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Dtech 2015 the distribution management system network model

The presentation will illustrate the methodology deployed to achieve an accurate Distribution Network Model at Duke Energy Carolinas. It will also dive in to the impact on various stakeholders in the organization, as well as the change management process that drives the successful implementation of the model.

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Dtech 2015 the distribution management system network model

  1. 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. 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 2
  3. 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. 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. 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. 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. 7. A Typical DSDR Load Shape Begin DSDR at 3:00 pm, Finish at 6:00 pm 7
  8. 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. 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. 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. 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! 11
  12. 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 12
  13. 13. Track DNM Quality over Time 13
  14. 14. Commission SE and Closed Loop Functions 14 Software Project to Upgrade DSCADA and Place DMS in Production Iterative Process to Commission SE and DSDR
  15. 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 15
  16. 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 16
  17. 17. Real Time Data is Used to Improve State Estimation 17 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. 18. Real Time Data is Used to Improve State Estimation 18 • 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. 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 19
  20. 20. 20 Michael B. Johnson, PE MichaelB.Johnson@duke-energy.com Tom Christopher tom.christopher@schneider-electric.com

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