The document outlines essential strategies and considerations for implementing a Distribution Management System (DMS) network model, emphasizing the importance of accurate distribution network models (DNM) for optimizing results. It discusses the integration of DNM with various operational systems and the need for stakeholder engagement and change management. Key points include the impact of model accuracy on performance and the necessity of real-time data to enhance state estimation and system optimization.

1. The Distribution Management System Network Model
The Cornerstone of a Successful DMS Implementation
Michael B. Johnson, PE
Project Director Grid Solution
Duke Energy
Tom Christopher
VP, Global Customer Relations, Smart Grid IT
Schneider Electric
1
February 5, 2015

2. Distribution Network Model (DNM)
Key Points
 Confidence in DNM is crucial to achieving optimized results
 Getting the DNM right can make or break a project
 DNM requires integration with GIS, OMS, SCADA and CIS
 Requires stakeholder engagement and change management
 Real time State Estimation (SE) has been commissioned at
Duke Energy as part of the DSDR Carolinas Project
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3. Duke Energy
 Electric Customers: 7.1 Million
 Gas Customers: 500,000
 Market Cap: $49 Billion
 Employees: 29,250
 Service Territory: 104,000 sq mi
 Generation Capacity: 49,600 MW
 Transmission Lines: 32,000 mi
 Distribution Lines: 250,200 mi
Duke Energy International operates 4,300 MW’s
of generation
3

4. Duke Energy Progress & DSDR
(Distribution System Demand Response)
4
•Deployed on entire distribution
grid
•Controllable load: 8,400 MWs peak
•315 substations
•1,150 feeders
•1.5 million customers
•34,000 square miles of service area
Duke Energy Progress Statistics

5. The DSDR Business Case
5
Resource
Planning
Generation TransmissionSystem Operations
/ Dispatch
Fuel /
Purchased Power
Customer
Optimizing the Energy Value Chain
Distribution
Investment in
T&D eliminated
the need to build
235 MWs of new
peaking plants

6. DSDR Principles of Operation
6
Existing
Flattened Profile after feeder conditioning
Lower
Regulatory
Limit
Upper
Regulatory
Limit
• Flattened profile allows greater voltage reduction
• Dynamically lower voltage to regulatory limit
o
DMS network model used to maximize voltage reduction over time
o
Each regulating zone and each phase is optimized independently
Lower Voltage to Reduce MWs
Feeder
Voltage
Feeder Distance

7. A Typical DSDR Load Shape
Begin DSDR at 3:00 pm, Finish at 6:00 pm
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8. DNM Accuracy Affects Performance
8
DMN accuracy can substantially impact how
much risk you take when moving voltage to the
regulatory limit
0.5 Volt range of error
could affect DSDR
benefit by 15%!

9. • Integrate with multiple business applications
• “Feed the DMS beast” both with real-time information and historical information
• Fast real-time feedback from the field is key to optimizing the system
Integrations Needed
9
CIS
Report
Analysi
s

10. 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
20122008 2009 2010 2011
PLAN & DESIGN DSDR
CONDITION 1,100 FEEDERS to DSDR STANDARDS (MV Network)
INSTALL SUBSTATION ELECTRONICS and
CONTROLS (360 subs)
INSTALL FEEDER CONTROL DEVICES
(7800 devices)
OPTIMIZE SECONDARIES (LV Network)
COMMISSION EACH SUB
INSTALL DMS
Phase 1
Upgrade Legacy
DSCADA
MW OPTIMIZATION
BUILD IT and TELECOMMUNICATIONS INFRASTRUCTURE
High Level Project Plan
10
2013 2014
1 2 3 4 1 2 3 4
INSTALL DMS
Phase 2
COMMISSION EACH FUNCTION
DESIGN MODEL, INTEGRATE DATA
Approx 10 man-years
were needed to
achieve good DNM
Quality

11. Build Initial DNM
 Need a cross functional team
 IT (Architecture, Reporting, Support)
 Business SMEs (Control Room Operators, Engineers)
 Vendor
 Develop substation one line diagrams for DMS
 Validate data in the field – phasing, wire size, transformers
 Replace erroneous data – transformer pole number
 Add missing data – regulator tap position, low voltage network
 Add customer load profiles, CVR ratios
 Data import process will generate many errors to be cleaned!
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12. How do you Measure Model Quality?
 Capture the delta between state estimation results and actual
data from sensors
 Create boundaries for good results, i.e.
 Voltage <2% difference
 Reactive Power < 600 kvar difference
 Track performance of each sensor point over time
 Track performance of each feeder/substation over time
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13. Track DNM Quality over Time
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14. Commission SE and Closed Loop Functions
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Software Project to
Upgrade DSCADA and
Place DMS in
Production
Iterative Process
to Commission
SE and DSDR

15. Stakeholders Maintain DNM Accuracy
 The DNM brings lots of change to the control room!
 Integration with OMS model is crucial to maintaining accuracy
 Requires real-time data flows between OMS and DMS
 Processes in the control room must be changed
 Switching, restoration, power factor management, etc.
 Maintain status of breakers, reclosers, switches in real time
 Grid Technicians monitor status of devices in real time
 Perform initial troubleshooting
 Maintain high availability of regulators, sensors, capacitors
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16. DNM Requires Focus from the Whole
Organization
 Process changes are needed from many stakeholders to ensure data
is managed well
 Work Order Design, Construction, GIS Techs, Engineering, IT
 Because many organizations are affected the timeline will be longer
than you’d like
 Start process development early and include change management
resources
 You should assume that bad/missing data will happen:
 Improve processes
 OR correct it during model import process
 OR your DMS will manage it in real time
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17. Real Time Data is Used to Improve State Estimation
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Switch
Router
Distribution Feeder
Cap Bank
Recloser
(Sensor data)
VR
Regulator
S
Sensor
DSDR Substation
Cap Bank
SEL
Feeder
Breaker
S
Voltage
Regulato
r
VRC
Gateway
Telecom
Cabinet
PQ Meter
• Each Sensor sends status and analog data to DMS in 10 to 60
second intervals
• Real Power, Reactive Power, Voltage and Current
• Tap Position, Switch Status
• 3,500 Regulators
• 2,800 Line Capacitors
• 1,500 MV sensors
• 800 Reclosers
• 3,000 LV sensors
Sensor

18. Real Time Data is Used to Improve State Estimation
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• SCADA database has approx. 400,000 points
• 90,000 of those points are used by State Estimation
• 30,000 points – Voltage
• 15,000 points – Current
• 18,000 points – Real Power
• 18,000 points – Reactive Power
• 8,000 points – Power Factor
• That’s an average of 4 to 5 sensing locations per feeder
which typically serves > 1,000 customers
• When DSDR is not active, DSE and optimization
algorithms operate every 15 to 25 minutes

19. Conclusions
 The DNM was crucial to our effort to provide 310 MW
 Confidence in DNM quality was achieved through:
 Dedicated project resources were used to build initial model
 Real time data from sensors in the field
 Integration with GIS, OMS, SCADA and CIS
 DMS functions must assume the DNM is not perfect!
 Measure model quality over time
 Stakeholders must be engaged throughout the process
 Implement process change to keep the DNM accurate
 Implement change management to keep everyone informed
 Commission the network in stages to reduce impact to the control
room
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20. 20
Michael B. Johnson, PE
MichaelB.Johnson@duke-energy.com
Tom Christopher
tom.christopher@schneider-electric.com