx+kM1UbJCbfZLo7KrKDeCZyFbGIEUTjiiJrTXY+rTmyuDhpxIQnUl0DhcIlSugHyuyPIp8nPZ+QIrLDpp8oG7lCke18fGmqa6DwSHZ00V7cuhQKhSKDyQ6hr1AoFOWNdmc4p/GBEvoKhUKRjhx9jXMaHyihr1AoFOlIglzLldBXKBSKtEQJfYVCocgelKbvE3GTEIVCoSg3KKHvjZID2v6pdtEsExTQSKFQKOImQZp+5i7Omvom8Mcr9vu8qpWECgDofImHeOcKRbJQmr43yoq1v6XF1mnSUehXqpPqGmQfydikXqHwirLpe8XFZgTpKPSbH5vqGmQfsv2KK9VOfj3i4QLJjmWKco4S+t5I0h7LtuRV8H5OpVrB10Nhj2wDj6oNk1+PeKjayFv6dmcmph6K4EiQUpq5Qt+Q+rI9Ww0SvdOQn92ASg8FXw+FPTJN38+2hqnEq1JYWDUh1VAEiDLveMTNXpO71yW2DjJhYkduYXI3bs4WTh1m/3uu5DklaiObRNDmNHiW+knYi1URL0roeyQNGnWOy9vb9fLI53ScZyjvdDwPeGSX9e9eO+d0g0P+2k3RCcHXReGPao1jjyVog7/MlTDpoMm4Ne807KR/CGhDimNujj+PjMLh7ZEK/SS2H9kL7wUO+TMFXP4dcO2v8ZVtpmZRfOdf/Gkg1Sh3SOf/lKZf/jCESYXqQPtzon+7aBRQsVZ0OiAYoX/01c5pelwffznlBad7KhP6yVQa8grjO59D8C0gGnWOr+ygaXVqqmsQLEdeANw2B7jOyWwraW/Kpu+VNND0Df/vq8cDg16N/k0UKobQqVgT6Pdk/OW68Tzp9+/4y7HCymvp/PcSV6Ydvl4em/aTV9F3VaTk5Md3fqgkYQJCytn/B5z2nPy3eDvLZF5HMjh1GFCzOVBQJdU1CZO5Qj8dJuIMoc8MFFQ2/cgICxZR6B92Uvzl5hYANZrZp0nky2U1cVqvHXD977HHxRciXvOAFIdrlW1AbSe8gp53cZtf38e1v2eaFIjtK2LzOPkhYOj6+Osmo9OF7kaTfkkjAWnJSfe7S2c8lzSaq3OsCRE1JaIJRLSAiOYT0W2m3+8iIiaiOsKxk4holp7+V+F4fyJaTETLiOi+YC8lDnrdA1R3EJJ+CNv0HbQfo3OgXHeTirVaOpRLQNvTndPY4dRp2FGhul6GqXlRDtCgA1C3XfRxUcCecJf7cnrc4C6dUY/rJsp/lwp9G6Uh6BfYvCLY6t4ffbU2Id1lMFC7VeT4wd0Id2y1W2lpev1TomikOTdN0/4mwrTW/6nYY/FsUuJ2vs6t0E+iOdFN6y0FcBczHwGgJ4CbiOgIQOsQAPQF8LeRmIhqAHgdwJnM3B7A+frxXACvARgA4AgAFxn5JAQvN5EoMZqvIcCd6iI2IL+eJG1OA469Rc+PgBPvBVr3tz/nuNvlx4duAG6ZCZzyqLc6tOzjkEC/x6KP+OEmG26oTNNkz3rDubx6bd3Vy3i2FWvKf6+jC9Dj74gcsxX6AbcVs0Cofbg8Xa6F7b/kQKROTsKloDz45ydAANY6LPr7gKfjy8+tZ57b5yIjQR2BY02YeQMzz9Q/7wGwEIDhbvACgHsQ/ZQuBvAFM/+tn7NZP94dwDJmXsHMxQBGARgUyFXImPish8QUnPbWoKOQrZGn5OExIywERUEvNqaeN0a0ZpHqEm+PE+7Shv+Ga2LFGsBAh3vQ52H58YJKQG4+cPzt0cdbnAh0usg6P/M9NDfa8AsgCM1LP0PU/TnsJE2T7XyxTcUBNDsG6DJE8/boejnQeoB9enO5Ih3P00whpzwir3vz480ZOZdl4CacQ8yIyEKLtBI0ZcKCPqcOqVlP5/rEhUtBdVhvieeQfq5XYWfcr/xKNmlM967Hdd7KMON2RXN5NO+IEFERgKMATCGiQQDWMfNsU7LWAGoS0S9ENIOILtOPNwYgLoFdi0jnIZZxLRFNJ6LpW7Zs8VI9d1w0KvYY5QQ3B1ClfuTzwKc1U0zNFvbnWGn3/f8NNJW8pOe9G/396p+BJl1j04kC4NwRknKFx+/GVJJXQesMDAZ/Ff37yQ9qWqoYP+i2ORFzVPg6TYLJeMmHrgdqOdyrhp2BId8BV/6oXV/rvsAZLwEXj9J+k2EnCK+ZoI0AzKYQFlbkms/3oukXVnNOYzbvuAkAJ9bhpKEeBCWb/saBW7u2jJzc6LYECNfgVei7EGNBDs76PBwZHTqW61bop5d5BwBARFUAfA7gdmgmn6EA/iVJmgegK4DTAPQD8BARtXZbDjMPZ+ZuzNytbt26bk+LD6LETB4ddhJw60wgX+bNIvHekQmT426NPVa5NnDZNy4qIOTX8Tz7pP1dePNQTiTP01+I9S9v1Bm4ZYYwOmHNc+Gyr7VRSM3m0enPeSuSzlxfQBPuZuq2BVpYLCqyEsbhF07yu5WwDMq8c/En8uON9U661mHyuY+z3ogOonbivdZlnHQvLO/hkRdGf2cP2rSTqbHXPfa/y+aejI7i+DskIxq/mr5+zXV0MXPUpbJE3vJ0U56XtOVN0yeifGgCfyQzfwGgJYAWAGYT0SoATQDMJKIG0DT4Mcy8j5m3AvgNQCcA6wA0FbJtoh9LLtIGRfbDvfvXus/f7cNt2gPhRm7XiGSaPgAcdmJwdXGqg5hGTOcoZHVqNI3MN4hUb6L9PVEXHmZ/9RYnRNYyGNiOyKyuQWJWatzNPj835Vi5LQJAq76aEDKuUZb/nQs1E4dM6He+GGh3RuRY76E29UGkXZufSagk+rsxigiVWud1+Cl6Xg4jjpycSOdl0OHcyOeWvWPP6XmDZoIsOt6mfXrVevVrrtEMeHCzZvaLSRKkqu8hr9wC7a/T6C2dJnKJiACMALCQmZ8HAGaey8z1mLmImYugCfouzLwRwNcAjieiPCKqBKAHtHmAaQBaEVELIioAcCEAN+pqsMheZgLQVdJQDDwFp3LRIB7ZBVSpJ5xic048GkIi3TJtXRrdlqvnccJd2j2RvRjmY0df5TJvWX0kHZYfoX/iPZqXjHnBncglnwI3T7O+FxwCqjUCKlSLFa7iOdWbwh0Wmn6ZaT+JJt21v+bOQMRo727MTKInFuUAJ/9LELqyaxeOmfP3MgqJylLIM6/QudzwZ4dyzK6xsvKcMIR+OdP0jwMwGMDJuhvmLCIaaJWYmRcC+BHAHABTAbzFzPOYuRTAzQDGQOsEPmHm+XFfgVekL3OShn6dLwVa9PJWvqsGZtF4A29oBFcvjFO5YYHrZs8DQTA8sst+ItLyXkk0faOOVsLdLspm3TbALdNdhsEWyjQELqAJR3NdZFw/Cbh1lnP9LDV9PV3/p4AHNmlrJQCgzEbTN8I0u3GfNe7fKY8C96zQtP8K1eR1MR+LuW4XNn2p262pnNouXJrd0GWwu3RBlGXQ/TrvYbI94sZ7ZxIzEzMfycyd9X/fm9IU6aYc4/szzHwEM3dg5heF498zc2tmbsnMTwR6JW6Ravo+hX71ptpwUsTo2WWc9Row5FubDB1eEs9YnHvLTJ/ZkUuB7VRnLzZRLx2XkK9oFpPZ9I0gd1YCoq7gDppvWoFrd+1nvaGtwA6XLZRpmLjang60OiVy3OyV0/uByOeKNeST253NXlQWmn6zY7S/LXppc0vG5Kmdpn/yg8B5b7sU+nqnUqW+4BJrqkPU3I+N0HfS9K+bCDQ8Mva4MWdmTJpXqiUJsCfmGaeSZ663zHTpxFGDgTtEnVevX88boq0ACSB9xhzJwq+m3/Ef8uOiHbphZ+C05z1UxqZcUei0OyP6u5kazeXHrQSmkzeRyEPbgPPfFTN1PifIEYaXdQuigD3hLmFoLalz54tjzWwi/R6PfLby75fR+WKgSTexUsJHY3RhdmU1mTnqmRavyTjhn/Lj5ks99hbNg6q+viTGuJ9ms49IXqFmm3ejcBjvk50pSLxeL3maMTsCGLTur400zM4IMk89r5zxkkSom66h7+NwpFItrT6nPqYfYPl8TxLCUGSf0LcLbNTxfOvTKrvwJLruV6CKF48jC43mnpXAtb9Evl/wgX0gKqsyLSdaLY6fOwK4cUr0sdw8SM0jZsRFP15t+na4XQRjJqrj8eFBIS6EqtEc6ODg/WRZDxfzCGe+rJn+vOZ72nPABfqG7lbaMVG0sDQ8q8S9mCvUiA7vLevYrcJjGCu/RXdZ4zqNuYG2ojVYyNu8EjpsLrNqF6Z6GfGjKEdbU2KYlQzaDIhEsK1QIzIH40Wwdr08Vqj7FcxtBnhTIBJE9gl92ZJ/I+BVG5sFPr3+GdspJKpXrlTLm0nBK3aLlKSrXAXTgeHZ0bhLdJIrfxDyd2nTd4On3cdI+jGMbLMUV9kScN4IbUGR95Mln03PskYzzfTnlaOvBtoZ4TYMm77DvW/cFRj0GnC6MCK9b7Wm0dpxzQT58fZnaaPBuhKv7IIqwF1LokMgiM++TmtNi75lpvavmsmWLS6WA2I9jgoNN2ub9mS8s9WbaNdtlb5SHQ8r0F22X5nLsaWLsMuiAyC7hf7xd2iLkgyPkPbnWC9SqlQLOPetyPdjbgYu+SyYOhkaTtPu1mlkQ946rYHj77Q+x41me+GHwpDTqmxhkrDtQG0RleiqV/twi5XIDriayPXrdirRsP2GuKhQw3TAY4iP8GcL804QhNuHg0Ai0vzYZSu9xTRm7CatYzpToXOrWt/0u+i9k6Np0bVbyudWjjJNpJq96Kwmr0WOuRl4YKNmxjOnE59DtUaxK9CtMNr+iffFuhSLNOgQe8y4j5aRcBNv3innWwb5QBROhVWBAf+JfCfSbJlTbOK+tDwZaDMQ6B5HsCYz1Ztq0Sfr2Kxhkwn9m6fZ5+tGo257mnMa8yShsXrVCDFgNn04luvSZc5VXhY07gq0PxuY83FktOAphLFQt+7XRtfFk8z24CYaD0Y4a3FFuF+s7vlVY93V3dYF2cPzFOcJTn8xdgVvuB3aCF4iYdRsY6Z0W6/7/o50mL3v1/5ZFx57qO3pmhk1hRvTZ5fQf2QXcGivfRqnhz/4S+dyrv0V2LHSfb0AuVYg4kdQBDWhanjCdLsy+nilWsD96+xjnkjrlWBt5oofNa+XQa9p+xMYmqYfTb9+B0FT9VFv6egjAZp+/faaX7mrTtwndiNRGbIRjZc2KXbSnS6M/b39OcD+7UCXy2J/s0Um9E31suo87UZIMXladC7S1fHJs+9kl9AHPGihcdCos7cdidwM931phwEJ1+qNrfeYDdtVJVjZwNufBayaGBv5UIqHa2jUBVgzJTICyc0HKgsTlm4WGxkYE5e9LLxk3CKdyE3QCx6EX3kg2D0zL5q+IJ7EOa5+TwINjtTMQz2u9VAvD27GN031kG8cNDlacxRYoztQJMF7J/uEfhJsZgkhlZq+H26bY+3x1O0qzV7rZpvAAn0U0aiLfToA6DsMOPJ8+aQi4O2FKqxqv5m6LxKo6aclNp5ybrDqpI+5yV91DBdeMYS2gXm+oGINf2V45epx2t/n2yenPGST0D/jZe2vU6NLRZ/g5kWwmzCKJ99EYeVTDWj1crsvrGGrdppsBjTN3hwLJiH4FNphmZ/hQt+23Xnx3ErAZjWyjrxKfeCc4cGW5Rvx/qQonn7GYNjMHRtSmo4EjKBknkjTa/GEDx/7eDjteWsbsV/zzMkP6htjl1NNv57PvY6ksQ19CH1xTUEi6HU3ULVBYsvwQoKVtezR9MMaoAuXNq8ce6tzvI94casZi6RRkCffhIN/BdlUbZ6xbUA3ny9jr7u1v8t1X/fypOnfNNWHQAzIe4dIW5hoLLAKnACfw+kvaFuujjzXOW2i6+JA9gh9A0fzji4oax8ObFvmLs++FhuBO1GpDnBgR+J69lSad4LirNeBGe969xyxYvCX3sJQBEkiXTYTRd02cZzsU5BVaQDs3ah9FsNLJ4og3pNuV8bXmff5F/Dlde5W/sdJ9gl9R01fnzxKxot52VfAsnGJW5qdCZp+5Trxe9CItDw5gEwkL/cJd0XvGCbFoe0dfwewdanvWsXN1T8Dy8bGn0+8XkrXjAc2zI6/HsnG3HnYBV800+nCiFtqtcbA+r+AvIr25/gkM4V+PLHec5Io9Ks3McU8CZoM0PTTiQYdNaEoC9LWR7aJnAknYWgOO5BsmnSVb7vpGUm7a9oj4pboRPUm1pvPpDunPKpv/lMzNpSKW856HVhyViRIXsBkn9BPJ00/0WSCeSed6P2Atvip0VE+MyinE7m+Ea7z0s+BXcnfKM8zRw2OhKMWOfNVd26cbkM52FGhuuZ6nCAyU+jbbvDhVtNnbdm5m42t0xUl9IMlN88UNtkjiV6clS7I2l1hVYtgfmnGIIvdstJm4Vv8ZKbQj8e8IwbFCmryMChOf9Hj1o0mrpkA/D05sOoovJIlmv6RFwB/vGIfqjwdOOEubQ7Fb9jsckpmCv14Xqqwpm+zXV6q6HZFfOc37hIbElmRPBIZZTOdqN0SeGBDqmvhTPUmwOWS8McZTmYK/Xheqkyy6SvSi6bdtRAUx92a6poospjMFPpxafr6LVFCXxE0ObnRm5coFCkgAxy5JbjR9K22p0umy6ZCoVAkmezU9B/cbLOhRjlcNalQKBQuyUyh76Tp28WxyRa3OoVCkZVkpnknHpt+tnhYKILhhH8mKZyzQhEMmSn04zHNhIW+Mu8oXNDnIeCan1NdC4XCNRkq9OPQ0o39Xk+6N5i6KBQKRRqRmTb9eMw7uXkJ2CZPoVAo0gNHTZ+ImhLRBCJaQETzieg20+93ERETUR3T8aOJqJSIzhOODSGipfq/IcFdhgllj49w4UfALTNTXQswM96auAKbdh9MdVV8s/tgCRZu2J3qaigUceHGvFMK4C5mPgJATwA3EdERgNYhAOgL4G/xBCLKBfAUgJ+EY7UAPAygB4DuAB4mogQFkldCP0zbgYnf1csFq7btx+OjF+L6D2akuiq+ufjNyRjw0sRUV0OhiAtHoc/MG5h5pv55D4CFABrrP78A4B7EStlbAHwOYLNwrB+Ascy8nZl3ABgLoH981besdPT3Sz5LSDEK95SWaRPjew6Wprgm/pm3Tmn5ivKPp4lcIioCcBSAKUQ0CMA6Zp5tStMYwNkA3jCd3hjAGuH7WkQ6j8RRvRnQ6tSEF6NQZBLb9xVj98GSVFdDkQBcC30iqgJNe78dmslnKADZdkEvAriX2Z/PIxFdS0TTiWj6li1b/GQRremrkPJpgTK4lS+6DBuLHk+MT3U1HCkuDeG3JT7lRJbiSugTUT40gT+Smb8A0BJACwCziWgVgCYAZhJRAwDdAIzSj58H4HUiOgvAOgBNhWyb6MeiYObhzNyNmbvVret3k2AlYtIV1QeXHw6UpGF4cRNP/bgIl709FTP/3pHqqrhixuod4BQ7mrjx3iEAIwAsZObnAYCZ5zJzPWYuYuYiaKaaLsy8kZlbCMc/A3AjM38FYAyAvkRUU5/A7asfC54M9t5ZvW0f/ly+LdXVSCpvT1qJNdv3p7oaaUNxaQgTlyrtFgBWbNkLANi5vzjFNXFmwqLNOPeNP/C/P1entB5uNP3jAAwGcDIRzdL/DfRaEDNvBzAMwDT932P6seCpXAe44U/9S2bplic+8wsuerP87X5l9MNed3DctvcQHvtuAYa8M9VjeYyvZ63DwXKgrQJafU9+9hd8+ddax7TPjFmEwSOmYsbq8qHdKjTW7NAUl2Wb96a0Hm68dyYxMzHzkczcWf/3vSlNETNvlZx7OTN/Jnx/m5kP1/+9E8wlSCDyvxN9OYWZ03rijXWTG3nshA+WalNDB4q9Ce9Jy7bitlGz8NSPi1yfs2zzHrz681KEQoy9h5LrZRRiYMXWfbjrk9mOaVds2QcA2LEv/bXbRJO5Y/rEkZlhGKJIbrP4ZfFmDHrtd5SFklvuqGlrcOQjP6VciwiaA8Wa8C3M89ZUdx/QzvOyGOwf/zcZz/60BP/6Zh46PDzGUvDHa5PdX1yKx75d4LkjM5i4TNOvlMCL4FWZSCWc4ieXBUI/udz+8SzMXrMTuw4kV+sev1BbErF8i3uhX3TfaDw7ZnGiqhSFXzm5XxeM+bnemqqfF8swBX3913oAwB6LkVO8U0YjJq7E279r/2Lydjj3YEkZiktVMMDyiNEtpXrKMQuEfvAaQElZCKe/MlHqKpYqfcOrrdzg1QnLgq2IA17raYyYkvqeONQx3roU6wvVSssiObkdPSR7BKkIEFPj37m/GFNWJN8pIwuEfvBs2n0Q89btxv1fzLVMs33fIWxPgc011VqEE4s27sGhUu9mDWNFbzKxupduBfT2ffYvtZ+O2m/nnmpGz9mA9/5YZfn7mu37UZKCZ5xKhrwzDRcMn5z0kVsGC/3ES791Ow+g6L7R2CyxG5/y/G/oMmxswutgEJEF7q472b7CYnFLN7k3QRmnlZT5q+/3cze6L0svIkeXrPHeoX/835+4YHisp5Xs1rstS7RdJ+sZLli/G6PnbIgrj5s+nImHv5kv/W3LnkM44ekJ+Pf37ifdDdJdybFj4XotrEcoyReRuUK/Sn3t7wl3Bp61+RktcRBiJWUhhIRh+QeTV+OJ0QsCrZNXDTDZL4vfySujnvGYNbyOLCI7ZsrLdFOTb2avd5xULy9K+8CXJ+KmDxMXqXWt7so4fXUcHtzl5WZCaD8pqnPmCv2Cylpc/K6XB5IdM2Pr3kOezxs8YgpaPfADLns74mf+4Ffz8ObE2Em8IHArzMuPgqTVtDQOoe/Vs8PpHrq5x7d+9Jf1+ZK7ny4a69gFm1B03+iklmk4PVSvmJ+Q/If/tjzcsaQSu1a4fMte/L0tOXXMXKEfMLPW7ES3x8e5WjwjMnGp5l43aVnMMoZAMQTbW5NW4r7P5wSad0lZyLW2PPPvHZiwaHPM8fiFWuKk4k/zN+Kvv3eEhbGT2cQstKet2o6Lhk+2tEmb8zP6r5wc0Sjn0iyX4O76i5ne2ncQGKa7Ag8eWsyM+7+Yg7nr7Dc82rjrIJ78fhGueGea7/pNWbENRfeNTuiq8D7P/Ypez0xIWP4iWSH0X5uwDK0f/MEx3cgpqzHotd+lvy3euAcA8Puy2Im5VPvdisxYvQOjpq1xTOfFHtzvxd/Q5sEfXaU95/U/cMW70S/Yyq37fIVU/m7Oepz7hrayWqboHywpw8vjlzpOhDk9n2vfn4GzX/8j9jwGZqzeHuNPb751d3w8C3+u2IaNu+RrAoLU4sW87LLdvOegrwVmqZgoNtqil7K37yvGR1PXODpLGM/ey8LFshDjjFcmYdyCTQCAT2doHeGfCfK0MbePRC8MzAqh/8yYxa5myB/4ch5mr9kp/c3QyoJ8gX9fthWzLMrzimebvoe0xgrQh76a560Qnd7P+gsd8eZvK8KfZZNdb/yyHM+PXYKPpv4d85uIa5NXOFSEdjM37T6Ic9/4E3d/5rxK1q4c82HpRK5NHZdu2oOlm/ZY/i7zbOr+xHgM9LHhSyoWOUUu3X3ZeTnRosvqTGNS3ot1cO/BUsxdtwt3fDJLq1+CdLqw375wB/5cvg0dHh6T0MihWSH0g8CpOW7ecxBb9hzCjv3uNYpL3pqCsyxGFl5xEvrb9xVHTYb6acjvT16N3yVmqpKyEF4at9T3ClMRI7TAzv3FOCR01LL67tM1Ij8uoDKM8gzNc4+e/wLTFonmuoQnfi26UnOHFQ5JYfPMdu4vxs+LNE3z1Bd+w6kv/KafG824BZtw+AM/YMH62A1e/vZgjli1dR+K7hsd32SqT/zEZcqxkVzMjBfHLcGm3QfDeYa8SH1TPSIhROLDaEfm6xWrNn2Vdv8ve3sqfpgbn8eUFUroe8Tqxe7+xHgc/cS4pNbllfFL8bSL2DI79xejy7CxrtI6setACcpCjEe+mR+2cX4yfQ1eGLcEL/+81FUeVh3Oj/M24KhhYzFt1XZ0fmwsFm3cI5wjmfzU/5q10z+Xb4tyC/WrqVl770QfN8o3kptHlVbl22nV1/5vBq58d3pM9EixTszAuIVaxzB77U7LvNzw+3KtM9+027uzQvx4F6p2j3T++t14cdxS3PLRX+GEZX4aQZwa/q4DJVFmpQ+nyEek4jMV53ke+y5YD79wGQnJNQMhGzVk8AhvESCD4rmxS/D6L8sB2AsQY/QxZn7EZ93NPEQoxDGukvm5OZizdife/WMVbhuleagYGr6fiJaHSsvwzez1YGZMXqFpOXPXxk7O7T5Yivcny0PSmh/NRW9OxkvjIx2Q3zkXw2riVcN78vuFUd/f+GU55okTjjbVMd5/I5yG3/UJ5Qk/mr4bGT515fawFu1J07eAiMDMePO3Fdi85yCmrNgWHm0CwITFm3Hlu9PCQrzToz/hyEd+kuRkHvklFyX0XWI8yFCIsXbHgRTXJpqvZ62zDbMr1ZKFQweKy2LMA5t2H8RhQ79Hy6FRAVVRkJcTfpGCaKzDvluAWz/6C9OF+lvla55T8Gqrd4uR3OjwckzSiFnbvMNqhemijdH38oVxS3D6K5Ni8hezFev45PcLsU03c5kFod38wOg5G/DjPPeL0QDN2+qtiSs82fKLS0Moum80Xhy3xFNZVliN2FydJCEvN5KPEXAvqOgVizbuwRPfL8RFwyfjguGTcadu9weAK96Zhp8Xbfbe3oT0Vm0iSJTQ98hXs9anXTz720bNwkYX0SSJCC+OW4L7v5gb9kbSzv8LA1+eGBVg7Kr35C5umludfDjuZxLwr793hvP1Ohlt2MqNUdj2fcXSeQXP747RqVl4lTA07d1YYRqx6RvnOWTP0ffvYEkZNuyKKBLDhQlstxC0Va/XfzDD03nnvP4HHh+90DkhgJs/nImPp/0d3lFrxKT41prs2l+CsQs2RWn6381Zj5FTYkd0vy7ZgoEvTQy7xW43mb3EkXieYCIxNHFPi/tMCtFqwX/eiJdkmMFEE2TERu+urIhZUDDvCNfh5p32Q15Ccs1AgtiOjZnxoE8PmLjKFT6/OE4ze4geL8Yo4WBJCFUraMvi562LnRgEgII8ivFyMRC/nvjMBLSqVxVvDekmqU+kRsbL+NzYJWhZt7LztTCjNMRRUTeNYrsMGyvNQ3ypDpaUodvj4/Ds+Z3Qv0MD27Ks7MDmkROZjju98mZzxlXvTZO6AgPR+wjsPVRqWvTFkk/R/LF8K3YfKHW8Vjd8N2cDvpuzAV2b1wSgPbt563ahWoV8NKtdyXN+130wHZNXbMcjZxwBQLsfN3+oXd8lPZpHpb3709nYvOcQtu0tRoPqFdD72V8s8xU9e8LmHf2mX/3edCzetBsT7znZ8vyQ8Bxv+nBm+P0g8TdTxx19vmXW+rnW33O8602eySpN32pibu2O/fhj+VbbdB9NdfZ9d+JgSQgjLSZzEsE3s9dHhVq2ak+G8DaE8e0f/2WRUnuhIsNxDdltXb1tf3iS0Q6jbLcuamt3HECrB37Ap9PXSDXx5bp7qYhYvQ27NP91N5PaRoe0ZNPesFeFOb/i0lD4GkIMLNywG1NXuvOAWbFlH2as3h4l8M2jCnHdyLx1u/DL4uj75DQ6uvjNKa60fy+jrHs/1wIN7i8uw+mvTEKvZyZg2HcLpDGo7DBcgY3V1lYjxRmrt2PzHk2zFk03VojXYghp4++4hZuwZru1eXbB+t3htHsPleJnYaEhUeR5GG1g1bb9OPt17VjEPdTb2FJMnQyX2SwT+vLjvZ6egIvfnOKYLl5GTPI+bLfCzeKqWz/6C32e+zX8fcXWWIEIiLFmtL92C6mIJJpKQC5tbli1TbuGL2aui9TJ4RxmYPaanVFeNW4esfjy/iHsSyxef2koFJVe5tIaUx/976hpa8KLz2R5A4hafCSbjPTllCK4NPrJR7bmZcSklej+5HjXeWzafRBb9LAmYZOGxYP8etb68OdQiPGqg5fY9R9E4gT9MG+DnrVz6xy7YBMGvjwxqm1ZId4vw0RplODavCOJ8WQ2WyWCrBL6ExZvli6nNr9LiYp69+xPwUx8Ad5eUqe0OeQunUFIomHLvrtBPslsXZEndPvzgZIyYULUvuDlW/Zi0Gu/x3jVOCGueYqKCyNOvIGwUu9Ml9gsoBJJRPNyuvXv/7kqfF/nrtuFF8ctjTIViZ2XE07vhzg/YUWPJ8eH74MR08rqGsTyRvy+0vE9WiisqwiP0C0y33uoFB0fGYOJS7eER8XjFzmPUM2IcwZeJ43F5G/o3niJJKuE/mf6cuq/HFbBmp9Z/xd/S0yF4iBIuWFoQRF7pXXaGat34M6PZ4XPa/PgD3hSD4k7R+JqKcOP37qBMXE2a83OcNwVp85mpm6TdYrTYkYUNhXzc8OfOcqWHvl884d/YbeLcBNBhe2wuo8y19mHvp6PCYs1U4UhoA4KGnupB9dQu87tz+XbcMy/f8a3s9dbpjETdju2eJDide61uL9OLcfq90UbdmPPwVIMHjEV83UPNsN1OCYP0bPG9AwvenNy2EwVYnZlrjSyS/Zex1kl9A2cTCN/Lo+eVBNn6NMFb6MR+7Rm7xM7Hv12AdbrMWaIELVqdopLW3ZQmIfVVrj1TjGIeOFE7shEC7ON+TG8PN55gZpfTf/it6Y4J4IWTkSGEc1SVrwXjyG7tQNGNMufJUH3nLDW9COfv4szrr9d3k4dVfReBtG/ifM4oRBHuXKaMZ9rrLZOFhkv9Hd5CItgIIZBTiWhEOOhr+ZJPYfW7wxurUB40xCP0siNOccppnwgpjQiy/1sRZjZ1byDcR9+WxoR9KJAEKvspf7G6tpEb5rxuUWkzEMl0SYcMc5UUO6BxkSrcQ8XbtiNjg+PcXWudXuK3K+g9572u0/DIZtYXrIsxUVcH09fE8hiMb9kvMtmp8ciK+LEd23G6h04940/MHRgW9vzk7UnqTGpVr9ahfCxFVv34v3JqzFv/S7UqlSA87o2Cf/mpfF7DzjmLr0bc8wpz/9q+dutH/3luAGNu3oA97oIJy0+SjcdnNVuUeKZXppH58fGYtV/TgvMpu81G2Nv3n0JjOJ4x8dacDpjMDDAQ9C36NhQHPEqC+B+HSoN4fmxsXMBidh9TOvUo9+N9qaO7+zXf8e+AGJV+SGjNX2rEKXMEc3tSYct2g5/4Hvb34Oix5Pj0cPk/WAMo/cfKsP4RZtxw8iIV4KdpgHAk6eK4dZstH+374HfMLxG9t+YhtPGohxjLYFbdu4vdrUtotvX2+lljLLX+vGeScDCezcb/Bhtwm6DFzOvTVjmqz6HSsrQ9wV5h/+9RSCxHYLnihEefN66Xa5ChbtpizLT25oEbK4SYnasz2yX81+JIKM1fbOW/qMee4bh/FAMEjwSj+Gez2ZjxuodWL/zIN654mgAwGLJxJlTnBtR83W6BkNj332wxNOuSUHHXjc6Mq/xxF17RQX0MG/XJ7IB76aaG0fOwKSlwW+o4+YeHCoN4aRnJniKBPvMmMW+6rNu5wHLUZzVAkVxvcL7f65G//YNXIeV2LG/BHsPlaJKoTeRZqw5cIPb9u5n74hkktFC32roNnrORlcLh5LFlj0RLe2T6RF77DwbbxOzfdaMOJF240h3S/O9xhRKRez1eFi38wDW6+6EQfXlXoW+l43ag6a4NIRVSdqSz87sJXpCAUCdKoUxI5UFG3bjqGFjXZd360d/oUalfMz6V19P9UwEQ9JkTtCKjBb6Vg3PjcBfuXUfJidopxwzfkIyO5l3xI01ZKtURSJLv4OfyE0ntu4tDi/CW71tPz6e9jd6HlY7rjxTOB+H9/9cbblFo4xiD2njxW6iskJ+tFW5Vb0qvvafNrNzf4mrNQKAtgfE6m3274Vf1u44gLpVC8Pfh37pfjSRDDJc6Pt/I894ZVLCty1zwq76TvHBvWwkbkyYufExD4JETJ75wcvQ3opUbpXpdfs+N7vHBYXVu3ewpAwVTJp+kBu3uJ2DuPvT2fhqlvu1BF4R9SGrOPqpwnEil4iaEtEEIlpARPOJ6DbT73cRERNRHf37JUQ0h4jmEtEfRNRJSNufiBYT0TIiui/4y4kmHqGfaoEPaLFNrHBy+fIi9I0VpQ941EicVsJakR4iPxg2p2TTEX8kU+hbKSV9nvs1KgomEOyeAW5f+UQK/HTHjaZfCuAuZp5JRFUBzCCiscy8gIiaAugLQOzKVgI4kZl3ENEAAMMB9CCiXACvATgVwFoA04joG2ZOzPYwSP4kbNC8YBOv3MmV1I+rqdeXL99nSMCXxi3FrwncAzSZnPHqJOdEaUIyhb7Vu7du5wHUEUwfqUIWQ8r5nHJmz7TAUdNn5g3MPFP/vAfAQgCN9Z9fAHAPBOWNmf9gZmM10WQAhnN5dwDLmHkFMxcDGAVgUCBXYUGiF8GkknctNvBIJgd87JQFIGMEPlC+FItkvg92SseGABcWeilXJNHiO537B09++kRUBOAoAFOIaBCAdcw82+aUqwD8oH9uDEB0uF2LSOeREFI5yZZovMaRSQR/LE/ORLciGJL5OtgJ3817EmcSc+PTD/iTDWksxz3heiKXiKoA+BzA7dBMPkOhmXas0veGJvSP91IhIroWwLUA0KxZMy+nxrAtAI+AbMAY6lbMz/WtvSvSn2SOStJlsl4RiytNn4jyoQn8kcz8BYCWAFoAmE1Eq6CZcGYSUQM9/ZEA3gIwiJkNdXAdgKZCtk30Y1Ew83Bm7sbM3erWrevvqnTERTQKa4z386Q28d1vRXqTTE+jTBxlezHZpPMaFkdNn7TZixEAFjLz8wDAzHMB1BPSrALQjZm3ElEzAF8AGMzM4kzkNACtiKgFNGF/IYCLg7oQGX6CrWUzP3jcVFtRzkiiIM7k+bTyjhvzznEABgOYS0Sz9GNDmdkqKM2/ANQG8Lo+212qa+6lRHQzgDEAcgG8zczz46q9A+k8maJQJJsv/nLeESooMlHTzxQchT4zT4LDHAYzFwmfrwZwtUW67wEkJ4KZQqFIGZmo6XuJbJvOCmdGR9lUKBSpIVkhyZOJ1eY05Y0MF/pp3N0qFBlMKjcJUdiT4UJfoVCkgj1pEMYklWzbm9x9b72ghL5CoVAETDIjmnpFCX2FQqHIIpTQVygUiiwio4V+OrtNKRQKRSrIaKGvUCgUimgyWugX5Gb05SkUChtqVspPdRXSkoyWihd1b+qcSJEw+rSt55xIoUgQ7RtVT3UV0pKMFvp5StNPCdUqaNE91JyKIpW8cEHnVFchLcloqZiB4T/KBZGFOf6l/sU94ttLIV4a16iY0vIzjZ/u6JV0JaBqBdfbhcTgcydQXxxWp3LyCkOmC/002oK7Sc2KePiMI1JdjaQQ6Wz93f96VQtxzQmHBVYfPxTkeXs1CvNy8P2tJySoNuWfHAKObJxcc0s8nUxuEqX+/w3uKj1+ftcm0uPxktlCP4Uyv3PTGjHHssXacUG3+OZScsqhXah25QJUyE+f18lKkKQOwv+u7IFW9aokrcTcONpRMtug1YbriapD+rTSBJDKLdsOFMduO5iTzDFjCunQuFpc53u5TZUKcuMqywqv7xsRpVVnlT410SACqlfKx1HNaiStzHieh59zm9T0ZxK0Kqo0QUHrMlroL9281/M5Bbk5ePvybnGXfdZRsXu+p9uLmCjy45xAt9J8ZNx5auu4ygqSNJL5nu5hMkhFqOVkm3faNfSn7Fh1MIdKE7NfdUYL/a9nrfd8jjbhFP8Lc/2J0TZpovR7ERNFvEI/x8PpybS92kGU3vuipprSslQIff/PY0CHBgHWxB6rWh4sUUI/KVSvmB/IMJ2IorwHmNNLEwyaw+pGPBDyw5Og/i44aOHpp1/w2ga0Tt17OdlCaUiLOllePOqqVkjOwq6XLuxs2dYOliQmUqcS+iZyiAJz1xp/14kxeZcXruuljVTc3gvR7SwvfJK/NzwvN7bQB09r5ysvwJ/G5/WMHKK0EvrmqiTbLdBMouzT6YTXDi0/lzCoc2PLdqM0/SRBOfEL5/+c0xEAUK9qhajj6WCJ+OS6Y1yla1hdq7v5XtSuXIAXL+iMP+47Oeo4EaHvEfX1c+Kr4wv/6BwjtI5oJLeXuinKT3U8T+Qivcx3YlXO7dIk5U4Ehnkn80W/d4xnRYQo76Y2DaompDz/qxcylCA0tgu7yxcWpYPNt2K+N28XTehHXtXcHJJOUucS4eWLjsLeQ6WYunJ7XHVsVKMi9rnceelIiWusmWoV87F9n7edjLw+K6J0eLoRxDZcrWJeXO6L8XJKu/ro1NTeR79e1UJs3nMoSTVKFM5d2jUntMA/+7VBmwd/DB8zFCtxpPDTHb3Qun5ihH5Gavq7D5bgtlF/Wf5+WseGGG7hx5xDiTPDpIMi6LkOLtPn5GgLmmpVLhDKiD75yuNaeCxcrIa8Il2a1Yz6Xlniwtm0ViXf5bqFkF7mO/F+3d2vDSoXJsa11Q1vDemGwjytfCsTyJjbewVSVqIWNMnw65psPBvjXsjaTSLnPjJS6JeVsaXnzpmdGuG1S7qgb/sGuOr4WCFESJy/dSeJVprsSIDipX12/TGYeE9vjLy6h3QxGeDeNCKaNo4uqoWK+bm44aRoDya3i5dkK6mb1XYnuGtWLog5VtHHoik/nWMqZP5pHRtKjxseVIV5OahUkIdqFdM74qTsuXnlnC6N8dS5RwZQG3fceNLhns9hjsx5XdqzOYDodpOMNpSRQj/fZgl9r9Z1w59l95fI3ib95Y3H+qrTfy/tmrDhmhdEDbBT0xpoWqsSjju8juWiGbsO8Le7e4c/i+aDWpULsHBYf3RtXisqvSHKrdwsxcOVCyOWx+oV813HwpHVt1U97/d9v2RxnVO5qfBMeeb8aCH37c3H48mzO6JlveiJ2/O7xrdKOp44NjKGndUh0PwAoFqFfN9zF0UmpeJsiQnTCbvnf3PvSAeRk0NY8viAcFiWZCsLmSn0Jd4fBuc5DP80m771+bV8aiQdLOKOJFtORGkV4nFTF9ivQwNUKsjFpT2tA5+J2rebdy0ynJX/LnYGdasW4vVLugDwtupW9uge8OH543UEps18JF/qmzu5WlUKcHGPZuHnafwcr9+51d4U3Yu0jr2Gy/tl3KMg5xjycghXHa/Zyv1ivo8dmzjHCfJyBZf0bIa6VQvDgQQL8nLCckZ895KhOGSm0He5ukfW7nLI34Kfr286zlP6fu01T5eQ4MpWuSAXD51uHZTN8KiJh+ihpPV1NqxeEQse64+2DdzZLb2YxNzGGjFMTuYXwW5zHFk9KnicvB4xpBvuHdDW0zlEwb+wZu3Tqly742bh7xer82tX0ZQgt9deTfd/D3KOoWOT6njo9CNQpdD9aCQmNlEAfZDdLWhYvSKmPXAKDqsbG3so2Y5VGSn043FP8+unX+TRD9q8+KNf+/oYd9eJ0nkGA6dGfVbnRp7qEKXpC19eurBz+HPX5tETpVZ4uedi0ncuP1paB/G7WYPubBO/xciiQTX/HWSfdvVRucC7OcP80ucJF3rMYbXDn4cNah/+bHT+Mr688TjLuRYDp8424g6YWOcEc5yrRcP6S9Pf078N7h/Q1nIuwg92o4bxd50oVRLMptZkuPVaIT5DZdNPAWTjvTNsUHtLjcatzHvo9COkERCPLqqFhtXt7dZite48tTVuPKml6XfnSrjRyAZ1jtgzi+pUxintnHfAcmXekQzte7etFzbf5ESkPABtnUOftvXw6sVdovKxM70E/dLUqeLOnJeXkxMj+MRFZqKJavAxReHPdmuWiJzNf1aX6+U2NKhWAQ8MdDKByXM0e6IYWI2WKxXk4boTW0ZtcHTakd46AGPhoIFdGOyWdaugZuXY9pJnqt+Jrf3s8kY23zzkkm6aPhE1JaIJRLSAiOYT0W2m3+8iIiaiOvp3IqKXiWgZEc0hoi5C2iFEtFT/NyT4y/FGK8nEKtn46dt5QLg1b1x1fAv0a98ALfSRQT0HjbSRxKRTIT8Ht/ZphSHHFrkqE5CbRMQq97ER7OK1WQkpV9dv4aIW0iWG+XhuDmHE5Ufj6KLoCWE7/Gi0VQrz8Pw/Opny0f42sDCpmTvcCvk5YcFXu3IBzuvaBHf3i5iIrKoliwRruJ0SYm1Gx7asHfXd8r5T1B9bLu7RDNeYBKlrLArwYu57zdSpy5j9cF9U198/83tr9vx57vxOGHtHxAVUVhfzqu+hA9vi9/tOxuT7++Dja3tG/XbFcUWO9QP8j6Zk5yVyfsiNpl8K4C5mPgJATwA3EdERgNYhAOgL4G8h/QAArfR/1wJ4Q09bC8DDAHoA6A7gYSJyZztIEFY+vVYNNmSjJltpNu9ecbQ0auf1J7bEB1f1QK9WdSVnRXhSX90rYmhX9aoW4tY+rXCdHtzNTZMTL0FsbMe2rGN5zoOnHYGWemwdq3DVXsw75tt7aQ/Nde2MTprGV8mDbTamHj7euxwCzukS3RasNFiDI00TfUSE6voI5B9HN8Wz53dC7ShhZNWmor/XqVIYOUPydsaYJUzZ5oSFvWHLjyQ4s5Pc/Ce7xsuPLUJHwflAvK8FeTkYMaQbxtzeC01ravMOj5zZHqceUV+a3i1VbZ579Yr54dGSqL80rVURt57cKirtuV2bRHUMsqrkmeb98nJz0LhGRTSoXgE9DovuWB8+oz0+vKZHTB5W5kiviPcqGUv8HN8uZt4AYIP+eQ8RLQTQGMACAC8AuAfA18IpgwD8jzXpMJmIahBRQwAnARjLzNsBgIjGAugP4KPgLscbXjcvCIW8e9uc1EauQefmEI5vVQc1KuXj7d9Xok+7yAuz+PH+uPLdafh92TbbvIkId57aGqOm6n2uXXuxsI+7oVntSnjvyu44/qkJKLOQgq46HP1vTg7hzcu6YdeBEgCad829A9qCANx5ahvHuQtzeIvoeujC2sN1fnJ9bGgKpxe4c9OaKMzLwSnt6mP03A0AtEnKuY/09TQfYFYkerWugzHzNmp1QGx7O/3Ihti0+yCuO7El1u04ENWG/9m3dXguQ1Z/q1GL7F49cqY271B03+iY35rXqhRur3ee2hqdm9ZAv/b1cW7XJuH0frTegR0b4uPpayx/N+5Vri6wG9eoiIn3nGyZ3kBWFzsPP7/IOrq6VQtR6LALm1i/ZHiAeVKpiKgIwFEAphDRIADrmHm26aY2BiA+ubX6MavjaYeVlmKn6fvt5Ts0ro5V/zkt6lhhXm64YYslGp2RVWxymZZw+bFFKA2FMH3VDizauMdfJRHRjCzLdmPdMV5aoiitkIjCL2HdqoXScwGtM/xk+lqc37UJ3p+82rYebuYuHj2zPY5pWdt2/YRVPjUq5WPx4wMwfdV2jJ67IXxtVtEZ7x/YFuMWboo5br6f5mdoLr8wLxdvXKrNCZkneW8WNF4y/dXykl+Mcfi0jg3DHZgdYi4FeTnon6QwxMatalyjInq3qYubentfHGWQF2f4byDWWcCsLM586FRXLt5p671DRFUAfA7gdmgmn6EA/hV0hYjoWiKaTkTTt2zZEnT2busgPW4nSIIelvVqpZlbmgkhBE7QjxWXyUOuitUeMaQbburdEkMHtsPjZ3UMX5Nft0JjNBzy0OGYMart1/ZZmJeLwT2b27pghq8TwJBjmtsGmDuxdV3HBXOOE6kuLuXMTo3Qsm4VnNe1Cd68LNrUZ34eRPaan9VIK7ZesRWTPbperevismM085rotWVg+Pf/s2/EBz7IHelaCF5vuw+W2KY1yi3IzcE7V3RHN5dzPbJnZJ7IdcxD0r6rm+b4YtZMuFzTI+adFuYdACCifGgCfyQzf0FEHQG0AGBo+U0AzCSi7gDWARCX/zXRj62DZuIRj/9iLouZhwMYDgDdunVLSVA+q/Zg98IFPQN/1fEtcGbnRlGmjLv7tcWbE1da10H43Kdd/SiTUbzVC2v6VuYdiwI+v+FYfDZjDT6auiYswBKp2YhZPzrIftWn3WSjMUdjpRCaT5XdFXOaZ8/vFJPGbvRo/qVN/apobxFtNKZsSR1kRf3vyu7hzzLt9+WLjsLaHQfQok5ldGleA6c8/5ur8gHgqXM7orjUOib8j7efEKUtt2tYDT/M24g6VQqxdW9s8DWj/l7fNTH9AwPbSUN3+yGm/fjMVsymd9t6WLxpj+9FoG5wFPqkSfURABYy8/MAwMxzAdQT0qwC0I2ZtxLRNwBuJqJR0CZtdzHzBiIaA+BJYfK2L4D7A72agPAykfvhNT0wZt7GwPtnIoqxXdu5pmnn+PvNDYYQDFm8w1bZd21eE20aVEV+bg4u7dkc7/y+KqGByYwRiRtlVFYNY2TVpn5VXNvrMAzu2RwnPD0h9lypAcU75jbFbDbJRD6/dFFnz7uSebUXf3b9MdixP6Jx5+fmRGnjXrjgaOvV3ABiFv7deFJLnHZkQzz01Typ0I/Y9P3f8zYNqqJX67q2nZEM2b0LSOZHvQ9392uDK48rsp23ihc3mv5xAAYDmEtEs/RjQ5n5e4v03wMYCGAZgP0ArgAAZt5ORMMATNPTPWZM6qYbVo1KNjw+tmUdHNuyTtL2AH38rA42wdGcmx2z5iL3gcQmPrhncxzdQj5kNu6JW/OCSJXCPDw2qAPW7tgflVciiNyD6Hr+s29rrNq2H5/NWBs+ZvY4Wvx4//ALmJNDGGryXR86sC2e/H6RVo5+quFR4ncxmCHEG9eoiHU7D8QIFze3+4/7TnYVOtpNXm5MJolq6Xm5OWgpWbFqYLxj8bQfw+sqiCYY6znl12Uz8jk3hxzduOPFjffOJDh0YsxcJHxmADdZpHsbwNveqph4uhfVwmXHNsfaHQcAWGvFzZIQotcJIzKfHSe2jnUDFVe3nnZkQ+mCGLsgWIYNNJ7OrWYlbcjq5hqc+PyGY8LhekWsJnJvPrkVFm7YHS30Tc9Zlp/Itb1ahoW+QbuG1fDCBZ2iTGmRuhhuk9Z5PnNeJ4yYtAKt6lfFPZ/NARj46Nqe+HT6GlQtzIsSsFZCu1GNimhkEZAu2EFVuBElFMPM1LZBVanzgVfh+tQ5R+Lit6agWoU81NDbYBArlM15+HfZTO5MbsZuovLIGUfgkW8XANBidfd7UW6L/OuhU1GxIDdqclD2EL69+Xh0bFIdK7fuk+aT5Al4Sybe01vqARPvBJGTduXmJapcmBfjqeQXcwRPA0NzdiOX4nnZxDPPPkq+3qN/+wa4pEcz3HFqa8t8GlSvgAdOOwKf650RQ/PosgrQ5xbZ9cc7AZss2WQU0/Ow2lFCPyc8z+KtIobPvmgeDULTN+fhW9OPvyqeyNgwDJcLG3bYbTtWs3JBjDeI7OEZUfesXpxkL6W2ommtSlLvlqa1NE3Q685ZBqnceckLhk+07DmZL8EucJuZPm2j11u46eQK8nLwxNkdoxZcGZhHY1YxbPwKauO8qPkBXzklH+NeGN5qBkYb9NoW5fb4+NuzWU74zTLZr1bGavpmerepG7XC0A4/WkA67Y8q4+nzOuH0IxtJQ0+4IdV7rLrF6PDcCDiniXED2egk3rsxYki3qM3C3a1z8F6O2C7tPIW8kKzOI8ZmnmPMtySpAgKiItW2QVVtQ3NTGv82fWXeSQjvXNHdOZGO+SF8JlmxWd6oUpiHgXFGNrziuCIM6CDPI136vIimH/ub2cTlVujLiPd683JzIE4hWJnfjOvo3qIW2nrYKFtu3vFQQQnJesThqXgGfr37JGzTJ6kNvcOrcA3C9/0owXniR31rx827D0alMfvtpysZa96JB7NSK26oUF6GyIng4TPao7uFd0+6bAse1vQlEq5VvSpR+/R6XaAjErR2NqBjA1zUvRketNhP4bFB7T2NtsI+7eKxOOoXnXdi3wLx3javXTm8D7Jh1glqxOK3TgZGLepUKcBDpx8RtYCtXUN/++cmAyX0JZg1CXNwJgM/W6plGg/62JUqaN654mh8c/Nx6NO2Hm7qrUW/lImFnBzCv86ICNV0MskV5uXi3+fE2v/9xmKpVbkA3Ytq4UVhlW3cmn6S75e5vjkOa0X8MKBDA8sNkF6+6CjbFd3hepG2c1dFIXz2qGt62pyRWrLGvOMFQ+gX5OVg0j29pd4Ch9WpjBcu6JyU+jx7fies2LI3KWWVR3rrQe1GXH50OIibHd2a18T01TsSXa1AiGjs3gRubg5JAsmVL5u+mY6Nq2PtjgOo4GOjexlOnmRWUUndUN3jdpvJRAl9CUZI2zzJQgljiFnJYrs3Lx4hbnHa1zeV+F0an3BsJNMHV/fAnoOlvrLt174+xsyPDZyWKIzLCOL+JsOmP+b2Xli9Te7W7BYj+J55gP3cPzrh8mOLEr54yS0psDIFQkYL/QcGtnMdq0TEznbYvHYl3DegrVQLePysDuh5mPsNP4ImFRtzG2Wmi8yPLEKzpkJ+rud9cw1euaiLq9FEUMhcL/3nFUAmDrRpUNXWRdoNj5/VEU1rVorZa6JSQV5MrHs3JPq9SDuFx4GMFvp+dwMyzDtSDxAiXH9iy9gfEMxKUz+kQ6NLhzoAoudHYl70grwc2/DPiSKI+zugYwPbePVuSXTnUbdqoeWEdjpRpYImPkWPtnF39kJFH/sr33BSy5h1CYkio4W+X9xoi4r0RHNNbYDLhH1oyzNBtsGT2tTD+1d1x8qt+/Cvr+d7Pl8M5VGeqFqo2dfP79bUIaU3qhTmYeZDp0a5ah5ez98o597+bZ0TBYQS+naUr7adEtLNrklEeP2S2I3nyy3h+xvMUOqEVnVxQqu6/oR+2hjxvFGxIBeLH+8f93zbpHt7x0Q5TWQI5EShXDYlhM07Suo7EploLJ8CId25Qd+AvaHFVocKdxTm5cbdRpvUrIT6aTKJHA9K05eQn0uoWpiH+wem3gc93ZEtAlIEx/ndmgZullBkN0roSyAizH20X6qrUb5QUj/jMRTl/FQEv1EEhhL6irg4t2tjfDN7fcZMnGYTlQu8ua02qVkRt5x8OM7tkr7rRhTOKKGfARhhIrxupRcE9apWwA+3nZD0chXx8eE1PVBU29s2iESEu4T4MoryiRL6GcCZnRthyeY9uKn34amuiqKccGzL5PiEK9IPJfQzgPzcHNw/QE06KxQKZ9SMjEKhUGQRSugrFApFFqGEvkKhUGQRSugrFApFFqGEvkKhUGQRSugrFApFFqGEvkKhUGQRSugrFApFFkGJ2mEoCIhoC4DVcWRRB8DWgKqTSjLlOgB1LelKplxLplwHEN+1NGfmurIf0lroxwsRTWfmbqmuR7xkynUA6lrSlUy5lky5DiBx16LMOwqFQpFFKKGvUCgUWUSmC/3hqa5AQGTKdQDqWtKVTLmWTLkOIEHXktE2fYVCoVBEk+mavkKhUCgEMlLoE1F/IlpMRMuI6L5U18cvRPQ2EW0monmprku8EFFTIppARAuIaD4R3ZbqOvmFiCoQ0VQimq1fy6OprlM8EFEuEf1FRN+lui7xQESriGguEc0ioumprk88EFENIvqMiBYR0UIiOiawvDPNvENEuQCWADgVwFoA0wBcxMwLUloxHxBRLwB7AfyPmTukuj7xQEQNATRk5plEVBXADABnldPnQgAqM/NeIsoHMAnAbcw8OcVV8wUR3QmgG4BqzHx6quvjFyJaBaAbM5d7P30ieg/ARGZ+i4gKAFRi5p1B5J2Jmn53AMuYeQUzFwMYBWBQiuvkC2b+DcD2VNcjCJh5AzPP1D/vAbAQQOPU1sofrLFX/5qv/yuX2hMRNQFwGoC3Ul0XhQYRVQfQC8AIAGDm4qAEPpCZQr8xgDXC97Uop8IlUyGiIgBHAZiS4qr4RjeJzAKwGcBYZi6v1/IigHsAhFJcjyBgAD8R0QwiujbVlYmDFgC2AHhHN7u9RUTedrG3IROFviKNIaIqAD4HcDsz7051ffzCzGXM3BlAEwDdiajcmd+I6HQAm5l5RqrrEhDHM3MXAAMA3KSbR8sjeQC6AHiDmY8CsA9AYHOTmSj01wFoKnxvoh9TpBjd/v05gJHM/EWq6xME+rB7AoD+Ka6KH44DcKZuCx8F4GQi+iC1VfIPM6/T/24G8CU0U295ZC2AtcLo8TNonUAgZKLQnwagFRG10CdALgTwTYrrlPXok58jACxk5udTXZ94IKK6RFRD/1wRmtPAopRWygfMfD8zN2HmImjvyc/MfGmKq+ULIqqsOwhAN4X0BVAuvd6YeSOANUTURj/UB0BgDg95QWWULjBzKRHdDGAMgFwAbzPz/BRXyxdE9BGAkwDUIaK1AB5m5hGprZVvjgMwGMBc3RYOAEOZ+fvUVck3DQG8p3uK5QD4hJnLtbtjBlAfwJeaboE8AB8y84+prVJc3AJgpK64rgBwRVAZZ5zLpkKhUCisyUTzjkKhUCgsUEJfoVAosggl9BUKhSKLUEJfoVAosggl9BWKLMBL8D4i6kVEM4molIjOM/02hIiW6v+GeCjfMk9TugIiGk5ES/RgY+eafj+XiJiIugnH7teDKy4mon7C8duIaJ4eFO924XgnIvpTD872LRFV048XEdEBPWDbLCL6r368EhGN1uszn4j+I+RVSEQf6+VP0VebO9XLU0BIIjpfLzdkuu58InpPv46FRHS/U14AAGZW/9Q/9S/D/0GL5dIFwDwXaYsAHAngfwDOE47XguY+WAtATf1zTcm5v7jNU5LuUQCP659zANQRfqsK4DcAk6EFVgOAIwDMBlAILXzBcmiu2h2g+elXgubCOQ7A4fo50wCcqH++EsAwoY4x90fPo7f+uQDARAAD9O83Aviv/vlCAB871CtX/3yYntdsAEc4PI92ANoA+MW4bv34xQBGCXVcBaDI6fkqTV+hyAJYEryPiFoS0Y96rJqJRNRWT7uKmecgNh5PP2hxhrYz8w4AY+FyJbJNnmauBPBv/ZwQR0fMHAbgKQAHhWODoAm+Q8y8EsAyaCtx2wGYwsz7mbkUwK8AztHPaQ2t84B+DVGjCUnd9zPzBP1zMYCZ0Fb6G+W/p3/+DEAffSGiVb0sA0ISUVci+lV/HmP0yLRg5oXMvFhWNQCViSgPQEUAxQAcQ5sooa9QZC/DAdzCzF0B/BPA6w7pExrM0FjlDGCYbgr6lIjq6791AdCUmUe7rNM8ACcQUW0iqgRgICLhWeYjEnn3fESHbWmhBzn7lYhOsKjjGQDGm8vXO5ddAGrb1Et6XA9R8gq0UVBXAG8DeCLmJkXzGbS4PBsA/A3gWWZ2jMqbcStyFQqFM3rgu2MBfKqvYgU0U4Tf/L6EZsYoANBMWHX9EjO/4zKbPGga9B/MfCdpcf6f1ecOngdwudv6MPNCInoKwE/QBOMsAGX6z1cCeJmIHoIWoqVYP74BQDNm3kZEXQF8RUTtWQ8MqGvUHwF4mZlXuK2LS9pAM0mN1Z9Hrl4fO7pDu6ZG0MxtE4lonFPdlNBXKLKTHAA7WYsU6pZ10MKCGDSBZmcGM58NhMNmv8vMJ8E72wDsB2AE4/sUwFXQbPkdAPyiC8QGAL4hojNhE2CRtZAlI/R6PQlNqwYzL4IWmwdE1BrafgJg5kMADumfZxDRcmimIGMXruEAljLzi0J5Rvlr9U6hun4ddoEfZccJwHxm9rJD1sUAfmTmEgCbieh3aJvh2Ap9Zd5RKLIQXXtdSUTnA1pAPCLq5HDaGAB9iagmEdWEJjjHBFgnBvAtIh1LHwALmHkXM9dh5iLWgsNNBnAmM0+HpqlfqHvRtADQCsBU/Zrq6X+bQbPnf2g6ngPgQQCGl05d0uIpgYgO0/NaoX9/HJpAv91U7W8AGF5M50ELWsc29bIKCLkYQF3St0XUPXPaO9yyvwGcrKevDKAn3AT+C8IzQP1T/9S/9P4HzSyxAUAJNI33KmjmmB+heZAsAPAvPe3Repp90LTW+UI+V0KblFwG4ApJOUWQe+/Y5TlL+Nwc2iTrHGh282aSvH5BtBfLA9A8YhZD96rRj0/Ur2s2gD7C8dugbam6BMB/EIlBdi40e/8saJO1Z+jHm0CbNF2o/zYLwNX6bxWgjUiWQRPqh7mo10C97OUAHhCOd9avfbZej2v042fr9+4QgE0AxujHq+hlz9ev8243bUEFXFMoFIosQpl3FAqFIotQQl+hUCiyCCX0FQqFIotQQl+hUCiyCCX0FQqFIotQQl+hUCiyCCX0FQqFIotQQl+hUCiyiP8Hgjluqhdo090AAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(timestamps, V[\"646/VCM\"])\n", + "plt.plot(timestamps, V[\"646/VBM\"])\n", + "\n", + "plt.figure()\n", + "plt.plot(timestamps, V[\"646/VCA\"])\n", + "plt.plot(timestamps, V[\"646/VBA\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of