Smart Grid Operations - Clemson University Research, Education and Innovation-Ecosystem Opportunities

Transcription

1 Smart Grid Operations - Clemson University Research, Education and Innovation-Ecosystem Opportunities G. Kumar Venayagamoorthy, PhD, FIET, FSAIEE Duke Energy Distinguished Professor & Director & Founder of the Real-Time Power and Intelligent Systems Laboratory The Holcombe Department of Electrical & Computer Engineering Clemson University gkumar@ieee.org March 25, 2015 Acknowledgement: DOE DE-OE000060, NSF: IIP # and ECCS #

2 Smart Grid Intelligent Measurements Intelligent Computational Decision and Smart Intelligence Smart Grid Control = [Intelligence, Systems Bidirectional Power Flows, Generation Environment Grid Sources - (Renewable, Traditional), Smart Devices (Loads, Energy Storage), Renewables & Cyber Security Objectives Transformation/ {Maximize (Security and Reliability)}, EVs Minimize Empowering (Cost, Emissions)}] of Intelligent the Traditional Visual & Data Power Power System Analytics Electronics Intelligent Communications

3 August 14, 2003 Blackout Regular Night August 14, 2003 > 60 GW of load loss; > 50 million people affected; Import of ~2GW caused reactive power to be consumed; Eastlake 5 unit tripped; Stuart-Atlanta 345 kv line tripped; MISO was in the dark; A possible load loss (up to 2.5 GW) Inadequate situational awareness.

4 PMUs (2008)

5 PMUs and 2014

6 Big Data Smart Micro-grids Wind/Solar Energy storage power data & forecast Load demand Intelligent Sense-making Energy pricing Volume, Velocity, Variety, Veracity & Value Visualization Secured Wireless Wind/Solar power data & Forecast Big Data Smart Power Systems PMU data Intelligent Sense-making Energy storage Situational Intelligence & Visualization Situational Intelligence & Visualization Decision Making & Intelligent Control Cyber power flows Decision Making & Intelligent Control Cyber power flows Control/Dispatch signals Global reference time (GPS Satellite) PMU Control/Dispatch signals Wind Farm Real power flows Solar Farm Smart Transmission Grid Smart Micro-grids Smart Micro-grid

7 Situational Awareness (SA)

8 Situational Awareness (SA) More information (a lot of data) does not necessarily matter in critical operations; rather, what is important is to prioritize the understanding of what matters at the respective instances. Sense-making is critical and is a process by which individuals attach a meaning to an experience. It is also critical that an understanding be gained from a shared view because the electric power grid is interconnected, and its dynamics are spatially and temporally connected.

9 Situational Intelligence Integrate historical and real-time data to implement near-future situational awareness Intelligence (near-future) = function(history, current status, some predictions) Predict security and stability limits Contingency analysis RT operating conditions Oscillation monitoring Dynamic models Forecast load Predict/forecast generation Advanced RT and predictive visualizations Integrate all applications Predictions is critical for Real-Time Monitoring Topology updates and geographical influence (PI and GIS Google earth tools)

10 US NSF Research Alliance/Partnership for Innovation Project: Situational Intelligence for Smart Grid Optimization and Intelligent Control IIP # Objectives: Situation intelligence for real-time operations. Maximize penetration levels of variable and uncertain generation such as solar & wind power. Dynamic optimal energy & power management systems. Development of a rapid prototyping laboratory for real-time smart grid control centers. Partners & Supporters: Impacts: Energy resilience by improved reliability, sustainability and economic value. Rapid restoration from outages. Softening of negatives effects of the climate change on the economy.

11 Rapid Prototyping Laboratory Situational Intelligence Laboratory Real-Time Grid Simulation Laboratory

12 Rapid Prototyping Laboratory

13 Weather Monitoring System A real-time weather station has been installed on the roof of Riggs Hall (RTPIS Lab building), and in-house weather data visualization has been developed.

14 Solar Irradiance & Temperature Profiles Solar Irradiance (W/m 2 ) Time of the Day Temperature (Fahrenheit) Time of the Day Solar Irradiance (W/m 2 ) Time of the Day Temperature (Fahrenheit) Time of the Day 14

15 June 13, 2013 Event Across the street from Riggs Hall

16 CU-Real-Time Power and Intelligent Systems (RTPIS) Laboratory CU-RTPIS Ribbon Cutting ceremony (Nov. 7, 2013) Emphasis: Research, Education and Innovation-Ecosystem Laboratory for Smart Grid Technologies

