Synchrophasor Applications Facilitating Interactions between Transmission and Distribution Operations
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The document discusses the development of an ideal grid for synchrophasor applications, focusing on the increasing need for observability in the transmission and distribution networks due to renewable energy sources. It presents a methodology for utilizing Phasor Measurement Units (PMUs) to extract dynamic information and improve data handling, along with the introduction of the Khorjin gateway for protocol conversion between IEEE C37.118.2 and IEC 61850-90-5. The presentation also highlights various software development tools, including the Smart Grid Synchrophasor Development Kit (S3DK), to facilitate real-time data management and application development.
Synchrophasor Applications Facilitating Interactions between Transmission and Distribution Operations
- 1. ideal grid for all Synchrophasor Applications Facilitating Interactions between Transmission and Distribution Operations Luigi Vanfretti Associate Professor, Docent KTH Royal Institute of Technology, Stockholm, Sweden https://www.kth.se/profile/luigiv Thursday February 9th 2017
- 2. ideal grid for all Acknowledgements • The work in this presentation is result of the research carried @KTH SmarTS_Lab in the FP7 IDE4L project (2014-‐2016) and other projects form 2011-‐2016. • Special thanks to Dr. Hossein Hooshyar, who undertook the day-‐to-‐day project management of this project at KTH SmarTS Lab. with great dedication. • All of the following students contributed to this presentation with their hard work! 29/11/2016 WWW.IDE4L.EUSLIDE 2 Hossein Reza AliFarhan Ravi Narender Maxime Jan
- 3. ideal grid for all Outline • Introduction: PMUs in distribution networks? • PMU Application Use Case Definition using SGAM • Methodology and Experimental Lab Testing • Development, Implementation and Testing using RT-‐HIL Simulation • The Reference Distribution Grid Model • Handling PMU Data: • Getting the data: The Khorjin Gateway and The S3DK Toolkit • Cleaning the data: PMU Data Feature Extraction • Using the Data: Applications • Steady State Model Synthesis • Dynamic Line Rating for Distribution Feeders • Small-‐Signal Dynamic Analysis • Conclusions 29/11/2016 WWW.IDE4L.EUSLIDE 3
- 4. ideal grid for all Introduction (1/2) • It is becoming necessary to increase observability between T&D grids, emerging dynamics active distribution networks due to renewables will lead to • Fast changing conditions in the network • Fast behavior of components • Traditional monitoring technology not capable of satisfying the technical requirements to meet these conditions:types of signals, time-‐ synchonization and speed of data acquisition • There is great potential of utilizing real-‐time Synchrophasor data from PMUs (Phasor Measurement Unit(s)) • From different actors, voltage levels and across conventional operation boundaries. • to extract key information related to fast changing conditions & dynamic behavior, • To use such information adequately, a properly designed and implemented architecture needs to be considered. SLIDE 4 29/11/2016 WWW.IDE4L.EU
- 5. ideal grid for all Introduction (2/2) • The IDE4L architecture is built upon the 5-‐layer Smart Grid Architecture Model (SGAM) framework. • The main inputs to the architecture design are the use cases. • The IDE4L use case, including the PMU-‐based functions for dynamic information extraction and exchange, is presented in this presentation. SLIDE 5 29/11/2016 WWW.IDE4L.EU