streams simulated:\n", + "234\n" + ] + } + ], + "source": [ + "print(\"Number of streams simulated:\")\n", + "print(len(V.keys()) + len(I.keys()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Push data to streams" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Put voltage data into the corresponding stream\n", + "sims.add_all_data(timestamps, V, streams_dict, prefix)\n", + "# Put current data into the corresponding stream\n", + "sims.add_all_data(timestamps, I, streams_dict, prefix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add long period of data\n", + "The following cell runs a loop which will generate and push data over a much longer period. \n", + "The purpose of the loop is to avoid generating all the data at once - instead, the full time period is divided into smaller time chunks, with data generated and inserted for each chunk sequentially. \n", + "\n", + "The code is divided into two cells below - the first is the initialization step. The second runs the loop. If there is an error during the loop, the second cell can be re-run and will pick up where it left off. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collection prefix in which data will be added\n", + "prefix = \"simulated/ieee13\"\n", + "# Get the desired output streams to which data will be pushed\n", + "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)\n", + "# If the desired streams exist, retrieve them. Otherwise create them.\n", + "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)\n", + "\n", + "\n", + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Simulation time window - the FULL time range over which we want to generate data\n", + "start_time = datetime(2022, 1, 2, 0, 0, 0)\n", + "end_time = datetime(2022, 1, 3, 0, 0, 0)\n", + "fs = 30 # Hz\n", + "Ttotal = int(((end_time - start_time).total_seconds()) * fs)\n", + "print(\"We will generate\", Ttotal, \"samples.\")\n", + "\n", + "# The simulation time step - this is the amount of data we insert at once.\n", + "step = timedelta(minutes=5)\n", + "nsteps = int((end_time - start_time) / step)\n", + "\n", + "# Create progress bar\n", + "pbar = tqdm(total=nsteps, desc=\"Adding simulated data\")\n", + "\n", + "t0 = start_time" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1f55605fad2421f944ee0fc466c5752", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Adding simulated data: 0%| | 0/110 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "T = 100 # Number of time points to simulate\n", + "\n", + "# Get the original loads\n", + "load, load_names = sims.get_loads()\n", + "nloads = len(load_names)\n", + "\n", + "# Generate the randomized scaling factors\n", + "mu = 1.1\n", + "sig = 0.1\n", + "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n", + "\n", + "# Generate the new load values by scaling the original loads with randomized s\n", + "new_load = s * load[:, np.newaxis]\n", + "\n", + "# Visualize some of the load curves\n", + "plt.plot(new_load[0:5, :].T);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to iteratively run the simulation for each load setting and retrieve the resulting voltages and currents...\n", + "\n", + "The function `simulate_network` does this all for you. It has the signature `simulate_network(loads, load_names, contypes=['Line', 'Transformer'])`. The first argument is the desired load values in an $n\\times T$ matrix (as created above), where $n$ is the number of loads we want to set. The second argument is the names of the loads to set (as returned by `get_loads()` above). Finally, the optional argument `contypes` specifies which types of connections we want to return current data for. \n", + "\n", + "The function returns dictionaries of voltage and current, with the sames keys as `v2dict` and `i2dict` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running simulation: 0%| | 0/99 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "key_list = [key for key in V.keys()]\n", + "\n", + "plt.plot(V[key_list[0]])\n", + "plt.title(key_list[0]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And one of the resulting current magnitudes. " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "key_list = [key for key in I.keys()]\n", + "\n", + "plt.plot(I[key_list[0]])\n", + "plt.title(key_list[0]);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 0dbd4dcf535b56acd2a50c3d71c99c7d351511d7 Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 18:56:45 -0800 Subject: [PATCH 06/10] rerun Intro_to_Simulation_with_OpenDSS notebook --- .../Intro_to_Simulation_with_OpenDSS.ipynb | 174 +++++++++++------- 1 file changed, 109 insertions(+), 65 deletions(-) diff --git a/btrdbextras/opendss_ingest/notebooks/Intro_to_Simulation_with_OpenDSS.ipynb b/btrdbextras/opendss_ingest/notebooks/Intro_to_Simulation_with_OpenDSS.ipynb index 84daa3a..1a69a9b 100644 --- a/btrdbextras/opendss_ingest/notebooks/Intro_to_Simulation_with_OpenDSS.ipynb +++ b/btrdbextras/opendss_ingest/notebooks/Intro_to_Simulation_with_OpenDSS.ipynb @@ -13,15 +13,20 @@ }, { "cell_type": "code", - "execution_count": 66, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.888588Z", + "start_time": "2023-11-22T02:55:24.917889Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import opendssdirect as dss\n", "import matplotlib.pyplot as plt\n", - "import simulation_utils as sims\n", + "import btrdbextras.opendss_ingest.simulation_utils as sims\n", "\n", "%matplotlib inline\n", "\n", @@ -44,20 +49,30 @@ }, { "cell_type": "code", - "execution_count": 67, - "metadata": {}, + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.893660Z", + "start_time": "2023-11-22T02:55:25.889173Z" + } + }, "outputs": [], "source": [ "# The location of the .dss file specifying the model.\n", - "model_loc = \"./Models/13Bus/IEEE13Nodeckt.dss\"\n", + "model_loc = \"../Models/13Bus/IEEE13Nodeckt.dss\"\n", "# Activate the model in OpenDSS\n", "dss.run_command(\"Redirect \" + model_loc);" ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": {}, + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.898240Z", + "start_time": "2023-11-22T02:55:25.894304Z" + } + }, "outputs": [ { "name": "stdout", @@ -89,8 +104,13 @@ }, { "cell_type": "code", - "execution_count": 69, - "metadata": {}, + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.903962Z", + "start_time": "2023-11-22T02:55:25.897811Z" + } + }, "outputs": [ { "name": "stdout", @@ -114,8 +134,13 @@ }, { "cell_type": "code", - "execution_count": 70, - "metadata": {}, + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.905799Z", + "start_time": "2023-11-22T02:55:25.902491Z" + } + }, "outputs": [ { "name": "stdout", @@ -146,8 +171,13 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:25.909931Z", + "start_time": "2023-11-22T02:55:25.906842Z" + } + }, "outputs": [], "source": [ "# Run / solve powerflow\n", @@ -163,14 +193,19 @@ }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.020507Z", + "start_time": "2023-11-22T02:55:25.910818Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Voltages at 633 [2426.425943474452, -109.95862245792392, -1300.0214661176276, -2096.2770897808787, -1120.4367179288215, 2128.6149747573713]\n" + "Voltages at 633 [2426.4294610283428, -109.95560266598143, -1300.0199686417882, -2096.278760655414, -1120.4408234615348, 2128.6174379174963]\n" ] } ], @@ -197,8 +232,13 @@ }, { "cell_type": "code", - "execution_count": 73, - "metadata": {}, + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.021115Z", + "start_time": "2023-11-22T02:55:25.914397Z" + } + }, "outputs": [], "source": [ "v = sims.v2dict(bus_names)\n", @@ -207,15 +247,20 @@ }, { "cell_type": "code", - "execution_count": 74, - "metadata": {}, + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.021347Z", + "start_time": "2023-11-22T02:55:25.920067Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sourcebus/VAM 66393.52580623927\n", - "sourcebus/VAA 29.99273887927827\n" + "sourcebus/VAM 66393.52583661868\n", + "sourcebus/VAA 29.992738867675403\n" ] } ], @@ -239,19 +284,20 @@ }, { "cell_type": "code", - "execution_count": 75, - "metadata": {}, + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.073726Z", + "start_time": "2023-11-22T02:55:25.923884Z" + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -287,19 +333,22 @@ }, { "cell_type": "code", - "execution_count": 76, - "metadata": {}, + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.140034Z", + "start_time": "2023-11-22T02:55:26.046946Z" + } + }, "outputs": [ { "data": { + "text/plain": "Running simulation: 0%| | 0/99 [00:00" - ] - }, - "metadata": { - "needs_background": "light" + "text/plain": "
