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Lecture 3 Intelligent Energy Systems: Control and Monitoring Basics

Seminar Course 392N ● Spring2011. Lecture 3 Intelligent Energy Systems: Control and Monitoring Basics. Dimitry Gorinevsky. Traditional Grid. Worlds Largest Machine! 3300 utilities 15,000 generators, 14,000 TX substations 211,000 mi of HV lines (>230kV)

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Lecture 3 Intelligent Energy Systems: Control and Monitoring Basics

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  1. Seminar Course 392N ● Spring2011 Lecture 3 Intelligent Energy Systems:Control and MonitoringBasics Dimitry Gorinevsky Intelligent Energy Systems

  2. Traditional Grid • Worlds Largest Machine! • 3300 utilities • 15,000 generators, 14,000 TX substations • 211,000 mi of HV lines (>230kV) • A variety of interacting control systems 2 Intelligent Energy Systems

  3. Smart Energy Grid Intelligent Energy Network Source IPS energy subnet Load IPS Intelligent Power Switch Generation Transmission Distribution Load Conventional Electric Grid Conventional Internet Intelligent Energy Systems

  4. Intelligent Energy Applications Tablet Smart phone Communications Internet Computer Energy Application Presentation Layer Application Logic (Intelligent Functions) Business Logic Database Intelligent Energy Systems

  5. Control Function • Control function in a systems perspective Intelligent Energy Systems

  6. Analysis of Control Function • Control analysis perspective • Goal: verification of control logic • Simulation of the closed-loop behavior • Theoretical analysis Intelligent Energy Systems

  7. Key Control Methods • Control Methods • Design patterns • Analysis templates • P (proportional) control • I (integral) control • Switching control • Optimization • Cascaded control design Intelligent Energy Systems

  8. Generation Frequency Control • Example control command Controller sensor measurements Turbine /Generator disturbance Load Intelligent Energy Systems

  9. Generation Frequency Control • Simplified classic grid frequency control model • Dynamics and Control of Electric Power Systems, G. Andersson, ETH Zurich, 2010 http://www.eeh.ee.ethz.ch/en/eeh/education/courses/viewcourse/227-0528-00l.html Swing equation: Intelligent Energy Systems

  10. 1 0.8 0.6 0.4 0.2 0 0 2 4 P-control • P (proportional) feedback control • Closed –loop dynamics • Steady state error x+0 frequency droop Step response u x(t) frequency droop Intelligent Energy Systems

  11. AGC Control Example • AGC = Automated Generation Control • AGC frequency control AGC generation command frequency measurement disturbance Load Intelligent Energy Systems

  12. AGC Frequency Control • Frequency control model • x is frequency error • cl is frequency droop for load l • u is the generation command • Control logic • I (integral) feedback control • This is simplified analysis Intelligent Energy Systems

  13. -kI b  P and I control • P control of an integrator d x -kp • I control of a gain system. The same feedback loop cl g x Intelligent Energy Systems

  14. Cascade (Nested) Loops • Inner loop has faster time scale than outer loop • In the outer loop time scale, consider the inner loop as a gain system that follows its setpoint input outer loop setpoint outer loop inner loop setpoint Outer Loop Control (command) - inner loop Inner Loop Control - Plant output Intelligent Energy Systems

  15. off on Switching (On-Off) Control • State machine model • Hides the continuous-time dynamics • Continuous-time conditions for switching • Simulation analysis • Stateflow by Mathworks furnace heating passive cooling setpoint Intelligent Energy Systems

  16. Optimization-based Control • Is used in many energy applications, e.g., EMS • Typically, LP or QP problem is solved • Embedded logic: at each step get new data and compute new solution Optimization Problem Formulation Control Variables Measured Data Embedded Optimizer Solver Plant Actuators Sensors Intelligent Energy Systems

  17. Cascade (Hierarchical) Control • Hierarchical decomposition • Cascade loop design • Time scale separation Intelligent Energy Systems

  18. Hierarchical Control Examples • Frequency control • I (AGC)  P (Generator) • ADR – Automated Demand Response • Optimization  Switching • Energy flow control in EMS • Optimization  PI • Building control: • PI  Switching • Optimization Intelligent Energy Systems

  19. Power Generation Time Scales • Power generation and distribution • Energy supply side • Power Supply Scheduling 1/10 1 10 100 1000 Time (s) • http://www.eeh.ee.ethz.ch/en/eeh/education/courses/viewcourse/227-0528-00l.html Intelligent Energy Systems

  20. Power Demand Time Scales • Power consumption • DR, Homes, Buildings, Plants • Demand side Enterprise Demand Scheduling Building HVAC Home Thermostat Demand Response 100 1,000 10,000 Time (s) Intelligent Energy Systems

  21. Research Topics: Control • Potential topics for the term paper. • Distribution system control and optimization • Voltage and frequency stability • Distributed control for Distributed Generation • Distribution Management System: energy optimization, DR Intelligent Energy Systems

  22. Monitoring & Decision Support • Open-loop functions • Data presentation to a user Intelligent Energy Systems

  23. Monitoring Goals • Situational awareness • Anomaly detection • State estimation • Health management • Fault isolation • Condition based maintenances Intelligent Energy Systems

  24. Condition Based Maintenance • CBM+ Initiative Intelligent Energy Systems

  25. mean quality variable 6 3 9 12 12 15 sample SPC: Shewhart Control Chart • W.Shewhart, Bell Labs, 1924 • Statistical Process Control (SPC) • UCL = mean + 3· • LCL = mean-3· Upper Control Limit Walter Shewhart (1891-1967) Lower Control Limit Intelligent Energy Systems

  26. Multivariable SPC • Two correlated univariate processes y1(t) and y2(t) cov(y1,y2) = Q, Q-1=LTL • Uncorrelated linear combinations z(t) = L·[y(t)-] • Declare fault (anomaly) if Intelligent Energy Systems

  27. Multivariate SPC - Hotelling's T2 • Empirical parameter estimates Harold Hotelling (1895-1973) • Hotelling's T2 statistics is • T2 can be trended as a univariate SPC variable Intelligent Energy Systems

  28. Advanced Monitoring Methods • Estimation is dual to control • SPC is a counterpart of switching control • Predictive estimation – forecasting, prognostics • Feedback update of estimates (P feedback  EWMA) • Cascaded design • Hierarchy of monitoring loops at different time scales • Optimization-based methods • Optimal estimation Intelligent Energy Systems

  29. Research Topics: Monitoring • Potential topics for the term paper. • Asset monitoring • Transformers • Electric power circuit state monitoring • Using phasor measurements • Next chart Intelligent Energy Systems

  30. Electric Power Circuit Monitoring model Optimization Problem • State estimate • Fault isolation Electric Power System • Measurements: • Currents • Voltages • Breakers, relays ACC, 2009 Intelligent Energy Systems

  31. End of Lecture 3 Intelligent Energy Systems

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