- 6. ideal grid for all DistributionTrans. ProcessFieldStationOperationEnterprise PMU (distributed) Transducer (PS/SS/distributed) Electrical conversion (PS/SS/distributed) Synchrophasor calculation (distributed) Synchrophasor Calculation (PS/SS) PMU (PS/SS) Measurement acquisition (PS/SS) Communication interface (PS/SS) Data concentration (PS/SS) PDC (PS/SS) DMS at DSO Data curation and extraction of components Partial derivation of key information Data transfer TSO Computation unit (PS/SS) Measurement acquisition Communication interface Data curation and extraction of components Derivation of key information Data export Gen. DER Cost. Prem. • Data flows from PMUs => PDC => Super PDC => DMS computers (where dynamic information is extracted) and exchanged with the TSO (possible also w. DSOs). • Data processing and information extraction can occur at both the station (partially) and the operation (i.e. DMS computers) zones. • Actors: Instrumentations, PMU, PDC, communication interface, DMS, and TSO • Functions: electrical conversion, synchrophasor calculation, data acquisition, data concentration and time-‐ alignment, data exporting, data curation, and derivation of key information out of the data (aka PMU applications). SLIDE 6 29/11/2016 WWW.IDE4L.EU Use Case Definition
- 7. ideal grid for all • The development of the applications has been carried out using real-‐time hardware-‐ in-‐the-‐loop simulation consisting of: • A real-‐time simulation model of active distribution networks. • The real-‐time simulation model is interfaced with PMUs in HIL. • PMU data is streamed into a computer workstation through PDC. SLIDE 7 29/11/2016 WWW.IDE4L.EU Development, Implementation and Testing using RT-‐HIL Simulation • The computer makes available to specialized software development tools within the LabVIEW environment. • All data acquisition chain is carried out using the corresponding PMU standards.
- 8. ideal grid for all SLIDE 8 29/11/2016 WWW.IDE4L.EU The Reference Distribution Grid Model (1/2) 101 100 102 103 104 105 106 800 802 806 808 810 812 814 850 816 818 820 822 824 826 828 830 854 856 852 832 858 864 834 842 844 846 848 860 836 840 862 838 888 890/799 701 + - + - 702 713 704 714 718 725 706 720 707 722 724 705 712 742 703 727744 728 729 730 709 731708732 775 733 734 736 710 735 737 738 711 741 740 0.4kV 220 kV 36 kV 36kV 36kV 6.6 kV 6.6 kV 6.6kV Electric power system ZIP load Motor load Voltage regulator Wind farm PV farm Residential PV system Battery storage + - Capacitor bank Circuit breaker Overcurrent protection Recloser Legend Component Number of units 1 three-phase unit 11 three-phase units, 56 single-phase units 2 three-phase units, 3 single-phase units 3 sets each consisting of 3 single-phase units 3 three-phase units 2 three-phase units 3 single-phase units 2 three-phase units 11 single-phase units 1 three-phase units 1 set consisting of 3 phase relays and 1 ground relay 1 three-phase unit, 2 sets each consisting of 3 single-phase units Rated Voltage Number of branches 220 kV 7 three-phase 36 kV 28 three-phase, 8 single-phase 6.6 kV 39 three-phase HV MV LV 0.4 kV 1 three-phaseRLV No. of buses 6 34 37 2 Roy Billinton Transmission Test System IEEE 34 bus test feeder IEEE 37 bus test feeder RLV DG and Load Steam turbine Governor ωm PSS Excitation System V dω System load Output feeder
- 9. ideal grid for all The Reference Distribution Grid Model (2/2) • Runs on 4 cores using the State-‐Space-‐Nodal (SSN) solver with time-‐step of 100μs. • The two different implementations of the grid model can be downloaded from the KTH SmarTS Lab GitHub repository at • The SSN implementation is also available in the ARTEMiS 7.0.5 demo section of Opal-‐RT. SLIDE 9 29/11/2016 WWW.IDE4L.EU https://github.com/SmarTS-‐ Lab/FP7-‐IDE4L-‐KTHSmarTSLab-‐ ADN-‐RTModel