", + "image/png": 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -350,19 +400,20 @@ }, { "cell_type": "code", - "execution_count": 80, - "metadata": {}, + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-22T02:55:26.482497Z", + "start_time": "2023-11-22T02:55:26.358654Z" + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -372,13 +423,6 @@ "plt.plot(I[key_list[0]])\n", "plt.title(key_list[0]);" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 29676ba95f26d79946f6ed611bd061959f4be88a Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 19:18:32 -0800 Subject: [PATCH 07/10] edit to workflow for skip draft PRs --- .github/workflows/release.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 87de83e..d82f1e4 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -9,8 +9,8 @@ on: - 'v[0-9]+.[0-9]+.[0-9]+*' jobs: - test-suite: + if: github.event.pull_request.draft == false runs-on: ${{ matrix.os }} strategy: matrix: @@ -75,4 +75,4 @@ jobs: uses: pypa/gh-action-pypi-publish@master with: user: __token__ - password: ${{ secrets.PYPI_DEPLOYMENT_TOKEN }} \ No newline at end of file + password: ${{ secrets.PYPI_DEPLOYMENT_TOKEN }} From 4ac5daea78cf3daa8c1f605184aaf2f2e5f5bad3 Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Tue, 21 Nov 2023 19:24:52 -0800 Subject: [PATCH 08/10] refactor py scripts --- .../opendss_ingest/opendss_ingestor.py | 245 +++++++----------- .../opendss_ingest/simulation_utils.py | 83 +++--- 2 files changed, 134 insertions(+), 194 deletions(-) diff --git a/btrdbextras/opendss_ingest/opendss_ingestor.py b/btrdbextras/opendss_ingest/opendss_ingestor.py index d6ae589..8b903bc 100644 --- a/btrdbextras/opendss_ingest/opendss_ingestor.py +++ b/btrdbextras/opendss_ingest/opendss_ingestor.py @@ -1,159 +1,92 @@ -#%% +import argparse +from datetime import datetime + +import btrdb import numpy as np -import pandas as pd import opendssdirect as dss -import matplotlib.pyplot as plt -from tqdm.notebook import tqdm_notebook, tqdm -import btrdb as db -import uuid -from btrdb.utils.timez import datetime_to_ns import simulation_utils as sims -from datetime import datetime, timedelta - -import importlib - -importlib.reload(sims); -#%% -# Connect to the database -conn = db.connect(profile="collab") -#%% -model_loc = "./Models/13Bus/IEEE13Nodeckt.dss" -dss.run_command("Redirect " + model_loc); -#%% md -# # Create output streams -# The following cells create the output streams or retrieve them if they have already been created. -#%% -prefix = "simulated/ieee13" -collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) -#%% -nstreams = len(collections) -print("Creating", nstreams, "streams") -for i in range(nstreams): - print(collections[i] + "/" + names[i]) -#%% -streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) -#%% md -# ## Generate simulated measurements -# The following cell generates data that will be converted into streams. -# For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. -#%% -# Samples to generate -start_time = datetime(2022, 1, 1, 0, 0, 0) -end_time = datetime(2022, 1, 1, 0, 1, 0) -fs = 30 # Hz -T = int(((end_time - start_time).total_seconds()) * fs) -print("We will generate", T, "samples.") - -# Generate the nanosecond timestamps for the data -start_ns = datetime_to_ns(start_time) -end_ns = datetime_to_ns(end_time) -timestamps = np.arange(start_ns, end_ns, 1e9 / 30, dtype="int") -#%% -# Get the original loads -load, load_names = sims.get_loads() -nloads = len(load_names) - -# Generate the randomized scaling factors -mu = 1.1 -sig = 0.1 -s = np.random.normal(loc=mu, scale=sig, size=[nloads, T]) - -# Generate the new load values -new_load = s * load[:, np.newaxis] -#%% -V, I = sims.simulate_network(new_load, load_names) -#%% -plt.plot(timestamps, V["646/VCM"]) -plt.plot(timestamps, V["646/VBM"]) - -plt.figure() -plt.plot(timestamps, V["646/VCA"]) -plt.plot(timestamps, V["646/VBA"]) -#%% -print("Number of streams simulated:") -print(len(V.keys()) + len(I.keys())) -#%% md -# # Push data to streams -#%% -# Put voltage data into the corresponding stream -sims.add_all_data(timestamps, V, streams_dict, prefix) -# Put current data into the corresponding stream -sims.add_all_data(timestamps, I, streams_dict, prefix) -#%% md -# # Add long period of data -# The following cell runs a loop which will generate and push data over a much longer period. -# The purpose of the loop is to avoid generating all the data at once - instead, the full time period is divided into smaller time chunks, with data generated and inserted for each chunk sequentially. -# -# The code is divided into two cells below - the first is the initialization step. The second runs the loop. If there is an error during the loop, the second cell can be re-run and will pick up where it left off. -#%% -# Collection prefix in which data will be added -prefix = "simulated/ieee13" -# Get the desired output streams to which data will be pushed -collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) -# If the desired streams exist, retrieve them. Otherwise create them. -streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) - - -# Get the original loads -load, load_names = sims.get_loads() -nloads = len(load_names) - -# Simulation time window - the FULL time range over which we want to generate data -start_time = datetime(2022, 1, 2, 0, 0, 0) -end_time = datetime(2022, 1, 3, 0, 0, 0) -fs = 30 # Hz -Ttotal = int(((end_time - start_time).total_seconds()) * fs) -print("We will generate", Ttotal, "samples.") - -# The simulation time step - this is the amount of data we insert at once. -step = timedelta(minutes=5) -nsteps = int((end_time - start_time) / step) - -# Create progress bar -pbar = tqdm(total=nsteps, desc="Adding simulated data") - -t0 = start_time -#%% -remaining_steps = int((end_time - t0) / step) -pbar = tqdm(total=remaining_steps, desc="Adding simulated data") -while t0 < end_time: - # Generate the nanosecond timestamps for the data - t0_ns = datetime_to_ns(t0) - t1_ns = datetime_to_ns(t0 + step) - timestamps = np.arange(t0_ns, t1_ns, 1e9 / fs, dtype="int") - - # The number of samples to be generated in this iteration - T = len(timestamps) - # Generate the randomized scaling factors - mu = 1.1 - sig = 0.1 - s = np.random.normal(loc=mu, scale=sig, size=[nloads, T]) - # Generate the new load values - new_load = s * load[:, np.newaxis] - # Simulate - V, I = sims.simulate_network(new_load, load_names) - - # Push data to database - # Put voltage data into the corresponding stream - sims.add_all_data(timestamps, V, streams_dict, prefix) - # Put current data into the corresponding stream - sims.add_all_data(timestamps, I, streams_dict, prefix) - - # Increment time - t0 = t0 + step - pbar.update(1) -pbar.close() -#%% md -# # DELETING streams -# **DO NOT RUN** unless you are certain you want to delete. -#%% -prefix = "simulated/ieee13" -# Get the desired output streams to which data will be pushed -collections, names, tags, annotations = sims.get_stream_info(base_col=prefix) -# If the desired streams exist, retrieve them. Otherwise create them. -streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn) - -for key, val in streams_dict.items(): - print("Deleting", key) - ### val.obliterate() ## UNCOMMENT IF YOU WANT