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OpenFIDO

Develop a data interchange and analysis framework to exchange information between different power systems tools, enhancing resource integration and accelerating California's climate change mitigation goals.

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OpenFIDO

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  1. OpenFIDO Berk Serbetcioglu (SLAC) CEC Advanced Simulation Program 6 September 2019 This presentation was prepared with funding from the California Energy Commission under grant EPC-17-047. SLAC National Accelerator Laboratory is operated for the US Department of Energy by Stanford University under Contract No. DE-AC02-76SF00515

  2. Overview • Motivation • Approach • Preliminary Results • Next Steps External datasets Import & Validate (OpenFIDO) CSV Files External models Simulate (HiPAS & GLOW) Analyse (custom) Plots Shared datasets Copy/link (Cloud Security) Acknowledgements The LoadInsight project is funded by the US Department of Energy under the Office of Electricity (OE) Advanced Grid Modeling Program and the Office of Technology Transition (OTT) Technology Commercialization Fund (TCF). MySQL Data Shared models

  3. Problem Solution Goals Difficult exchange of power systems data between: • Utilities • Customers • Consulting Engineers • Regulators limits the growth of resources and places a limit on the speed California can reach its climate change mitigation goals Create a data interchange, synthesis and analysis framework that provides information exchange between different power systems tools such as: • CYME • GridLAB-D • OpenDSS • Opal-RT • RTDS • Produce data integration framework (including UI) • Establish foundation for long-term data integration and exchange system • Raise barrier for fast resource integration • Increase speed California can reach its climate mitigation goals

  4. OpenFIDO Workflow [AMI] Import Loads Build GLM {Loads} {Settings} [SCADA] Modify GLM {GLM} Import Network Validate GLM Ok? {Network} [CIM] (GLOW)

  5. Importing existing load data for simulation • Residential Building Stock Assessment (RBSA) • By Northwest Energy Efficiency Alliance (NEEA) for Bonneville Power Administration (BPA) • Originates from Pacific Northwest region only • Data projected to California cities using weather sensitivity analysis • Original data set from 2012-2013 • Updated data set(s) coming • Commercial End-Use Survey (CEUS) • Commercial building loads developed by Itron for CEC • Supports all regions of California • Based on simulations calibrated to measurements • Original data set from 2002 • Updated data set(s) coming • Currently being used to develop and validate NERC Planning Load Models

  6. Constant across pipelines RBSA Pipeline Pipeline Specific [Device map] [Configurable] [RBSA config] Extract RBSA {RBSA data} Clean RBSA {RBSA loads} Group Devices {Site loads} [Sensitivity Analysis Parameters] Group Zipcodes {Zipcode map} Group Sites {TMY data} Zipcode Correlation [City list] {Area loads} {TMY map} {Loadshapes} Find Sensitivities [NOAA Data] Index Heat/Cool {HeatCool index} [Temperatures] Project Loadshape [Electrification Fractions] {Normal loads} Group Enduses {Total loadshapes} Discount Gas {Enduse loadshapes} Normalize [Normalizing Rules] {Enduse loads} [Electrification Fractions] {Normal loadshapes} Undiscount Gas [Daytype Definitions] {Residential Composition} Extract Daytypes {Total loads} [Normalizing Rules] Normalize

  7. Example output

  8. Constant across pipelines CEUS Pipeline Pipeline Specific [Enduse map] [Configurable] [CEUS config] Extract CEUS {CEUS data} Clean CEUS {CEUS loads} Group Enduses {Enduse loads} [Electrification Fractions] Undiscount Gas [Sensitivity Analysis Parameters] {TMY data} Zipcode Correlation [City list] {Enduse loads} {TMY map} {Loadshapes} Find Sensitivities {NOAA data} [Temperatures] Project Loadshape [Electrification Fractions] {Enduse loads} Normalize {Total loadshapes} Discount Gas {Enduse loadshapes} Normalize [Normalizing Rules] [Normalizing Rules] {Enduse loadshapes} [Daytype Definitions] {Commercial Composition} Extract Daytypes

  9. Example output

  10. Climate Sensitivity Analysis

  11. Electrification Base Target Gas Total Total Gas Electric Electric Electric Electric Undiscount Gas Projection Discount Gas

  12. Constant across pipelines Feeder Composition Pipeline Pipeline Specific [Configurable] Done internally by GridLAB-D {Enduse Components} Apply RofA {Residential Composition} {Enduse Components} Apply RofA {Commercial Composition} [Rules of Association] [Rules of Association] [Building Type Mix] Mix Building Types [Building Type Mix] Mix Building Types {Residential Components} {Commercial Components} [Feeder mix] Compose Feeder {Feeder Composition}

  13. Feeder Composition Suburban Mix Rural Mix

  14. Importing/exporting network models for simulation • Common Information Model (“CIM” IEC Standards) • CIM → GridLAB-D (GLM) • Conversion based on PNNL GridAPPS-D toolkit • Current process is user-intensive • Automation is work-in-progress • GLM → CIM • Export to CIM is not required in current workflow • Highly useful to help validate the import process • GridLAB-D currently supports output to JSON • JSON ←→ CIM converter is current being investigated • Current JSON → OpenDSS → CIM is user intensive

  15. Example output On its way

  16. Next Steps • Simplified Framework • User Interface • Cloud Based • Distribution • https://github.com/slacgismo/loadinsight • Help from TAC Members • Testing and validation • Addition of new/useful file formats • Contribution of sample datasets (NDA or public)

  17. Acknowledgements • Lawrence Berkeley National Laboratory • Joe Eto • Bonneville Power Administration • Dmitry Kosterev • Anthony Faris • Andres • North American Electric Reliability Corporation • Mohamed Osman • Numerous engineering staff from the following organizations:WECC, RF, PJM, NPCC, MRO, SPP, ERCOT, FRC, SERC

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