Combining Real Time Emula0on of Digital Communica0ons between Distributed Embedded Control Nodes with Real Time Power System Simula0on

Transcription

1 1 Combining Real Time Emula0on of Digital Communica0ons between Distributed Embedded Control Nodes with Real Time Power System Simula0on Ziyuan Cai and Ming Yu Electrical and Computer Eng., Florida State University Mischa Steurer, Mike Sloderbeck, and Karl Schoder Center for Advanced Power Systems Florida State University Presenter: Karl Schoder, schoder AT cap.fsu.edu, (850) Panel on Co- simula0on of ICT and Power Distribu0on ISGT, Feb , 2014, Washington, DC

2 2 Overview Mo0va0on Integrate team work Real 0me power system M&S Real 0me communica0on M&S Real 0me distributed controls System Integra0on Challenges addressed Conclusion This work was supported by ERC Program of the Na0onal Science Founda0on under Award Number EEC

3 3 Mo0va0on Integra0on of salient aspects Several teams at several ins0tu0ons Integra0on into coherent system- of- systems test bed Real 0me opera0on and demonstra0on of new technology Addressing verifica0on and valida0on aspects while developing As close as possible to final implementa0on May not need a (full) hardware demonstra0on but Hardware- in- the- Loop Three core aspects Electric power system Opera0on and controls (algorithms) Communica0on

4 4 Problem Domain(s) Electric power system Power flow vs. Dynamic vs. Electro- magne0c transient Time series Phasors Point- on- wave Detailed models of large- scale electric transmission and/or distribu0on systems executed in real 0me Embedded controls and coordina0on Real 0me placorms for algorithms Communica0ons Network Link subsystems with real 0me characteris0cs Parameterized soeware models Possibility for both Control and Hardware- in- the- Loop tes0ng

5 5 FREEDM Distribu0on System Flexible integra0on of genera0on and storage resources (microgrid) Improving energy management and reliability Power electronics At many nodes, loads, and some lines Solid State Transformers (SST) Fault- Isola0on devices (FID) Extensive data communica0on Not a centralized supervisory control and data acquisi0on (SCADA) Distributed Grid Intelligence (DGI) manages Configura0on, Power dispatch, and Faults Grid Substation SST FID FID Dist. SST DGI Communication Network FID DGI DGI FID DGI FID FID Dist. SST FID FID Dist. SST

6 6 Real Time Power System M&S Massively parallel processing designed for power system modeling and simula0on Real Time Digital Simulator (RTDS ) Availability and access to measurements and signals Analog and digital I/O Standard protocols (Modbus, DNP3, IEC 61850) Custom protocols using FPGA, every 0me step Accurate internal clock or GPS synchronized Discrete 0me step solvers 50 us for network, machines, and controls 2 us for converters with PWM

7 7 Real Time Communica0on M&S Specialized soeware for modeling, simula0on, and analysis Discrete event simulator Protocols supported, e.g., IPv4/6, TCP, UDP, custom Observing traffic, round- trip delays, channel loading OPNET with System- in- the- Loop (SITL) interface Real 0me library module Sub- Millisecond range Features supported through message filters Delays, dropped package, communica0on errors Example: Small network of six DGIs, single router with background network traffic of 1kPackets/s and 1024 B/packet. Round trip time (µs) Hardware network Software network Adding delay of 1 ms Software network with deliberate time delay

8 8 Real Time Distributed Controls Embedded computa0on placorms Implementa0on of controls and coordina0on x86- and ARM- based boards with Linux Supports TCP/IP and UDP DGI agents Distributed Grid Intelligence compu0ng nodes to manage energy, power distribu0on, reconfigura0on, and (some) faults Communicate (exchange messages) with peer nodes Local brokers coordinate group behavior and maintains state of system Interface with Solid State Transformers (SST) plug- and- play capable

9 9 Tes0ng subsystems System Integra0on RTS Guaranteed real 0me (or halts execu0on) DGI Dedicated, embedded, RT scheduler OPNET Compared simulated network model to hardware implementa0on, depends on placorm Building a coherent real 0me M&S placorm and test bed Custom implementa0on for access to RTS values Server- Client communica0on model Automa0ng M&S Setup and startup: Configura0on files and scripts Execu0on of scenarios Post- processing Remote access: cross- campus collabora0ons

10 10 Simula0on host: (worksta0on) System Integra0on Raw data directly from physical to soeware network Hardware network and compu0ng nodes interact with simulated network Ability to link mul0ple physical networks Simulation Host OPNET Network Modeler Real Network Ethernet Adapter API Utility Systemin-the-Loop Interface Simulated Network Real Network Ethernet Adapter API Utility Systemin-the-Loop Interface

11 11 System Integra0on Real 0me FREEDM system model implementa0on FPGA Fiber PCIe Real Time Simulator PC UDP Switch Simulated power system Embedded DGI FREEDM Circuit/Feeder model Interface to exchange power system data Embedded DGI instances 2/50 us RTS: 128 Samples/50 us DGI: 1000 Packets/s Simulated communication network SITL TCP/IP and UDP Switch SITL DGI Network for information exchange Communication network model DGI: 1000 Packets/s 100 µs/0.5 ms

12 12 Stability of algorithms Convergence proper0es considering latencies Discrepancies between the expected and simulated Existence of oscilla0ons and/or limit cycles Communica0on needs (messages/second, payload) Requirements for (precision) clock synchroniza0on and state Power system Challenges Addressed Component controls and behavior Integrated system stability and performance

13 13 Summary and Conclusion Integra0on of all aspects R&D needs fully integrated, real 0me cyber- physical test beds Power system components, distribu0on system, coordina0on, local embedded controls, and communica0on Power system models in RTDS Communica0on models in OPNET Distributed control processes on embedded placorms Test bed is an extremely valuable tool to assist development Uncovered several errors in opera0on that were not encountered in other, less realis0c tes0ng environment Time to algorithm convergence for power dispatch higher than expected (difference between analy0cally determined and measured)