Seminar Course 392N Spring2011. ee392n - Spring 2011 Stanford University. Intelligent Energy Systems 1

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1 Seminar Course 392N Spring211 Lecture 3 Intelligent Energy Systems: Control and Monitoring Basics Dimitry Gorinevsky Intelligent Energy Systems 1

2 Traditional Grid Worlds Largest Machine! 33 utilities 15, generators, 14, TX substations 211, mi of HV lines (>23kV) A variety of interacting control systems Intelligent Energy Systems 2 2

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

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

5 Control Function Control function in a systems perspective Physical system Measurement Control Control System Logic Handles Sensors Actuators Plant Intelligent Energy Systems 5

6 Analysis of Control Function Control analysis perspective Goal: verification of control logic Simulation of the closed-looploop behavior Theoretical analysis Control Logic Measurement Model System Model Control Handle Model Intelligent Energy Systems 6

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 7

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

9 Generation Frequency Control Simplified classic grid frequency control model Dynamics and Control of Electric Power Systems, G. Andersson, ETH Zurich, 21 P P P e P load Swing equation: x u d I P m P e x P m / I u P e / I d Intelligent Energy Systems 9

10 P-control P (proportional) feedback control u k P x Closed loop dynamics x x k px x e k p t d Steady state error x d / s s k p 1 k t k frequency droop p d (1 e p ) x+ u x u d frequency droop Step response x(t) 2 4 Intelligent Energy Systems 1

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

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

13 P and I control P control of an integrator u k P x x bu d d b x -k p I control of a gain system. The same feedback loop u k I x x g u cl g c l, -k I x Intelligent Energy Systems 13

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 (command) - Outer Loop Control inner loop setpoint - Inner Loop Control output Plant outer loop inner loop Intelligent Energy Systems 14

15 Switching (On-Off) Off) Control State machine model Hides the continuous-time dynamics Continuous-time conditions for switching Simulation analysis Stateflow by Mathworks x 7 setpoint off passive cooling x x on furnace heating Intelligent Energy Systems 15

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 Measured Data Embedded Optimizer Solver Control Variables Sensors Plant Actuators Intelligent Energy Systems 16

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

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 18

19 Power Generation Time Scales Power generation and distribution Energy supply side Power Supply Scheduling 1/ Time (s) Intelligent Energy Systems 19

20 Power Demand Time Scales Power consumption DR, Homes, Buildings, Plants Demand side Demand Response Home Thermostat Building HVAC Enterprise Demand Scheduling 1 1, 1, Time (s) Intelligent Energy Systems 2

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 21

22 Monitoring & Decision Support Open-loop functions - Data presentation to a user Physical system Measurement System Sensors Monitoring & Decision Support Data Presentation Physical system Intelligent Energy Systems 22

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

24 Condition Based Maintenance CBM+ Initiative Intelligent Energy Systems 24

25 qua ality varia able SPC: Shewhart Control Chart W.Shewhart, Bell Labs, 1924 Statistical ti ti Process Control (SPC) UCL = mean + 3 LCL = mean sample Upper Control Limit mean Lower Control Limit Walter Shewhart ( ) Intelligent Energy Systems 25

26 Multivariable SPC Two correlated univariate processes y 1 (t) and y 2 (t) cov(y = -1 = L T 1,y 2 ) Q, Q L y y y Uncorrelated linear combinations z(t)=l [y(t)-] = z 2 T 1 2 y Q y ~ Declare fault (anomaly) )if T 1 2 y Q y c Intelligent Energy Systems 26

27 Multivariate SPC - Hotelling's T 2 g Empirical parameter estimates p p X E t y n n t ) ( 1 ˆ 1 y t y t y n Q T n t T t cov ) ) ( )( ) ( ( 1 ˆ 1 1 Hotelling's T 2 statistics is ) ( ˆ ) ( 1 2 t Q t T T Harold Hotelling ( ) T 2 can be trended as a univariate SPC variable ) ( ) ( 1 2 t y Q t y T Intelligent Energy Systems 27

28 Advanced Monitoring Methods Estimation is dual to control SPC is a counterpart of switching control Predictive estimation forecasting, gprognostics 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 28

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

30 Electric Power Circuit Monitoring E261: EY26 OUTPUT VOLTAGE Ax Bf y Cx Df model E14: BATT 1 CB OUTPUT VOLTAGE E242: LOAD 2 BATT OUTPUT VOLTAGE IT24: BATT 2 OUTPUT CURRENT ESH244A: EY244A RELAY ISH262: INV 2 INPUT CB E281: LOAD 2 DC VOLTAGE IT261: INV 2 INPUT CURRENT ESH26A: EY26A RELAY w v Measurements: Currents Voltages Breakers, relays Optimization Problem Electric Power System ACC, 29 Intelligent Energy Systems State estimate Fault isolation E14: BATT 1 CB OUTPUT VOLTAGE E261: EY26 OUTPUT VOLTAGE 5 1 IT24: BATT 2 OUTPUT CURRENT ESH244A: EY244A RELAY ISH262: INV 2 INPUT CB E242: LOAD 2 BATT OUTPUT VOLTAGE E281: LOAD 2 DC VOLTAGE IT261: INV 2 INPUT CURRENT ESH26A: EY26A RELAY RINV2: DC/AC Inverter

31 End of Lecture 3 Intelligent Energy Systems 31