TO DELETE -#%% +from btrdb.utils.timez import datetime_to_ns, ns_delta, to_nanoseconds + + +def simulate_network(load, load_names): + V, I = sims.simulate_network(load, load_names) + return V, I + + +def initialize_simulation(fs, start_ns, end_ns): + # The location of the .dss file specifying the model. + model_loc = "./Models/13Bus/IEEE13Nodeckt.dss" + # Activate the model in OpenDSS + dss.run_command("Redirect " + model_loc) + + T = int((end_ns - start_ns) * fs / 1e9) + load, load_names = sims.get_loads() + nloads = len(load_names) + return T, load, load_names, nloads + + +def generate_scaling(mu, sig, size): + return np.random.normal(loc=mu, scale=sig, size=size) + + +def run_simulation(start_ns, end_ns, collection_prefix, fs=30, conn=None): + # Initialize simulation parameters + T, load, load_names, nloads = initialize_simulation(fs, start_ns, end_ns) + timestamps = np.arange(start_ns, end_ns, 1e9 // fs, dtype="int") + scale = generate_scaling(1.1, 0.1, [nloads, T]) + new_load = scale * load[:, np.newaxis] + collections, names, tags, annotations = sims.get_stream_info( + base_col=collection_prefix + ) + streams_dict = sims.create_streams( + collection_prefix, collections, names, tags, annotations, conn + ) + + # Run simulation + V, I = simulate_network(new_load, load_names) + sims.add_all_data(timestamps, V, streams_dict, collection_prefix) + sims.add_all_data(timestamps, I, streams_dict, collection_prefix) + + +def main(collection_prefix, start_time, end_time, fs, profile): + conn = btrdb.connect(profile=profile) + run_simulation(start_time, end_time, collection_prefix, fs, conn) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Simulate network.") + parser.add_argument( + "-s", + "--start_ns", + default=datetime_to_ns(datetime.utcnow()), + type=int, + help=("Start time in nanoseconds. " "(default: %(default))"), + ) + parser.add_argument( + "-e", + "--end_ns", + type=int, + help="End " "time " "in nanoseconds relative to Jan 1, 2023.", + ) + parser.add_argument( + "--frequency", default=30, type=int, help="Sapler frequency in Hz" + ) + parser.add_argument( + "--duration_days", + default=1, + type=int, + help="Duration in hours relative to start.", + ) + parser.add_argument( + "--collection_prefix", default="simulated/ieee13", help="Collection prefix" + ) + parser.add_argument( + "--profile", default="ni4ai", help="BTRDB profile name (default: %(default))" + ) + + args = parser.parse_args() + if args.end_ns is None: + args.end_ns = args.start_ns + ns_delta(days=args.duration_days) + main( + args.collection_prefix, args.start_ns, args.end_ns, args.frequency, args.profile + ) diff --git a/btrdbextras/opendss_ingest/simulation_utils.py b/btrdbextras/opendss_ingest/simulation_utils.py index 445dbb6..8ada7b6 100644 --- a/btrdbextras/opendss_ingest/simulation_utils.py +++ b/btrdbextras/opendss_ingest/simulation_utils.py @@ -1,5 +1,5 @@ import uuid -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, List, Optional, Tuple import numpy as np import opendssdirect as dss @@ -110,25 +110,34 @@ def i2dict(con_names: List[str]) -> Dict[str, float]: if phases[pidx] == 0: continue # Construct the complex current. - current = (coni[2 * (end * nphases + pidx)] + 1j * coni[ - 2 * (end * nphases + pidx) + 1]) + current = ( + coni[2 * (end * nphases + pidx)] + + 1j * coni[2 * (end * nphases + pidx) + 1] + ) # Save the magnitude data col, name = get_lineflow_stream_colname( - con_names[cidx], con_ends[cidx][end], - PHASE_LETTERS[phases[pidx] - 1], True, ) + con_names[cidx], + con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], + True, + ) I[col + "/" + name] = np.abs(current) # Save the angle data col, name = get_lineflow_stream_colname( - con_names[cidx], con_ends[cidx][end], - PHASE_LETTERS[phases[pidx] - 1], False, ) + con_names[cidx], + con_ends[cidx][end], + PHASE_LETTERS[phases[pidx] - 1], + False, + ) I[col + "/" + name] = np.angle(current, deg=True) return I def simulate_network( - loads: np.ndarray, load_names: List[str], - con_types: Optional[List[str]] = None - ) -> Dict[str, np.ndarray]: + loads: np.ndarray, + load_names: List[str], + con_types: Optional[List[str]] = ["Line", "Transformer"], +) -> Dict[str, np.ndarray]: """ Simulates the network for all the values of load in the input loads. @@ -204,8 +213,9 @@ def simulate_network( ############################################################################### -def get_stream_info(base_col="simulated") -> Tuple[ - List[str], List[str], List[Dict[str, str]], List[Dict[str, str]]]: +def get_stream_info( + base_col="simulated", +) -> Tuple[List[str], List[str], List[Dict[str, str]], List[Dict[str, str]]]: """ Returns collection names, tags, and annotations for all the streams we want to create to hold @@ -247,13 +257,9 @@ def get_stream_info(base_col="simulated") -> Tuple[ if p == 0: continue # Magnitude stream - cM, nM, tM, aM = get_voltage_stream_info( - bus, phases[p - 1], True, basekV - ) + cM, nM, tM, aM = get_voltage_stream_info(bus, phases[p - 1], True, basekV) # Angle stream - cA, nA, tA, aA = get_voltage_stream_info( - bus, phases[p - 1], False, basekV - ) + cA, nA, tA, aA = get_voltage_stream_info(bus, phases[p - 1], False, basekV) # Save results collections.append(base_col + "/" + cM) collections.append(base_col + "/" + cA) @@ -268,13 +274,12 @@ def get_stream_info(base_col="simulated") -> Tuple[ # Get the names of all connectors con_names = get_connectors() for con in con_names: - # Set the current connector to be "active" dss.Circuit.SetActiveElement(con) # Get the buses that this connector connects # (the split removes terminals indicating the phases at each end - # so three phase busX.1.2.3 becomes busX) + # so three-phase busX.1.2.3 becomes busX) to = dss.CktElement.BusNames()[0].split(".")[0] frm = dss.CktElement.BusNames()[1].split(".")[0] # Check that to and frm are different (these can be the same for capacitors) @@ -289,17 +294,13 @@ def get_stream_info(base_col="simulated") -> Tuple[ nphases = int(len(conphases) / 2) for end in ends: - for p in conphases[0:nphases]: - # We don't want to save any phase 0 information if p == 0: continue # Magnitude stream - cM, nM, tM, aM = get_lineflow_stream_info( - con, end, phases[p - 1], True - ) + cM, nM, tM, aM = get_lineflow_stream_info(con, end, phases[p - 1], True) # Angle stream cA, nA, tA, aA = get_lineflow_stream_info( con, end, phases[p - 1], False @@ -318,7 +319,7 @@ def get_stream_info(base_col="simulated") -> Tuple[ def get_existing_streams(col_prefix, conn): - """ Get the existing streams under the base collection col_prefix """ + """Get the existing streams under the base collection col_prefix""" streams = conn.streams_in_collection(col_prefix) # Build the dictionary of the streams streams_dict = {} @@ -329,9 +330,14 @@ def get_existing_streams(col_prefix, conn): def create_streams( - col_prefix: str, collections: List, names, tags, annotations, conn: BTrDB, - verbose: bool = False - ): + col_prefix: str, + collections: List, + names, + tags, + annotations, + conn: BTrDB, + verbose: bool = False, +): """ Given a set of collections, names, tags, and annotations for intended streams, check if they exist. If not, create them. @@ -361,8 +367,11 @@ def create_streams( stream_id = uuid.uuid4() stream = conn.create( - uuid=stream_id, collection=collections[i], tags=tags[i], - annotations=annotations[i], ) + uuid=stream_id, + collection=collections[i], + tags=tags[i], + annotations=annotations[i], + ) existing[stream_info] = stream if verbose: @@ -377,9 +386,7 @@ def get_lineflow_stream_info(line_name, line_end, phase, ismag): unit = "amps" else: unit = "degrees" - collection, name = get_lineflow_stream_colname( - line_name, line_end, phase, ismag - ) + collection, name = get_lineflow_stream_colname(line_name, line_end, phase, ismag) tags = {"name": name, "unit": unit} annotations = {"phase": phase} @@ -480,7 +487,7 @@ def get_buses(): def get_connectors(qualified=["Line", "Transformer"]): - """ This method returns all connection elements of the "qualified" types""" + """This method returns all connection elements of the "qualified" types""" connectors = [] pds = dss.PDElements.AllNames() for pd in pds: @@ -491,7 +498,7 @@ def get_connectors(qualified=["Line", "Transformer"]): def get_conn_ends(con_names): - """ The list of lists with names of connectors ends """ + """The list of lists with names of connectors ends""" con_ends = [] for con in con_names: # Set