- 10. ideal grid for all Outline • Introduction: PMUs in distribution networks? • PMU Application Use Case Definition using SGAM • Methodology and Experimental Lab Testing • Development, Implementation and Testing using RT-‐HIL Simulation • The Reference Distribution Grid Model • Handling PMU Data: • Getting the data: The Khorjin Gateway and The S3DK Toolkit • Cleaning the data: PMU Data Feature Extraction • Using the Data: Applications • Steady State Model Synthesis • Dynamic Line Rating for Distribution Feeders • Small-‐Signal Dynamic Analysis • Conclusions 29/11/2016 WWW.IDE4L.EUSLIDE 10
- 11. ideal grid for all IEEE Std C37.118.2 Today’s Architecture Deployment Time Guesstimate: ~15-‐20 Years Transition Likely Future Scenario Ø Security Not Addressed IEEE Std C37.118.2 Application System PDC PMU 1 PMU 2 PMU n PDC PDC PDC Communication Network PMU 1 PMU 2 PMU n PDC PDC PDC IEC 61850-‐90-‐5 Application System PDC Ø Fulfills the Gaps Not addressed in the IEEE Std C37.118 e.g. Security Enhancement Ø Harmonization with IEC 61850 Ø Security Not Addressed v Two Segregated Systems ßà Two Protocols (Even in the same substation) v It will be a huge CHALLENGE to adopt IEC 61850-‐90-‐5 Standard v Need of Interfaces v @PMUs, @PDCs, @App Sys,… à How to maintain this ? Getting the Data… in the Advent of IEC TR 61850-‐90-‐5 Standard Specification Application System PDC PMU 1 PMU 2 PMU n PDC PDC PDC Communication Network
- 12. ideal grid for all One possible approach and our contribution: Khorjin • A Gateway: • Can act as the IEEE C37.118.2 to IEC 61850-‐90-‐5 protocol converter. • Providing future compatibility for legacy devices already in the field • Capable of being used at various levels: • @PMU Level • @PDC Level • @Application Level • … • Gateway Application and Implementation Test: Wide-‐Area Controller Application System Application System PDC PMU PDC Communication Network IEEE C37.118.2 Compatible IEDs and APPs IEC 61850-‐90-‐5 Gateway
- 13. ideal grid for all Seyed Reza Firouzi srfi@kth.se Receiver Gateway National Instrument CompactRIO à PMU Wireshark captures IEC 61850-90-5 R-SV / R-GOOSE Khorjin Gateway Testing • Test of the gateway implementation was tested using real-‐time HIL simulation • Data is transformed from IEEE C37.118.2 from actual PMUs, and published using the IEC 61850-‐90-‐5 protocol
- 14. ideal grid for all Khorjin Gateway Architecture • The Gateway functionality of “Khorjin” library is getting use of its modular architecture: à Easier for future development • The Khorjin Gateway is designed and implemented in three main components of: 1) IEEE C37.118.2 Module, 2) IEC 61850 Mapping Module, and 3) IEC 61850-‐90-‐5 R-‐SV / R-‐GOOSE Publisher Module. 14 • In order to be platform-‐independent: à A Platform Abstraction Layer is Implemented. à Depending on the platform, the relevant platform-‐dependent functions are utilized (i.e. Socket, Thread, Time and …). • The Khorjin Gateway is designed and implemented in three main components of: 1) IEEE C37.118.2 Module, 2) IEC 61850 Mapping Module, and 3) IEC 61850-‐90-‐5 R-‐SV / R-‐GOOSE Publisher Module.
- 15. ideal grid for all • The Khorjin library is an Open Source code providing following functionalities: à IEEE C37.118.2 Traffic Parser à IEEE C37.118.2 to IEC 61850-‐90-‐5 Protocol Converter à IEC 61850-‐90-‐5 Traffic Generation à Routed-‐Sampled Value à Routed-‐GOOSE à IEC 61850-‐90-‐5 Traffic Parser à Routed-‐Sampled Value à Routed-‐GOOSE • The Khorjin library supports different platforms. à Will be publicly available sometime in 2017, keep an eye on our Github repo: https://github.com/SmarTS-‐Lab-‐Parapluie Khorjin Windows Linux Mac NI cRIO Raspberry Pi 15 Khorjin Functionalities – it’s more than a gateway!
