Tom Overbye Dept. of Electrical & Computer Engineering University of Illinois at Urbana-Champaign

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Power System Control: Enhancing the Human-System Interface

The Mathematics of August 14th 2003: How Complex?

Tom OverbyeTom Overbye

Dept. of Electrical & Computer EngineeringDept. of Electrical & Computer Engineering

University of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-Champaign

[email protected]@ece.uiuc.edu

March 13, 2004

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Humans as the key link

• Some of power system operations is automated– fault detection, under & over-frequency load-

shedding, under voltage load shedding

• But degree of automation is much lower than many people assume

• Humans are very much “in the loop”

• This is particularly apparent during emergency system events

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August 14th 2003

• The August 14th blackout demonstrated how crucial this link can be, and the critical need for an optimized human-system interface

• This talk demonstrates several techniques for enhancing this interface, with the August 14th blackout as a motivating example

• Talk also looks at accuracy of the mathematical models for the initial August 14th events

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Causes of August 14th Blackout

• US-Canada Interim Report determined three groups of causes for the blackout– Inadequate situational awareness by FirstEnergy

(FE)

–FE failed to adequately manage tree growth in its transmission rights-of-way

–Failure of the grid reliability organizations to provide effective diagnostic support (mostly the Midwest Independent System Operator [MISO])

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NERC Reliability Coordinators

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Control Implications of August 14th

• From a control perspective the August 14th event lasted for over an hour– Interim report noted that prior to 15:05 EDT the

system was in a reliable operational state

–From the first event at 15:05:41 until the blackout was complete at 16:13 there were essentially no human-initiated corrective control actions• There was a lot of talk, and some were prepared to act,

but the state of the grid was almost entirely dictated by its physics and automatic controls

• Talk looks at why and how to do better

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Early Power System Control (in 1919)

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Late 1990’s Control Centers (ComED)

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Control Center Trend Towards Overview Displays (ComED Now)

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Overview of Real-time Power System Operations

• Off-line studies used to plan system dispatch

• Real-time data comes to control center via SCADA–SCADA data is displayed to operators

–user entered topology is used to calculate line outage distribution factors (LODFs)

– flowgates values determined for “critical” facilities

– flowgate overloads are curtailed by TLR (transmission load relief)

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Overview of Real-time Power System Operations

• State estimator (SE) uses SCADA data and a system model to calculate the system state (mostly voltages at all system buses)– the key output of SE is a system power flow model

• Power flow model is used in advanced applications, such as contingency analysis (CA), optimal power flow (OPF), and security constrained OPF (SCOPF)–SCOPF calculates bus marginal prices (LMPs)

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State Estimator Algorithm

• Most state estimators use a weighted least-squares approach

1min ( ) ( ) ( )

where is the measurement covariance matrix

Tmeas meas

xJ x z f x R z f x

R• Because the power system is non-linear, the SE requires an iterative

solution–advanced apps can’t run without an SE solution

– topology errors in f can cause non-convergence

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Power Flow Equations

• The steady-state power flow equations, which must be satisfied at each bus i, are

1

1

( cos sin )

( sin cos )

where is the bus admittance matrix

n

i k ik ik ik ik Gi Dik

n

i k ik ik ik ik Gi Dik

V V G B P P

V V G B Q Q

j

G B

• The power flow solves for the bus voltage magnitude and angle vectors, V and

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The DC Power Flow

• The DC power flow makes a number of approximations to greatly simplify the non-linear AC power flow–completely ignores the reactive power flow

–assumes all voltage magnitudes are one per unit (i.e., at their nominal values)

– ignores line resistive losses

– ignores tap dependence of the impedance of LTC and phase shifting transformers

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The DC Power Flow Equation

• With these approximations the power flow is reduced to a linear, state-independent, set of equations

1

where is the vector of bus voltage angles,

is the negative of the imaginary portion of the bus

admittance matrix, and is the vector of real power

injections

θ B P

θ B

P

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Power Transfer Distribution Factors (PTDFs)

• The DC power flow approximation is used extensively by NERC to calculate both PTDFs and LODFs

• PTDFs approximate the incremental impact a power transfer has on the network (i.e., how power flows from the seller to the buyer.

