Integrated System Models Graph Trace Analysis Distributed Engineering Workstation

Transcription

1 Integrated System Models Graph Trace Analysis Distributed Engineering Workstation Robert Broadwater 1

2 Model Based Intelligence 2

3 Integrated System Models Merge many existing, models together, relating all measurements in context of ISM Community Model Automated Analysis Attach SCADA, all customer load, weather, outage, and other measurements Re use model from planning to training to real time analysis to real time control Push algorithms to data 3

4 Distributed Engineering Workstation Solves Integrated System Models Has solved transmission distribution system models with more than 3,000,000 nodes and 3,000 complex, multiphase loops Uses Graph Trace Analysis for algorithms Object oriented approach to topology management Time Series Analysis Loads, weather, generation ( NREL interface, Clean Power Research interface), time varying LMP costs 4

5 ISM as Living Model 5

6 GTA: Generic Programming Roots Algorithms that process objects in container, independent of object type Container with Objects Iterators CS Algorithms Generic Programming Ex: Sorting algorithms Algorithms that process edges or components of graph ISM Topology Iterators Engineering Algorithms Graph Trace Analysis Ex: Flow simulation Generic analysis code independent of system type (eg, electric, gas, etc.) 6

7 Matrix Analysis with Edge Node Graph Graph Trace Analysis with Edge Edge Graph Topology Iterators Global View N o d e s 5 4 Transform 4 Edges Computer Processing Local View 5 4 Edge knows neighbors Topology continuously maintained Algorithms with topology iterators Object oriented topology 7

8 GTA Based Power Flow 8

9 ISM Analysis Architecture Mass Storage Memory ISM edge-edge topology Living Model Customer Loads ISM SCADA Measurements Weather Measurements Interface provided by ISM to applications App 1 App 2 Topology iterators, sharing of results, measurements Push algorithms to data 9

10 Real World Versus Simplified Models The best equivalent is no equivalent Avoid scenario based solutions use scenarios in testing, not control algorithms 10

11 Relation of ISM to GIS Used to clean GIS data, especially non physics based GIS modeling 11

12 ISM Model Management for Distributed Environment Measurements Maintained in memory ISM Model Client: Fault Location ISM Model Server Supports distributed computations Model Queue ISM Model Client: Reconfiguration Analysis Processes 12

13 Analysis Automation Example: Probabilistic Risk Assessment Assembly line computing 13

14 Collaborative Solutions 14

15 Peak Load versus Time Varying Design Time varying designs provide better foundation for automation More capacity for automation to work with Time varying designs provide significant improvements in efficiency For medium sized utilities, $1,000,000s / year For large utilities, $10,000,000s / year 15

16 DEW Programming Interfaces C++ programming interface Used for over 15 years by graduate students Automatic creation of starter application Programmer can focus on calculations Matlab interface Can be used for modeling controller dynamics, such as PV controls, or for modeling power plant dynamics 16

17 MODEL CENTRIC DMS 17

18 Model Centric DMS Architecture 18

19 DEW Control Agorithms DSR control optimization for transmission Standard capacitor, voltage regulator, and LTC controls, including time delays PV controls, including voltage error feedback, voltvar control, and volt watt control Coordinated control with 3 modes CVR, feeder efficiency, and maximum capacity DG control Automated reconfiguration 19

20 DEW Real Time Controls SCADA interface with OPC client Used for supervisory control of distributed generation Used for coordinated control of capacitor banks (including multi step, individually phase controlled), LTCs, and voltage regulators Fully automated reconfiguration for restoration with fault re isolation and location Runs on blade computers, automatically distributing calculations 20

21 Model Centric Smart Grid Phase 1: Develop architecture and analyze costs/benefits of alternatives by using simulation Phase 2: Test design concepts and integration in lab setting, using production systems as much as possible Phase 3: Test design concepts and integration in field pilot Phase 4: Gather measurements from pilot project and compare against predicted analysis values 21

22 Model Centric Smart Grid Phase 5: Use simulation to plan most cost effective system implementation Phase 6: Implement throughout system Phase 7: Gather measurements from system implementation and compare against predicted analysis values Above phases should be iterated 22

23 VERIFICATION & VALIDATION 23

24 ORU ISM Validation Experiences Identified SCADA measurements failed 50% low Helped located failed controllers Prediction of annual system losses calculated from 8760 power flow within 0.4% of measured value

25 Phase Balancing Validation 25

26 Verification IEEE Standard Transmission and Distribution models Verification of line impedance calculations Verification of power flow calculations Verification of fault analysis calculations Robustness tests specified by independent consultant Passed 89 tests 26