- 16. ideal grid for all • PMU SW Apps require real-‐time data acquisition. • PMU data is sent to these SW Apps using many different comm. protocols. • For fast software prototyping and testing, communication protocol parsing is required. • Low level data management routines (windowing, etc.) do not need to be reinvented N times. • A tool-‐set is needed to assist students and researchers with a background in power systems, but lackingproficient software development and programming skills. PMU 1 PMU 2 PMU n PDCCommunication Network Infrastructure Data in IEEE C37.118 Protocol Real-‐time data locked into vendor specific software system Historical Data in Proprietary Database Interfaces using standard protocols and a flexible development environment are needed Last Mile in PMU App Development 16 The next step… Now you got the data… how do you implement an application?
- 17. ideal grid for all S3DK Smart grid Synchrophasor Software Development tool-‐Kit (SDK) • Infrastructure (external). • Software Development Kit (SDK): a set of different computer software that allows a user to develop other derived software applications. • Our SDK is composed by two main pieces: • Real-‐Time Data Mediator (DLL) • PMU Recorder Light (PRL) Computer/Server S3DK Real-‐Time Data Mediator (RTDM) “DLL” LabView PMU Recorder Light (PRL) PMU 1 PMU 2 PMU n PDCCommunication Network Infrastructure Data in IEEE C37.118 Protocol
- 18. ideal grid for all S3DK’s Real-‐Time Data Mediator a.k.a. the “DLL” • A library in C++ was implemented with the following architecture design in mind Client Applications (PRL) Synchronization Layer IEEE C37.118 Server IEC 61850 Server Other protocols Only prepared, not fully implemented. Future Support / Integration IEEE C37.118 Client IEC 61850 Client Other protocols Console Test Tool PMU or PDC 1 PMU or PDC n Interface Implemented and Tested Other Protocol Servers Clients Servers IEEE C37.118 DLL
- 19. ideal grid for all 19 GUI Many blocks to access and handle RT data! Graphical User Interfaces Communication Configuration Development Tooling S3DK – LabView API, UI and Tools
- 20. ideal grid for all S3DK’s LabVIEW Function Library/Toolbox/Pallete (aka PRL)
- 21. ideal grid for allRepositories Currently Available at GitHub! • Main repo: https://github.com/SmarTS-‐Lab-‐Parapluie/ • S3DK: https://github.com/SmarTS-‐Lab-‐Parapluie/S3DK • BabelFish: https://github.com/SmarTS-‐Lab-‐Parapluie/BabelFish • Khorjin: Will be available at GitHub sometime in 2017. L. Vanfretti, V. H. Aarstrand, M. S. Almas, V. S. Perić and J. O. Gjerde, "A software development toolkit for real-‐time synchrophasor applications," PowerTech (POWERTECH), 2013 IEEE Grenoble, Grenoble, 2013, pp. 1-‐6. doi: 10.1109/PTC.2013.6652191 L. Vanfretti, I. A. Khatib and M. S. Almas, "Real-‐time data mediation for synchrophasor application development compliant with IEEE C37.118.2," Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, Washington, DC, 2015, pp. 1-‐5. doi: 10.1109/ISGT.2015.7131910 L. Vanfretti, M.S. Almas and M. Baudette, “BabelFish– Tools for IEEE C37.118.2-‐compliant Real-‐Time Synchrophasor Data Mediation,” SoftwareX, submitted, June 2016. S.R. Firouzi, L. Vanfretti, A. Ruiz-‐Alvarez, F. Mahmood, H. Hooshyar, I. Cairo, “An IEC 61850-‐90-‐5 Gateway for IEEE C37.118.2 Synchrophasor Data Transfer,” IEEE PES General Meeting 2016, Boston, MA, USA. Pre-‐print: link. S.R. Firouzi, L. Vanfretti, A. Ruiz-‐Alvarez, H. Hooshyar and F. Mahmood, “Interpreting and Implementing IEC 61850-‐90-‐5 Routed-‐Sampled Value and Routed-‐GOOSE Protocols for IEEE C37.118.2 Compliant Wide-‐Area Synchrophasor Data Transfer,” Electric Power Systems Research, 2017. G.M. Jonsdottir, E. Rebello, S.R. Firouzi, M.S. Almas, M. Baudette and L. Vanfretti, “Audur – Templates for Custom Synchrophasor-‐Based Wide-‐Area Control System Implementations,” SoftwareX, in preparation, 2016.
