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Power System Control: Enhancing the Human-System Interface The Mathematics of August 14 th 2003: How Complex?. Tom Overbye Dept. of Electrical & Computer Engineering University of Illinois at Urbana-Champaign overbye@ece.uiuc.edu . March 13, 2004. Humans as the key link.
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Power System Control: Enhancing the Human-System Interface The Mathematics of August 14th 2003: How Complex? Tom Overbye Dept. of Electrical & Computer Engineering University of Illinois at Urbana-Champaign overbye@ece.uiuc.edu March 13, 2004
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
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
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])
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
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)
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)
State Estimator Algorithm • Most state estimators use a weighted least-squares approach • 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
Power Flow Equations • The steady-state power flow equations, which must be satisfied at each bus i, are • The power flow solves for the bus voltage magnitude and angle vectors, V and q
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
The DC Power Flow Equation • With these approximations the power flow is reduced to a linear, state-independent, set of equations
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.
PTDF Visualization of a Power Transaction from Wisconsin to TVA
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
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
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
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%
Are DC LODFs Accurate?August 14th Crash Test • Here are some results from August 14th
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
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
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
System with Dynamic Sized Pie Charts used to Indicate Loading
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
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
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
Single Device Contingencies: Contingency to Violated Elements
Single Device Contingencies: Violated Element to Contingencies
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