the current connector to be "active" @@ -511,7 +518,7 @@ def get_conn_ends(con_names): def get_loads(): - """ Get all the loads in the network """ + """Get all the loads in the network""" load_names = dss.Loads.AllNames() nloads = len(load_names) @@ -525,7 +532,7 @@ def get_loads(): def set_loads(load, load_names): - """ Set the value of load load_names[i] to load[i] """ + """Set the value of load load_names[i] to load[i]""" nloads = len(load_names) for i in range(nloads): # Set this load to be active From f79f299dd98a945809a781215ff30b327536ff73 Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Mon, 27 Nov 2023 18:37:58 -0800 Subject: [PATCH 09/10] update requirements to latest btrdb --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index a613877..0dcdf97 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,11 +6,11 @@ grpcio-tools>=1.16.1 dill==0.3.2 # BTrDB -btrdb>=5.11.7 +btrdb>=5.31.0 # Utilities and helpers tabulate==0.8.9 certifi # Readthedocs -sphinx_glpi_theme \ No newline at end of file +sphinx_glpi_theme From 53e9d1a884afe77eaa4c75ba64e7c82b5d5bca4a Mon Sep 17 00:00:00 2001 From: Jeff Lin <42981468+jleifnf@users.noreply.github.com> Date: Mon, 27 Nov 2023 20:39:52 -0800 Subject: [PATCH 10/10] update opendssingest as streaming ingestor --- btrdbextras/__init__.py | 2 +- .../opendss_ingest/opendss_ingestor.py | 164 +++++++++++++++--- btrdbextras/opendss_ingest/requirements.txt | 3 + .../opendss_ingest/simulation_utils.py | 96 +++++----- tests/test_opendss_ingestor.py | 41 +++++ 5 files changed, 238 insertions(+), 68 deletions(-) create mode 100644 tests/test_opendss_ingestor.py diff --git a/btrdbextras/__init__.py b/btrdbextras/__init__.py index 5a9dad2..4e1439e 100644 --- a/btrdbextras/__init__.py +++ b/btrdbextras/__init__.py @@ -1,5 +1,5 @@ from .conn import Connection -__version__ = 'v5.11.9' +__version__ = '5.11.9' __all__ = ["__version__", "Connection"] diff --git a/btrdbextras/opendss_ingest/opendss_ingestor.py b/btrdbextras/opendss_ingest/opendss_ingestor.py index 8b903bc..a2deb17 100644 --- a/btrdbextras/opendss_ingest/opendss_ingestor.py +++ b/btrdbextras/opendss_ingest/opendss_ingestor.py @@ -1,39 +1,119 @@ import argparse +import os +import time from datetime import datetime +from typing import List, Tuple -import btrdb import numpy as np import opendssdirect as dss -import simulation_utils as sims -from btrdb.utils.timez import datetime_to_ns, ns_delta, to_nanoseconds +import pyarrow as pa +from btrdb import connect as btrdb_connect +from btrdb.stream import Stream,StreamSet, INSERT_BATCH_SIZE +from btrdb.utils.timez import datetime_to_ns, ns_delta +import btrdbextras.opendss_ingest.simulation_utils as sims -def simulate_network(load, load_names): - V, I = sims.simulate_network(load, load_names) - return V, I +MODEL_REL_PATH = os.path.dirname(__file__) -def initialize_simulation(fs, start_ns, end_ns): - # The location of the .dss file specifying the model. - model_loc = "./Models/13Bus/IEEE13Nodeckt.dss" +def initialize_simulation(model_loc:str) -> Tuple[np.ndarray, List[str]]: + """ + Initializes the simulation by activating the model in OpenDSS and + retrieving the simulated loads. + + Parameters + ---------- + model_loc : str + The file path of the model to be activated in OpenDSS. + + Returns + ------- + load : np.ndarray + An array of loads retrieved from the model. + load_names : list of str + An array of load names corresponding to the loads in the model. + + """ # Activate the model in OpenDSS dss.run_command("Redirect " + model_loc) - - T = int((end_ns - start_ns) * fs / 1e9) load, load_names = sims.get_loads() - nloads = len(load_names) - return T, load, load_names, nloads + return load, load_names def generate_scaling(mu, sig, size): + """ + Generates a random scaling factor based on a normal distribution with mean `mu` + and standard deviation `sig`. The number of scaling factors generated is determined by `size`. + + Parameters + ---------- + mu : float + The mean of the normal distribution. + sig : float + The standard deviation of the normal distribution. + size : int + The number of scaling factors to generate. + + Returns + ------- + ndarray + An array of scaling factors generated from the normal distribution. + + """ + # TODO: add more types of scaling as additive noise/signal? return np.random.normal(loc=mu, scale=sig, size=size) -def run_simulation(start_ns, end_ns, collection_prefix, fs=30, conn=None): +def simulate_event(value, continue_event:bool=False): + + event_type = np.random.choice(np.arange(10)) # 10% probability of evnet? + + if event_type == 0 and continue_event: # where the values for V and I are + # 0.0 + value = 0.0 + elif event_type == 1: # with no data at all + value = None + elif event_type == 2: # value is out of bounds + value = np.random.choice([np.random.uniform(low=-10, high=-1), + np.random.uniform(low=1e15, high=1e20)]) + else: + pass + return value + +def run_simulation(start_ns, end_ns, collection_prefix, + fs=30, conn=None, + model_location=None): + """ + Runs a simulation from `start_ns` to `end_ns` with the given `collection_prefix`. + The simulation is initialized using a model obtained from `model_location`. + If `model_location` is not provided, it defaults to 'Models/13Bus/IEEE13Nodeckt.dss'. + The simulation uses frequency `fs` and a database connection `conn`. + + Parameters + ---------- + start_ns : int + The start time of the simulation in nanoseconds. + end_ns : int + The end time of the simulation in nanoseconds. + collection_prefix : str + The prefix for the name of the data collection for the simulation. + fs : int, optional + The frequency of the simulation, defaults to 30. + conn : obj, optional + The database connection used for the simulation, defaults to None. + model_location : str, optional + The file location of the model to be used for the simulation. + Defaults to 'Models/13Bus/IEEE13Nodeckt.dss' if not specified. + + """ + model_location = (model_location + if model_location is not None + else os.path.join(MODEL_REL_PATH, + 'Models/13Bus/IEEE13Nodeckt.dss')) # Initialize simulation parameters - T, load, load_names, nloads = initialize_simulation(fs, start_ns, end_ns) + load, load_names = initialize_simulation(model_location) timestamps = np.arange(start_ns, end_ns, 1e9 // fs, dtype="int") - scale = generate_scaling(1.1, 0.1, [nloads, T]) + scale = generate_scaling(1.1, 0.1, [len(load_names), timestamps.size]) new_load = scale * load[:, np.newaxis] collections, names, tags, annotations = sims.get_stream_info( base_col=collection_prefix @@ -41,15 +121,50 @@ def run_simulation(start_ns, end_ns, collection_prefix, fs=30, conn=None): streams_dict = sims.create_streams( collection_prefix, collections, names, tags, annotations, conn ) - + streamset = StreamSet(list(streams_dict.values())) + _stream_info = lambda s: "/".join( + [s.collection.replace( + collection_prefix + '/', '' + ), s.name] + ) + _values = lambda s, cont_event: simulate_event( + (V.get(_stream_info(s)) or I.get(_stream_info(s)))[0], + cont_event) + prev_timestamp = None + # For example, event lasts for at most 100 timestamps + continue_event_ind_left = np.random.randint(1, 100) # Run simulation - V, I = simulate_network(new_load, load_names) - sims.add_all_data(timestamps, V, streams_dict, collection_prefix) - sims.add_all_data(timestamps, I, streams_dict, collection_prefix) - + for i in range(new_load.shape[1]): + V, I = sims.simulate_network(new_load[:, [i]], load_names) + if continue_event_ind_left == 0: + continue_event_ind_left = np.random.randint(1, 100) + else: + continue_event_ind_left -= 1 + now = datetime_to_ns(datetime.utcnow()) + _datamap_gen = conn._executor.map( + lambda s: (s._uuid, + dict(time=now, + value=_values(s, + continue_event_ind_left > 0) + )), + streamset._streams + ) + data_map = {} + for k,v in _datamap_gen: + if v['value'] is not None: + data_map[k] = pa.Table.from_pylist([v]) + _streamset = StreamSet([Stream(conn, uuid=k) for k in + data_map.keys()]) + _streamset.arrow_insert(data_map) + if i % INSERT_BATCH_SIZE == 0: + [stream.flush() for stream in streamset] + print("Streamset flushed.") + time.sleep(round(1/fs,ndigits=5)) + # print(prev_timestamp, now) if i % 10 == 0 else None + prev_timestamp = now def main(collection_prefix, start_time, end_time, fs, profile): - conn = btrdb.connect(profile=profile) + conn = btrdb_connect(profile=profile) run_simulation(start_time, end_time, collection_prefix, fs, conn) @@ -69,7 +184,8 @@ def main(collection_prefix, start_time, end_time, fs, profile): help="End " "time " "in nanoseconds relative to Jan 1, 2023.", ) parser.add_argument( - "--frequency", default=30, type=int, help="Sapler frequency in Hz" + "--frequency", default=30, type=int, + help="Sampling frequency in Hz" ) parser.add_argument( "--duration_days", @@ -86,7 +202,9 @@ def main(collection_prefix, start_time, end_time, fs, profile): args = parser.parse_args() if args.end_ns is None: + print("end_ns is not given, using default value for duration") args.end_ns = args.start_ns + ns_delta(days=args.duration_days) + main( args.collection_prefix, args.start_ns, args.end_ns, args.frequency, args.profile ) diff --git a/btrdbextras/opendss_ingest/requirements.txt b/btrdbextras/opendss_ingest/requirements.txt index feb7b38..d959997 100644 --- a/btrdbextras/opendss_ingest/requirements.txt +++ b/btrdbextras/opendss_ingest/requirements.txt @@ -1 +1,4 @@ OpenDSSDirect.py[extras] + +numpy>=1.24.3 +tqdm>=4.66.1 diff --git a/btrdbextras/opendss_ingest/simulation_utils.py b/btrdbextras/opendss_ingest/simulation_utils.py index 8ada7b6..0119851 100644 --- a/btrdbextras/opendss_ingest/simulation_utils.py +++ b/btrdbextras/opendss_ingest/simulation_utils.py @@ -4,6 +4,8 @@ import numpy as np import opendssdirect as dss from btrdb import BTrDB +from btrdb.stream import StreamSet +from pandas import DataFrame from tqdm.auto import tqdm PHASE_LETTERS = ["A", "B", "C"] @@ -23,7 +25,7 @@ def v2dict(bus_names: List[str]) -> Dict[str, float]: Dict[str, float] V : dictionary of real values. The keys are the stream collection/name for the data. The collection / stream name encodes the bus, phase, and quantity - which are formatted by the method get_voltage_stream_colname. + which are formatted by the method get_voltage_stream_column_name. """ # Instantiate the dict of results V = {} @@ -50,17 +52,14 @@ def v2dict(bus_names: List[str]) -> Dict[str, float]: voltage = busvolt[pidx * 2] + 1j * busvolt[pidx * 2 + 1] - # Save the magnitude data - col, name = get_voltage_stream_colname( - bus, PHASE_LETTERS[phases[pidx] - 1], True - ) - V[col + "/" + name] = np.abs(voltage) - - # Save the angle data - col, name = get_voltage_stream_colname( - bus, PHASE_LETTERS[phases[pidx] - 1], False - ) - V[col + "/" + name] = np.angle(voltage, deg=True) + # Save the magnitude/angle data + for is_mag in (True, False): + col, name = get_voltage_stream_column_name( + bus, PHASE_LETTERS[phases[pidx] - 1], is_mag + ) + V[col + "/" + name] = (np.abs(voltage) + if is_mag else + np.angle(voltage, deg=True)) return V @@ -166,7 +165,7 @@ def simulate_network( connector, end, phase, and quantity which is formatted by the method get_lineflow_stream_colname. """ - [n, T] = np.shape(loads) + [n, T] = loads.shape # Get the buses and connectors bus_names = get_buses() @@ -176,33 +175,39 @@ def simulate_network( V = {} I = {} - # Run the first simulation to get the keys for the output dictionary - # Set the new load values - set_loads(loads[:, 0], load_names) - # Solve the power flow - dss.Solution.Solve() - # Get the data - vdata = v2dict(bus_names) - idata = i2dict(con_names) - # Save the voltage data - for key, val in vdata.items(): - V[key] = np.nan * np.ones(T) - V[key][0] = val - # Save the current data - for key, val in idata.items(): - I[key] = np.nan * np.ones(T) - I[key][0] = val + # if is_initial_run: + # # Run the first simulation to get the keys for the output dictionary + # # Set the new load values + # set_loads(loads[:, 0], load_names) + # # Solve the power flow + # dss.Solution.Solve() + # # Get the data + # vdata = v2dict(bus_names) + # idata = i2dict(con_names) + # # Save the voltage data + # for key, val in vdata.items(): + # V[key] = np.nan * np.ones(T) + # V[key][0] = val + # # Save the current data + # for key, val in idata.items(): + # I[key] = np.nan * np.ones(T) + # I[key][0] = val # Iterate through rest of the times - for t in tqdm(range(1, T), desc="Running simulation", leave=False): + prog_bar = tqdm(range(0, T), desc="Running simulation", leave=False) + for t in prog_bar: set_loads(loads[:, t], load_names) - dss.Solution.Solve() + dss.Solution.Solve() # TODO: explore incremental solution in opendss vdata = v2dict(bus_names) idata = i2dict(con_names) for key, val in vdata.items(): + if t == 0: + V[key] = np.nan * np.ones(T) V[key][t] = val for key, val in idata.items(): + if t == 0: + I[key] = np.nan * np.ones(T) I[key][t] = val return V, I @@ -405,7 +410,7 @@ def get_lineflow_stream_colname(line_name, line_end, phase, ismag): return collection, name -def get_voltage_stream_colname(bus_name, phase, ismag): +def get_voltage_stream_column_name(bus_name, phase, ismag): collection = bus_name if ismag: lastltr = "M" @@ -421,7 +426,7 @@ def get_voltage_stream_info(bus_name, phase, ismag, basekV): unit = "volts" else: unit = "degrees" - collection, name = get_voltage_stream_colname(bus_name, phase, ismag) + collection, name = get_voltage_stream_column_name(bus_name, phase, ismag) tags = {"name": name, "unit": unit} annotations = {"phase": phase, "basekV": str(basekV)} @@ -448,15 +453,15 @@ def add_all_data(times, data_dict, streams_dict, base_col): base collection prefix under which all streams can be found. """ # Create progress bar - nstreams = len(data_dict.keys()) + nstreams = len(data_dict) pbar = tqdm(total=nstreams, desc="Pushing data to streams", leave=False) - for key in data_dict: - stream_info = base_col + "/" + key + for key, value in data_dict.items(): + stream_info = f"{base_col}/{key}" if stream_info in streams_dict: - add_to_stream(streams_dict[stream_info], times, data_dict[key]) + add_to_stream(streams_dict[stream_info], times, value) else: - print("WARNING", stream_info, "not found") + print(f"WARNING: {stream_info} not found") pbar.update(1) pbar.close() @@ -466,21 +471,24 @@ def add_to_stream(stream, times, values): Given times and values, put them in the required tuple format and add them to the stream. """ - payload = [] - if len(times) != len(values): print("WARNING: times & values not same size") - for i in range(len(times)): - payload.append((times[i], values[i])) + payload = list(zip(times, values)) stream.insert(payload, merge="replace") +def ingest_streamset(streamset:StreamSet, data: DataFrame, period_ns: int): + while True: + for row in data.iterrows(): + streamset.insert(row.to_dict()) + pass + + ############################################################################### # Convenient wrappers to get model information ############################################################################### - def get_buses(): """A convenient wrapper to retrieve all buses in the system.""" return dss.Circuit.AllBusNames() @@ -517,7 +525,7 @@ def get_conn_ends(con_names): return con_ends -def get_loads(): +def get_loads() -> Tuple[np.ndarray, List[str]]: """Get all the loads in the network""" load_names = dss.Loads.AllNames() nloads = len(load_names) diff --git a/tests/test_opendss_ingestor.py b/tests/test_opendss_ingestor.py new file mode 100644 index 0000000..75b79d9 --- /dev/null +++ b/tests/test_opendss_ingestor.py @@ -0,0 +1,41 @@ +import os + +import btrdb +from btrdb.utils.timez import to_nanoseconds + +from btrdbextras.opendss_ingest.opendss_ingestor import ( + MODEL_REL_PATH,initialize_simulation,run_simulation +) + + +class TestOpendssIngestor: + def test_initialize_simulation(self): + # Arrange + mock_model_loc = os.path.join( + MODEL_REL_PATH, + "Models/13Bus/IEEE13Nodeckt.dss" + ) + load, load_names = ([1155., 160., 120., 120., 170., 230., 170., + 485., 68., 290., 170., 128., 17., 66., 117.], + ['671', '634a', '634b', '634c', '645', '646', '692', + '675a', '675b', '675c', '611', '652', '670a', '670b', + '670c']) + # Act + results = initialize_simulation(mock_model_loc) + assert results[0].tolist() == load + assert results[-1] == load_names + + def test_simulate_network(self): + # load, load_names = ([1155., 160., 120., 120., 170., 230., 170., + # 485., 68., 290., 170., 128., 17., 66., 117.], + # ['671', '634a', '634b', '634c', '645', '646', + # '692', + # '675a', '675b', '675c', '611', '652', '670a', + # '670b', + # '670c']) + start_time = to_nanoseconds('2023-01-01 00:00:00') + end_time = to_nanoseconds('2023-01-01 00:01:00') + db = btrdb.connect(profile='ni4ai') + run_simulation(start_time, end_time, + collection_prefix='simulated/ieee13', fs=30, conn=db) + assert True