- 22. ideal grid for all SLIDE 22 29/11/2016 WWW.IDE4L.EU Cleaning the Data! PMU Data Curation, Extraction of Different Time-‐Scale Components
- 23. ideal grid for all PMU Data Processing (1/3) • PMU measurements are normally polluted with • Undesirable noise • Outliers • Missing data • Measurements obtained from PMUs during different events in power systems contain different signal features at different time scales, i.e. features of different types of power system dynamics. • So before being fed to the applications: • PMU measurements should be curated. • Signals required for each application should be extracted from the PMU measurements. SLIDE 23 29/11/2016 WWW.IDE4L.EU
- 24. ideal grid for all PMU Data Processing (2/3) • An enhanced Kalman filtering technique has been utilized for both bad data removal, i.e. data curation, and to extract different time-‐scale dynamics from the PMU data (ie used as a real-‐time filter) • R and Q (the measurement and process noise covariance matrices), can be updated in real-‐time to perform the data processing. SLIDE 24 29/11/2016 WWW.IDE4L.EU Step Equation Significance 1 Prediction 𝑥" 𝑘 − = 𝐴𝑥" 𝑘−1 − + 𝐵𝑢 𝑘 Project the state ahead. 2 𝑃𝑘 − = 𝐴𝑃𝑘−1 𝐴 𝑇 + 𝑄 Project the error covariance ahead. 3 Correction 𝐾𝑘 = 𝑃𝑘 − 𝐻 𝑇( 𝐻𝑃𝑘 − 𝐻 𝑇 + 𝑅)−1 Compute the Kalman gain. 4 𝑥" 𝑘 = 𝑥" 𝑘 − + 𝐾𝑘(𝑧 𝑘 − 𝐻𝑥" 𝑘 − ) Update estimate with measurement zk. 5 𝑃𝑘 = ( 𝐼 − 𝐾𝑘 𝐻) 𝑃𝑘 − Update the error covariance.
- 25. ideal grid for all PMU Data Processing (3/3) • Sample results. SLIDE 25 29/11/2016 WWW.IDE4L.EU 0 20 40 60 80 100 120 140 160 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Time (sec) Vmag(p.u.) Raw data Curated data Steady state component Dynamics 0 20 40 60 80 100 120 140 160 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Time (sec) Vang(rad.) Raw data Curated data Steady state component Dynamics voltage magnitude (per unit) voltage angle (radian)
- 26. ideal grid for all Outline • Introduction: PMUs in distribution networks? • PMU Application Use Case Definition using SGAM • Methodology and Experimental Lab Testing • Development, Implementation and Testing using RT-‐HIL Simulation • The Reference Distribution Grid Model • Handling PMU Data: • Getting the data: The Khorjin Gateway and The S3DK Toolkit • Cleaning the data: PMU Data Feature Extraction • Using the Data: Applications • Steady State Model Synthesis • Dynamic Line Rating for Distribution Feeders • Small-‐Signal Dynamic Analysis • Conclusions 29/11/2016 WWW.IDE4L.EUSLIDE 26
- 27. ideal grid for all Real-‐Time Steady State Model Synthesis of Active Distribution Networks SLIDE 27 29/11/2016 WWW.IDE4L.EU
- 28. ideal grid for all Background • Currently, TSOs maintain reduced models of portions of the distribution networks, however: • The models covers limited portions of the distribution network due to the lack of network “observability” (measurement points) and computational burden associated with simulating large joint T&D models. • The models are not updated frequently. • The reduction methods, used by TSOs, often make assumptions that are no longer valid for active distribution networks. SLIDE 28 29/11/2016 WWW.IDE4L.EU