17 Online Coherency Analysis of Synchronous Generators in a Power System speed in p.u Time [s] Group index Generator index Time [s] Group index 100ms three phase fault at bus 8 Offline Clustering during 0~18s Online Clustering at 8s Online Clustering at 10s 1 G1, G8 G1,G8 G1,G2,G3, G8,G10,G11,G12,G 13 2 G2,G3 G2,G3 G4,G5,G6, G7,G9 3 G4,G5,G6, G7,G9 G4,G5,G6, G7 G14,G15, G16 Online Clustering at 15s G1,G8 G2,G3 G4,G5,G6, G7,G9 4 G10,G11 G9 G10,G11, G12,G13 5 G12,G13 G10,G11 G14,G15, G16 6 G14,G15, G12,G13 G16 7 G14,G15, G16

18 Online Modal Analysis of Synchronous Generators

19 Power System with PV Plants

20 Areas 1 and 2 AGCs Measured_PV PMU f λ R ACE α 1 α 2

21 PV Power and Tie-Line Power Flow PV penetration ( % ) Time ( hr ) PV Plant Power Output (MW) Tie-line power flow with the PV plant operation on October 21, 2014 between 06h00 and 18h MW drop 400 Tie-line power flow lines between buses with the PV plant operation on October 21, 2014 between 06h00 and 18h00. Tie-line Power ( MW ) Time (hr)

22 Large PV Plant

23 Electric Power Synchrophasor Synchrophasor technology uses Phasor Measurement Units (PMUs) to provide feedback of the current state of the power system in real time. Synchrophasor measurements are sent to a substation/control center Phasor Data Concentrator (PDC) and stored in a database. PMUs communicate to the substation/control center using TCP/IP network connections. Independent System Operator (ISO) Super Data Concentrator Security Gateway Data storage Applications Security Gateway Security Gateway Security Gateway Utility A Control Center Data Concentrator Utility B Control Center Data Concentrator Utility C Control Center Data Concentrator Applications Security Gateway Applications Security Gateway Security Gateway Applications Security Gateway Applications Security Gateway Security Gateway Substation #1 Substation #2 Applications Substation #m Applications PMU #2 PMU #2 PMU #2 PMU #1 PMU #r GPS Clock PMU #1 PMU #s GPS Clock PMU #1 PMU #t GPS Clock

24 Synchrophasor Security Vulnerabilities 1 2 PMU Attacks: Denial of Service Physical Attack General Class of Attack: Interruption Interruption Man in the Middle Interception Packet Analysis Malicious Code Injection Interception Modification Data Spoofing Fabrication Fig. 1. An illustration of a synchrophasor network and vulnerabilities. Note that can affect entire network. 1 2

25 Real-Time Grid Simulation Clemson Regular Night August 14, 2003 Sixteen Phasor Measurement Units 240-three phase node Real-Time Digital Power System Simulator OpenPDC

26 Smart Micro-grid The micro-grid is developed to function in the classical grid-connected and islanded modes. Utility or Simulated Grid P G (t) For Net-Zero Emission Advanced control methods are developed for the DFIG and energy storage system. 1 Controllable Load P NCL1 (t) 7 PMUC Sensors/ Control 2 8 P W (t) PMUC Control Critical Load PMUC Sensors P CL1 (t) 3 P PV1 (t) PMUC Control Diesel Generator PMUC Sensors P DG (t) 4 9 P CL2 (t) PMUC Sensors Centralized Energy Storage PMUC Control P B1 (t) P NCL2 (t) PMUC Sensors/ Control 5 Controllable Load PMUC Sensors/ Control P NCL3 (t) 6 P PV2 (t) PMUC Control Critical Load PMUC Sensors P CL3 (t) P B2 (t) Photovoltaic Wind Turbine Critical Load Controllable Load Micro-Grid Structure Photovoltaic Local Energy Storage

27 Dynamic Energy Management System (DEMS) for a Smart Micro-Grid Decision Tree and DEMS controller performance for second day of operation with SOC initial = 35% Critical load met kwhr (100%) kwhr (99.64%) * kwhr (100%) Controllable load met kwhr (44.88%) kwhr (48.99%) kwhr (50.11%) Battery discharge kwhr kwhr kwhr Battery charge kwhr kwhr kwhr Diesel dispatch kwhr kwhr kwhr Grid import 0.14 kwhr 0.55 kwhr 0.35 kwhr Grid export 0.00 kwhr 0.10 kwhr 0.08 kwhr Battery N dschr (increase by 23.9% ) 320 (increase by 15.9% ) Battery N chr (decrease by 0.4% ) 347 (increase by 37.7% ) Final SOC Mean SOC Normalized P.I Decision Tree Pretrained OEMS Controller OEMS Controller 64.11% 54.35% 46.77% 50.51% 47.27% 46.22%

28 Summary Economic drivers, carbon reduction, regulatory compliance, and an increase in the drive to provide customer self- management of energy costs and consumption are creating the perfect storm for grid modernization and smart electrification. Smart grid data analytics will play an increasingly critical role in the business and physical operations of delivering electricity and managing consumption. Intelligent computing will be a game changer to traditional ways of computing for smart grid operations.

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