1

where only has non-zero entries at the

buyer/seller buses

θ B P

P

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PTDF Visualization of a Power Transaction from Wisconsin to TVA

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Line Outage Distribution Factors (LODFs)

• LODFs are used to approximate the change in the flow on one line caused by the outage of a second line– typically they are only used to determine the change in the MW flow

–LODFs are used extensively in real-time operations

–LODFs are state-independent but do dependent on the assumed network topology

,l l k kP LODF P

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Flowgates

• The real-time loading of the power grid is accessed via “flowgates”

• A flowgate “flow” is the real power flow on one or more transmission element for either base case conditions or a single contingency–contingent flows are determined using LODFs

• Flowgates are used as proxies for other types of limits, such as voltage or stability limits

• Flowgates are calculated using a spreadsheet

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Flows in Northeast Ohio at 15:00 EDT on August 14th 2003

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Northeast Ohio 138 kV Voltage Contour: 15:00 EDT

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Flowgate 2265

• Flowgate 2265 monitors the flow on FE’s Star-Juniper 345 kV line for contingent loss of the Hanna-Juniper 345 Line–normally the LODF for this flowgate is 0.361

– flowgate has a limit of 1080 MW

–at 15:05 EDT the flow as 517 MW on Star-Juniper, 1004 MW on Hanna-Juniper, giving a flowgate value of 520+0.361*1007=884 (82%)

–Chamberlin-Harding 345 opened at 15:05; FE and MISO all missed seeing this

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Flowgate #2265

• At 15:10 EDT (after loss of Chamberlin-Harding 345) #2265 an incorrect value because its LODF was not automatically updated. –Value should be 633+0.463*1174=1176 (109%)

–Value was 633 + 0.361*1174=1057 (98%)

• At 15:32 the flowgate’s contingent line opened, causing the flowgate to again show the correct value, about 107%

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Flows in Northeast Ohio at 15:33 EDT on August 14th 2003

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Northeast Ohio 138 kV Voltage Contour: 15:33 EDT

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Flows in Northeast Ohio at 15:46 EDT on August 14th 2003

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Northeast Ohio 138 kV Voltage Contour: 15:46 EDT

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Flows in Northeast Ohio at 16:05 EDT on August 14th 2003

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Northeast Ohio 138 kV Voltage Contour: 16:05 EDT

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Are DC LODFs Accurate?August 14th Crash Test

• Here are some results from August 14th Time Contingency Element LODF MW (pred) MW (act)

15:05 Chamberlin-Harding 345

Hanna-Juniper 345

0.362 179 176

15:32 Hanna-Juniper 345 Star-Juniper 345

0.465 545 527

15:46 CantonCentral-Cloverdale 138

Sammis-Star 345

0.164 48 54

15:46 same Cloverdale-Star138

0.234 68 64

16:06 Sammis-Star 345Star-Urban 138W.Canton-Dale 138

Star-Juniper345

numerous 517 676

16:06 same Ashtabula-Perry 345

numerous 319 408

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The Results are Actually Quite Good!

• The initial LODF values were accurate to within a few percent

• Even after more than a dozen contingencies, with many voltages well below 0.9 pu, the purely DC LODF analysis was giving fairly good (with 25%) results

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System was Well Behaved

• Until the cascade began at about 16:10 the system was actually quite well behaved mathematically

• How the flow redistributed through the system could have been well predicted by essentially linear means

• Of course, once the cascade started (after more than a dozen contingencies) the dynamics got to be quite complex

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What was missing on August 14th?

• The key missing ingredient on August 14th was a high level view of the system

• Even though SCADA measurements were available, FE, MISO, PJM and AEP did not have a good view of what was happening on the grid, particularly outside of their areas of control/oversight

• Next few slides show some techniques for providing this view

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System with Dynamic Sized Pie Charts used to Indicate Loading

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Contouring

• Contours can be effective for showing large amounts of spatial data–weather maps showing temperatures and weather

radar images provide good examples

–potential power system applications• bus voltage magnitudes and LMPs

• percent loading and PTDFs on transmission lines

• flowgate values

–personally, I think discrete contours are best

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Continuous Contour of Bus LMPs

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Discrete Contour of Bus LMPs

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Interactive 3D Visualization

• Starting point is to re-map traditional one-line into 3D–builds upon the traditional 2D one-line, familiar

to power system users

–existing one-lines can be extended into 3D to highlight relationships between variables

–existing 2D one-lines were redrawn using a 3D visualization language, OpenGL

–easy navigation and interaction very important

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3D View of Generation Sources in Midwest

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Visualization of Contingency Analysis Results

• Contingency analysis results can be presented in a 2D matrix format (contingencies versus violated elements)–but such an approach loses the geographic

information for both the contingencies and the violated elements

• We are working on 3D approaches to supplement traditional 2D displays

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Single Device Contingencies: Contingency to Violated Elements

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Single Device Contingencies: Violated Element to Contingencies

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Conclusion

• Lack of situational awareness was a key cause of the August 14th blackout; this greatly hindered emergency control

• A lack of emergency control requires more constrained operation with increased system cost

• Automatic control, such as price feedback, could certainly help

• Better visualization technology is needed