- 29. ideal grid for all SLIDE 29 29/11/2016 WWW.IDE4L.EU Methodology and Application V1 abc <δ1 abc PMU1 I1 abc <φ1 abc Any feeder configuration with an arbitrary combination of load and DG PMU2 V1 a <δ1 a I1 a <φ1 a I2 a <φ2 a V2 a <δ2 a R a X a R a X a Ra Xa E a <δ a V0 a <δ0 a I0 a <φ0 a The reduced steady state equivalent model for phase ‘a’ 3 3 3 ... V3 abc <δ3 abc I3 abc <φ3 abc 3 PMU3 .. . PMU N VT abc <δT abc PMU at Transmission Level IT abc <φT abc Any feeder configuration with an arbitrary combination of load and DG 3 3 3 V1 abc <δ1 abc I1 abc <φ1 abc 3 PMU at Distribution Level VT a <δT a IT a <φT a I1 a <φ1 a V1 a <δ1 aR a X a R a X a V0 a <δ0 a E a <δ a Ra XaI0 a <φ0 a The reduced steady state equivalent model for phase ’a’ 2R a 2X a The reduced steady state equivalent model for phase ’a’ E a <δ a I2=0 Open-circuited VT a <δT a IT a <φT a With N available PMUs With 1 available PMU • Model parameters are obtained by writing KVL equations across the model branches and equate Vi’s and Ii’s to PMU measurements. Event1 Event2 0 80 1600 80 160 Event1 Event2 Event1 Event2 Event1 Event2 R(p.u.)X(p.u.) Ea (p.u.)δa (rad) Estimated parameters of equivalent model LabVIEW Application
- 30. ideal grid for all SLIDE 30 29/11/2016 WWW.IDE4L.EU Illustration Example (1/2) • Steady state model of a portion of the reference grid is estimated during wind curtailment at different dispatch levels. R X E δ E<δ R X
- 31. ideal grid for all SLIDE 31 29/11/2016 WWW.IDE4L.EU Illustration Example (2/2) • True and reproduced current and voltage phasors are compared using TVE. • The mean TVE is less than 2.5%. 1.5 1.55 1.6 1.65 1.7 1.75 1.8 0 5 10 15 20 25 30 35 40 TVE for V2 (%) Probabilitydistribution TVE for Voltage Phasor at PMU2 empirical generalized pareto generalized extreme value inversegaussian birnbaumsaunders 1 1.5 2 2.5 3 3.5 4 4.5 0 0.5 1 1.5 2 2.5 3 3.5 4 TVE for I1 (%) Probabilitydistribution TVE for Current Phasor at PMU1 empirical generalized pareto generalized extreme value inversegaussian birnbaumsaunders 1.3 1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 0 5 10 15 20 25 30 35 40 TVE for I2 (%) Probabilitydistribution TVE for Current Phasor at PMU2 empirical generalized extreme value generalized pareto inversegaussian birnbaumsaunders 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0 10 20 30 40 50 60 70 80 90 TVE for V1 (%) Probabilitydistribution TVE for Voltage Phasor at PMU1 empirical generalized pareto generalized extreme value beta gamma Variables with hat are reproduced ones.
- 32. ideal grid for all Dynamic Line Rating for Aerial Distribution Feeders SLIDE 32 29/11/2016 WWW.IDE4L.EU
- 33. ideal grid for all SLIDE 33 29/11/2016 WWW.IDE4L.EU Background • Dynamic line rating (DLR) is a way to optimize the ampacity of transmission and distribution lines by measuring the effects of weather and actual line current. • The inputs needed for the method: • Weather data => provided by a close-‐by weather station. • Line loading => provided by PMU! • Real-‐time sag => provided by a GPS-‐based measurement device.