27 Validation of Load Research Mount Valley Sub 2977 Res, 349 commercial Entergy no longer sends engineers into the field to capture peak measurement 27

28 Ameren Reconfiguration Validation Reconfiguration for restoration solution prevented major power outage in 2002 using system model of 50 interconnected feeders and over 2000 sectionalizing devices Since then validated in extensive smart grid lab tests at ORU for real time switching automation 28

29 CEC Funded NREL Study: Test Circuit 29

30 CEC Funded NREL Study: Sample Results 30

31 Fault Location Validation at Detroit Edison 31

32 Motor Start Validation at Detroit Edison 32

33 DEW APPLICATIONS 33

34 DEW Applications Data mapper and circuit builder Customer load attachment Monthly, demand, hourly SCADA data attachment Outage data attachment Weather data attachment Automatic schematic builder All schematics synchronized with GIS model 34

35 Power flow DEW Applications Solves transmission, radial distribution, lightly meshed distribution, and heavily meshed distribution, and secondary distribution, all in same model Diversified power flow Fault analysis Secondary fault analysis Fault location Validated at Allegheny Power Systems 35

36 DEW Applications Load research analysis Validated at Entergy and Detroit Edison Customer class profiler Load estimation Takes into account weather conditions Component impedance and admittance Takes into account temperature Planning tool 36

37 DEW Applications Switching sequence analysis Reliability analysis Reconfiguration for restoration Generic application that runs across critical infrastructure, including electric, gas, and water Monte Carlo analysis DER adoption Cascading power flows Storm response with reconfiguration for restoration 37

38 DEW Applications Outage display and protective device operation analysis Lightning density and outage analysis Phase balancing design Capacitor design Protection/coordination design Phase prediction Used at Detroit Edison 38

39 DEW Applications Flicker analysis Secondary equipment optimization Transformer load management Can use either monthly or hourly data 100% accurate at predicting overloaded transformers at ORU Contingency analysis Reconfiguration stressor Contingency analysis with reconfiguration 39

40 DEW Applications DR control DR fuse checker Checks for partial fuse damage DER adoption analysis DER assessment analysis Coordinated control 3 modes: CVR, feeder efficiency, maximum capacity 40

41 DEW Applications CES battery scheduling Feeder performance analysis Automates system analysis over time varying load Used to compare benefits of alternative designs Distributed series reactance design for flow control of transmission lines 41

42 DEW Applications Arc flash analysis Measurement based harmonic power flow Revenue flow 42

43 DEW SAMPLING OF SCREEN SHOTS 43

44 Auto Schematic Generation 44

45 Cascading Failure Analysis 45

46 Display of Capacity Over Google Earth Legend: >3 Required Capacity 2-3 Required Capacity Required Capacity Required Capacity Required Capacity Required Capacity 0kVA Remaining Capacity (e.g. Since the user entered 1000kVA, the green lines have more than 3MVA remaining capacity) 46

47 Load Density Over Google Earth 47

48 Fault Location Results over Google Earth Crew dispatched directly to fault location, eliminating patrol 48

49 Residential Heat Pump Load Scaling Factor 49

50 Revenue Flows 50

51 Storm Outage Forecasts Cumulative Outage Number Empirical Model Real Time Storm Data Five Hour Forecast with Observer Time Elapse since Storm Arrival (Hour) Normal Storm Cumulative Outage Number Empirical Model 50 Real Time Storm Data Five Hour Forecast with Observer Time Elapse since Storm Arrival (Hour) Abnormal Storm 51

52 DER Controller Motion Results 52

53 DER Fault Assessment Results 53

54 EV/Battery/Solar Analysis 54

55 Monte Carlo Storm Analysis Results Evaluation of automated system versus manual system for storm response 55

56 Overview Window Overview window immediately tracks loss of power throughout system 56

57 Circuit Coloring by Operating Voltage 57

58 Circuit Coloring by Voltage Ranges Davis 8960 Blue > 118 volts Green < 118 volts Red < 114 volts 58

59 Lightning Density Analysis Storm Number of Flashes inside the corridor above selected intensity >10 >20 >30 >40 > Half Corridor Width (feet) Calculates lightning density within corridor around lines

60 Load Growth Solution: Distribution DGs versus Transmission Lines $125 million transmission solution versus $23 million distribution solution 60

61 Integrated System-of-Systems Model 61

62 Zoom in on System of Systems Compartment Fire Electrical Isolation 62