Mixed Node/Breaker and Bus/Branch Models Using CIM
M. Kemal Celik
VP, Distribution Grid Management
Nexant, Inc.
[email protected]
Exponentially Increasing Challenges for the Grid
Transmission
Distribution
Downstream flows
State Estimation,
Power Flow
NEW Planning
Paradigms Needed
Protection & Switching
Schemes
Bi‐directional flows
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Network Based Planning for the Grid – T& D
Present
Hours into the future
Months into the future
Years into the future
Operations
Near-term Planning
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Next day/week with short‐term load forecasts based on weather forecast & using installed DER
Next month/season/year with annual load growth & using different DER penetrations
Planning
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Smart Grid Data Stack & Near‐term Distribution Planning
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Accurate Overlaying of Data Stacks  CIM
• Data interaction & integration of
• GIS, DMS, SCADA, MDM, OMS, DRMS
• Planning and Operations
• Across utilities, RTOs, ISOs
• Share network data seamlessly
• Advanced robust network‐based numerical analysis
• Planning analytics for DERMs impact
• Extending network visibility using smart meter data (for distribution networks)
• Incremental network expansion, such as adding micro‐grids, renewable generation farms
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Physical/Abstract Network Modeling: Node/breaker vs. Bus/branch
• Operational energy management system network data is based on node/breaker model
• Explicitly models physical equipment and devices
• Advanced network numerical analysis algorithms use an abstract model of the network for mathematical analysis
• Advanced numerical analysis include state estimation, power flow, optimal power flow, short circuit analysis, etc.
• Is typically called bus/ branch network model
• Eliminates the switches based on their open/close status
• Bus/branch network model is an abstract model that represent a physical network at a particular time
• Planning software use bus/branch network models
• Bus/branch models are derived from the node/breaker through a process usually called network topology processing (NTP)
• NTP needs to retain the mapping of the physical assets to the abstract bus/branch model in detail
• Bus‐bar‐sections with closed switches are modeled as a single bus (node)
• Otherwise, a particular snapshot of the network status will lose references to the physical bus‐bar‐
sections and switches
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Physical Model to CIM
* From CIM Premier
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CIM Equivalent of a Substation Configuration
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CIM Equivalent of a Feeder
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A Numerical Network Analysis Example – Generalized State Estimation
Conventional
• Read in node/breaker data
• Run measurement plausibility
• Do network topology processing
• Run SE on bus/branch model
• Run bad data (BD) analysis
• If none, stop
• Delete measurements with the largest normalized residuals & run SE again
Generalized
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Read in node/breaker data
Run measurement plausibility
Do network topology processing
Run SE on bus/branch model
Run bad data (BD) analysis
• If none, stop
• If BD are detected, go to next SE run
• Read in node/breaker data
• Build bad data pockets
• Use a hybrid model & model bad data pockets in detail
• Zoom around BD in pockets & perform combinatorial analysis
• Correct topology errors & eliminate BD
• Can be used to consistently and numerically overlay granular nodal (bus) data from different sources (SCADA, GIS, DMS MDM, etc.)
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Mixed Node/Breaker & Bus/Branch Modeling
* Generalized State Estimation; O. Alsac, N. Vempati, B. Stott, A. Monticelli
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Unique Network Modeling & Analysis
Bad Data
Substation
Region of Interest
Zoomed
Window
Bad Data
Pocket
Pockets & windows do not grow in size with total number of buses, thus their solutions
are system size independent
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Summarizing Point Made So Far….