- 34. ideal grid for all SLIDE 34 29/11/2016 WWW.IDE4L.EU Methodology and Application LabVIEW Application
- 35. ideal grid for all SLIDE 35 29/11/2016 WWW.IDE4L.EU • Results are obtained by applying the method on data from a real feeder. • Output shows accurate correlation with different inputs. Sample Results
- 36. ideal grid for all Small Signal Dynamic Analysis of Active Distribution Networks SLIDE 36 29/11/2016 WWW.IDE4L.EU
- 37. ideal grid for all SLIDE 37 29/11/2016 WWW.IDE4L.EU Background • For reliable operation of the power system, oscillations are required to decay. • Accurate and real-‐time estimates of active distribution networks oscillatory modes has become ever so important. • Timely extraction of these modes and related parameters from network measurements has considerable potential for near real-‐time dynamic security assessment.
- 38. ideal grid for all Extracting and Exchanging Small Signal Stability Information 38 Data curation, fusion and extraction of steady state component • Ambient Data Analysis (for stochastic variations) • Ringdown Data Analysis (for transients) TSO Calculation of stability indices Data process and analysis Data process and analysis Data process and analysis Centralized Architecture Decentralized Architecture
- 39. ideal grid for all SLIDE 39 29/11/2016 WWW.IDE4L.EU Implementation Architectures Centralized Decentralized
- 40. ideal grid for all SLIDE 40 29/11/2016 WWW.IDE4L.EU PMU data collection and transmission Dynamic component extraction by Kalman filter Data pre-‐processing (detrending and downsampling) Ringdown detected? Ambient analysis (ARMA) Ringdown analysis (ERA) Modal parameters Modal parameters LabVIEW Application Methodology and Application
- 41. ideal grid for all SLIDE 41 29/11/2016 WWW.IDE4L.EU Illustration Example (1/2) • 4 PMUs were deployed in the reference grid to be used for mode estimation under two different architectures of centralized and decentralized
- 42. ideal grid for all SLIDE 42 29/11/2016 WWW.IDE4L.EU Illustration Example (2/2) Centralized Decentralized Inter-‐area oscillatory mode between HV section and rest of the grid Forced local oscillatory mode detectable in decentralized architecture
- 43. ideal grid for all References and Resources of work presented and related to this presentation • Github Repositories (Open Source Software Resources) • https://github.com/SmarTS-‐Lab/ • https://github.com/SmarTS-‐Lab-‐Parapluie (PMU applications and software tools) • Under Review: • N. Singh, H. Hooshyar and L. Vanfretti, “Feeder Dynamic Rating Application for Active Distribution Networks using Synchrophasors,” Sustainable Energy, Grids and Networks. (under third review) • F. Mahmood, H. Hooshyar, and L. Vanfretti, “Sensitivity Analysis of a PMU-‐Based Steady State Model Synthesis Method,” IEEE PES GM 2017. • R.S. Singh, H. Hooshyar, and L. Vanfretti, “Real-‐Time Testing of a Decentralized PMU Data-‐Based Power System Mode Estimator,” IEEE PES GM 2017. 29/11/2016 WWW.IDE4L.EUSLIDE 43
- 44. ideal grid for all References and Resources of work presented and related to this presentation • Published/Accepted for Publication: • S.R. Firouzi, L. Vanfretti, A. Ruiz-‐Alvarez, H. Hooshyar and F. Mahmood, “Interpretation and Implementation of IEC 61850-‐90-‐5 Routed-‐ Sampled Value and Routed-‐GOOSE Protocols for IEEE C37.118.2 Compliant Wide-‐Area Synchrophasor Data Transfer,” Electric Power Systems Research, 2017. • F. Mahmood, H. Hooshyar, J. Lavenius, P. Lund and L. Vanfretti, “Real-‐Time Reduced Steady State