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Model-Centric Smart Grid for Big Data

This model-centric smart grid approach uses an integrated system model to analyze and manage utility data sets, including load forecasting, transmission, distribution, fault, reliability, outage management, substation models, GIS, renewable generation, device settings, customer load, SCADA/EMS/PMU data, and weather data. The model-centric approach allows for proactive modeling, holistic solutions, and finding solutions for hard problems.

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Model-Centric Smart Grid for Big Data

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  1. Model-Centric Smart Grid for Big Data August 5, 2015 Robert Broadwater dew@edd-us.com

  2. Utility Data Sets 1-Analysis models (load forecasting, transmission, radial distribution, heavily meshed network, power flow, fault, reliability, transient, outage management) 2-Substation models 3-GIS 4-Renewable generation 5-Device settings (control settings, protective devices) 6-Customer load (monthly, demand, AMI) 7-SCADA/EMS/PMU data 8-Outage data 9-As-Is Equipment List 10-Weather Data …..

  3. Model-Centric Smart Grid • The model-centric approach employs a holistic, construction detail, model of the physical system – “Integrated System Model (ISM)” • All measurement data, including weather data, is related to the ISM • Changes paradigm of “pushing data to algorithms” to “pushing algorithms to data”

  4. ISM Features • Model which includes everything needed for simulating scenarios that engineers, operators, and field personnel talk about • Allows any data/measurement set to be attached • Allows any calculation to be attached • Different calculations may work together as a team • Community maintained and shared model • Emerging problem to solve – use ISM • Move from “craftsman modeler” to “manufactured model” • Proactive modeling with Circuit Server

  5. Integrated System Model Merge different construction models together, relating all measurements “Aha” understanding Model for holistic solutions, not point or scenario based solutions

  6. The Best Equivalent Is No Equivalent Every model simplification leads to elimination of scenarios

  7. Model-Based Decisions Our ability to solve a problem depends upon the model we have to solve the problem ProblemDomain SolutionDomain Model Point solutions or scenario based solutions; Alphabet soup of systems with scattered data Makes it possible to find a solution that satisfies all scenarios

  8. Finding Solutions for the Hard Problems Physical System Model Big Data Big Model Big Analysis Analysis extends beyond that which is possible with Data Analytics

  9. Model-Centric Smart Grid Equation Reliability, Efficiency, Capacity, Protection, Controllability Performance Analysis + Economic Analysis + Lab Testing + Field Validation = Model-Centric Smart Grid

  10. Silo’edOrganizations with Disjoint Models Suppose data sets contain terabytes?

  11. ISM “Living Model” Organization Eyes of all experts on the same model Moves modeling from “age of modeling craftsman” to “manufactured models” created and used by many Push algorithms to data

  12. Incremental CBA Reliability goal at least cost CoordinatedControl Auto Reconfiguration, Monte Carlo Efficiency and energy reduction policy goals Distribution Automation (blue sky days)(storm conditions) Coordinated Control Efficiency and energy reduction policy goals Capacitor Design(capacitors on local control) Cap Design for Time Varying Load Phase-Balance(no capacitors) Efficiency policy goal Base System(not optimized, some capacitors) Phase Balance for Time Varying Load “Dependency Ordering” of Investments

  13. Big Analysis: Algorithms Working Together Monte Carlo Driven Reliability Analysis Restoration Analysis Protection / Coordination Outage Data Contingency Analysis Fault Analysis Power Flow Load Estimation Model Validation SCADA Measurements Customer Load Data Confidential

  14. Graph Trace Analysis for the ISM Graph Trace Analysis with Edge-Edge Graph Matrix Analysis with Edge-Node Graph  1 2 2 Topology Iterators 3 1 3   1 2 3 5 4  5 4 Transform 4 Computer Processing Global View Local View • Edge knows neighboring edges • Topology continuously maintained • Algorithms with topology iterators • Write KVL and KCL directly • Processing time required for configuration changes is independent of system size

  15. Common Analysis Architecture Point Solution App 1 Creation of simplified models App 1 Model Core Models Interfaces? Topology Management SCADA Data App 2 Customer Load Data App 2 Model Pushing measurement data

  16. ISM Analysis Architecture Mass Storage Memory App 1 ISM edge-edge topology Topology iterators, sharing of results, measurements Customer Loads ISM SCADA Measurements Weather Measurements App 2 Interface provided by ISM to applications

  17. ISM Model Management for Distributed Computation Environment Client: Fault Location Supports distributed computations ISM Model Server Model Queue Analysis Processes Client: Reconfiguration

  18. Summary: Some Uses of ISM Effects on transmission system of high renewable penetration at distribution level Automated renewable generation screening analysis Weather dependent load forecast that takes into account renewable generation forecast Storm outage prediction with radar-weather data, … System reliability from analysis team of Monte Carlo, Power Flow, and Restoration Distribution solutions versus transmission solutions Time series driven, CBA of smart grid investments

  19. Generic Programming Roots of GTA Algorithms that process objects in container, independent of object type Container with Iterators CS Algorithms Generic Programming Algorithms that process edges or components of graph Graph Trace Analysis Engineering Algorithms ISM with Topology Iterators Generic analysis independent of system type - electric, gas, fluid, etc.

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