Traditionally, transmission and distributions have been different processes
Traditionally, planning & operations have been separate processes
DERs are a fact of life, not optional nice‐green technologies
Traditional operational or near‐term planning is finally coming to distribution planning
Traditionally, power system operations & planning software has had proprietary data formats/objects/formatted files for interaction  Replaced CIM
• Physical world requires representation of the equipment, devices on the networks
• Numerical analysis, like power flow, algorithms work off abstract bus/branch models
• Several drivers are pushing these separate worlds to converge to a more comprehensive representation of the electric power networks
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A network is a network is a network
I can run a power flow algorithm for an ISO network with several 10K buses within seconds on my laptop
DERs & micro‐grids are challenging the way we operate distribution networks
Data integration is still the biggest nightmare at a utility
CIM is becoming the global standard for data interaction
Sophisticated numerical analysis algorithms need mixed node/breaker & bus/branch modeling
Visual representation needs to be able to handle both node/breaker & bus/branch data
Near‐term planning what‐if analysis environment requires modeling closer to the physical world
• Retained switches vs. all switches
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CIM Profiles & Dependencies
• Independent of other profiles and is imported first before any other profiles of the same network
• EQ_BD: Boundary equipment and connectivity for • BD: Boundary
adjacent networks
• EQ/CN: Basic equipment connectivity and their attributes/parameters
• SSH: Defines initial state of a network
• GL: Geographical layout of a network
• MS: Device measurements
• TP:
Defined by a process similar to NTP
• SV: State Variables as a result of state estimate OR also defines initial state of the network
• DIFF: Difference
• EQ_DIFF: Equipment difference as user edits
• SV_DIFF: State variable difference as edits to initial state
• Equipment profile may have dependency on BD profile(s), Δ for what‐if
• Steady state profile is dependent on EQ and BD
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Depends on EQ profile for equipment reference, Δ for what‐if
Dependent on EQ (devices at terminals)
Topology is result of connectivity analysis CNs & EQ
Represents results vector (OR also defines initial state of the network)
• Difference
• EQ_DIFF
• SV_DIFF
Equipment difference as user edits
State variable difference as edits to initial state
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Node/breaker vs. bus/branch modeling
Physical world vs. abstract math modeling
Visualization
Analytics
Equipment
Simulations
Connectivity
Steady State H
Boundary
M2M
Topology
Geographical
State
Graphics
Measurements
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Flow Diagram (Not complete :_))
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More Requirements…
• Merging networks
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Substation + feeders
Transmission + subtransmission
Subtransmission + distribution
Micro‐grids + transmission
• Visualization:
• Geographical
• Single‐line schematics
• Scaling with zooming • Node/breaker to bus/branch (NTP) is similar to connectivity nodes to topological nodes
• Requires to be identical for unique forward/reverse mapping
• Using topological nodes as buses may complicate mapping process
• SSH updates
• Retained switches
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Advanced Network Data Layers & Analytics
DIFF/All
Reverse Mapping to N/B
EQ_DIFF
3‐φ Load Flow Volt/VAR & CVR
SSH/SV/MS
3‐φ State & Load Forecast
Volt/VAR & CVR
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Actions/ Alerts/ Processes
Time‐series Analysis
Enhanced
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MS/SSH
3‐φ State
As‐operated
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EQ/CN
CIM
3‐φ Topology As‐built
networks
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Distribution State Estimation (DSE)
 DSE consistently and numerically integrates granular nodal (bus) data from different sources (SCADA, MDM, etc.)
– Filter the errors in the SCADA and smart meter data
– Use different weights to differentiate between different levels of reliability of data
– Impose non-linear power flow constraints
– Calculate an accurate state of the network to run power flows (discover any violations, reverse flows), CVR, Volt/VAR
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More Challenges
• Network topology processing
• Needs to be consistent at different layers/functions
• If a bus/branch TSO x network model is to be combined with a node/breaker TSO y model
• TSO y would go through a traditional NTP
• This may not necessarily correspond to a former TP representation of TSO y • ENTSO‐E IOP CIM Profiles 2013‐14
• ENTSO‐E Interoperability (IOP) tests used only Bus‐Branch models of test networks prior to 2014 (UCTE DEF version 2)
• In 2014, ENTSO‐E introduced Node‐Breaker test models in addition to bus‐branch models (CGMES) • Boundary profile group
• As multiple network models used in the simulations/calculations increase, so do the complications for retaining data in both node/breaker & bus/branch models
• Multiple sequential SE runs require mapping between node/breaker (N/B) & bus/branch (B/B) models to persist accurately •
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Multiple injection measurements
Zero‐injection measurements
First run is a conventional SE
Second run is a more detailed analysis
During the whole sequence, N/B  B/B mapping needs to persist and remain consistent
• Visualization
• Needs both node/breaker & bus/branch to be consistently available
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???s
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