Model Synthesis of Active Distribution Networks using PMU Measurements,” IEEE Transactions on Power Delivery, 2017. http://dx.doi.org/10.1109/TPWRD.2016.2602302 • F. Mahmood, H. Hooshyar and L. Vanfretti, "Extracting Steady State Components from Synchrophasor Data Using Kalman Filters," Energies, vol. 9, 2016. • H. Hooshyar, F. Mahmood, L. Vanfretti, and M. Baudette “Specification, Implementation and Hardware-‐in-‐the-‐Loop Real-‐Time Simulation of an Active Distribution Grid,” Sustainable Energy, Grids and Networks (SEGAN), Elsevier, vol. 3, Sept. 2015, pp. 36-‐51. Available in open-‐ access: https://dx.doi.org/doi:10.1016/j.segan.2015.06.002 • H. Hooshyar, L. Vanfretti and C. Dufour, “Delay-‐Free Parallelization for Real-‐Time Simulation of a Large Active Distribution Grid Model,” IEEE IECON 2016, Firenze, October 2016. • R.S. Singh, M. Baudette, H. Hooshyar, M.S. Almas, Stig Løvlund, L. Vanfretti, “‘In Silico’ Testing of a Decentralized PMU Data-‐Based Power Systems Mode Estimator,” IEEE PES General Meeting 2016, Boston, MA, USA. • A. Bidadfar, H. Hooshyar, M. Monadi, L. Vanfretti, Decoupled Voltage Stability Assessment of Distribution Networks using Synchrophasors,” IEEE PES General Meeting 2016, Boston, MA, USA. • S.R. Firouzi, L. Vanfretti, A. Ruiz-‐Alvarez, F. Mahmood, H. Hooshyar, I. Cairo, “An IEC 61850-‐90-‐5 Gateway for IEEE C37.118.2 Synchrophasor Data Transfer,” IEEE PES General Meeting 2016, Boston, MA, USA. • R.S. Singh, H. Hooshyar and L. Vanfretti, “Laboratory Test Set-‐Up for the Assessment of PMU Time Synchronization Requirements,” IEEE PowerTech 2015, The Netherlands, 2015. • R.S. Singh, H. Hooshyar and L. Vanfretti, “Assessment of Time Synchronization Requirements for Phasor Measurement Units,” IEEE PES PowerTech 2015, The Netherlands, 2015. • H. Hooshyar, F. Mahmood, and L. Vanfretti, “HIL Simulation of a Distribution System Reference Model,” NORDAC 2014. • H. Hooshyar and L. Vanfretti, “Specification and Implementation of a Reference Grid for Distribution Network Dynamics Studies,” IEEE PES General Meeting, 2014, National Harbor, MD (Washington, DC Metro Area). • L. Vanfretti, V. H. Aarstrand, M. Shoaib Almas, V. Peric, and J. O. Gjerde, ”A software development toolkit for real-‐time synchrophasor applications”, 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013; Grenoble, France, 16-‐20 June 2013. 29/11/2016 WWW.IDE4L.EUSLIDE 44
- 45. ideal grid for all Conclusions • The increase of intermittent renewable sources at the distribution network bring technical challenges into DSO operations. • Synchrophasor measurements have a great potential to support the technical operation of distribution networks • Applications can play an important role of extracting key information about the operation of the grid • TSOs-‐to-‐DSOs interaction in operations through PMU Apps. • DSOs can enhance the way they operate by having better knowledge of the system’s performance in near real-‐time • TSOs can gain visibility of the phenomena at lower voltage levels, and device actions • Real-‐time automatic control and protection is the next big step • Existing architecture, automation and system level technology not up to the task • Interoperability and Standardization: • Too slow and maybe even useless without Open Source Software and Hardware platforms and building blocks • Need to develop and support a truly open market of products and services 29/11/2016 WWW.IDE4L.EUSLIDE 45
- 46. ideal grid for all Thank you! www.ide4l.eu https://www.kth.se/profile/luigiv luigiv@kth.se