cascading failure
DESCRIPTION
CASCADING FAILURE. Ian Dobson ECE dept., University of Wisconsin USA Ben Carreras Oak Ridge National Lab USA David Newman Physics dept., University of Alaska USA. Presentation at University of Liege March 2003. Funding in part from USA DOE CERTS and NSF is gratefully acknowledged. - PowerPoint PPT PresentationTRANSCRIPT
CASCADING FAILUREIan Dobson
ECE dept., University of Wisconsin USA
Ben Carreras Oak Ridge National Lab USA
David Newman Physics dept., University of Alaska USA
Presentation at University of LiegeMarch 2003
Funding in part from USA DOE CERTS and NSFis gratefully acknowledged
power tail
blackout size S (log scale)
(log scale) S -1
-Se
power tails have huge impact on large blackout risk.
probability
risk = probability x cost
NERC blackout data15 years, 427 blackouts 1984-1998
(also sandpile data)
10-6
10-5
10-4
10-3
10-2
10-1
100
101
102
103
104
Sandpile avalanche
MWh lost
Probability
Event size
power tail in NERC data consistent with power system operated near criticality
Cascading failure; large blackouts
• dependent rare events + many combinations = hard to analyze or simulate
• mechanisms: hidden failures, overloads, oscillations, transients, control or operator error, ... but all depend on loading
Loading and cascading• LOW LOAD
- weak dependence- events nearly independent - exponential tails in blackout size pdf
• CRITICAL LOAD- power tails in blackout size pdf
• HIGHER LOAD- strong dependence- total blackout likely
Extremes of loadingVERY LOW LOADING
independent failures; pdf has exponential tail
TRANSITION ??
VERY HIGH LOADING
total blackoutwith probabilityone
blackout size
blackout size
log-log plot
Types of dependency in failure of systems with many components
• independent
• common mode
• common cause
• cascading failure
CASCADE:A probabilistic
loading-dependent model of cascading failure
CASCADE model
• n identical components with random initial load uniform in [Lmin, Lmax]
• initial disturbance D adds load to each component• component fails when its load exceeds threshold Lfail
and then adds load P to every other component. Load transfer amount P measures component coupling, dependency
• iterate until no further failures
5 component example
5 component example
Normalize so that initial load range is [0,1]
and failure threshold is 1 normalized initial disturbance d
d =
normalized load transfer p
p =
D - (Lfail - Lmax) Lmax -Lmin
P Lmax -Lmin
Formulas for probability of r components fail for 0<d<1
nr)( d (rp+d) (1-rp-d) ; np+d<1
quasibinomial distribution; Consul 74
r-1 n-r
for np+d >1, extended quasibinomial:• quasibinomial for smaller r • zero for intermediate r• remaining probability for r = n
average number of failures < r > n=100 components
d
p
example of application:modeling load increase• Lmax = Lfail = 1
• increase average load L by increasing Lmin
-
--
1
0
L
example of application:
• n = 100 components• P = D = 0.005
• p = d = 0.005
1 - Lmin
probability distribution asaverage load L increases
0.5 0.6 0.7 0.8 0.9L
20
40
60
80
100average # failures <r>versus load Lp=d and n=100
<r>
example 2 of application:back off Lmax ( n-1 criterion)
-
-
Lfail = 1
Lmin = 0
Lmax-k
Increase average load leads to change in d and p constant
1 5 10 50 100 5001000r
0.00001
0.0001
0.001
0.01
0.1
1f p=0.00075, n=1000
d=0.0005 d=0.05 d=0.2
d=0.25
GPD formulas for probability of r components fail
(r+) e / r! ; n+<n;r<n; remaining probability for r = n. For r<n agrees with generalized Poisson distribution GPD
r-1
for n+>n, extended GPD:• GPD for smaller r • zero for intermediate r• remaining probability for r = n
-r-
probability distribution as average load L increases
GPD model
1 2 5 10 20 50 100r
0.00001
0.0001
0.001
0.01
0.1
1f
L=0.82
L=0.2
L=0.747
SUMMARY OF CASCADE• features of loading-dependent cascading failure
are captured in probabilistic model with analytic solution
• extended quasibinomial distribution with n,d,p; approximated by GPD with nd, np
• distributions show exponential or power tails or high probability of total failure;
• power tail and total failure regimes show greatly increased risk of catastrophic failure
• power tails when np1
OPA:A power systems blackoutmodel including cascading failure and self-organizing
dynamics
Why would power systems operate near criticality??
• Near criticality, expected blackout size sharply increases; increased risk of cascading failure.
0
5
10
15
20
25
30
1 104
1.2 104
1.4 104
1.6 104
1.2 104 1.4 104 1.6 104 1.8 104
outages
Power Served
<Number of line outages> Power Served
Power Demand
Forces shaping power transmission
• Load increase (2% per year) and increase in bulk power transfers, economics
• Engineering:
• new controls and equipment• upgrade weakest parts
these engineering forces are part of the dynamics!
Ingredients of SOC in idealized sandpile
• system state = local max gradients • event = sand topples (cascade of events is an
avalanche)1 addition of sand builds up sandpile2 gravity pulls down sandpile• Hence dynamic equilibrium with avalanches of all
sizes and long time correlations
Power system Sand pile
system state loading pattern gradient profile
driving force customer demand adding sand
relaxing force response to blackout gravity
event limit flow or trip sand topples
Analogy between power system and sand pile
OPA model Summary
• transmission system modeled with DC load flow and LP dispatch
• random initial disturbances and probabilistic cascading line outages and overloads
• underlying load growth + load variations• engineering responses to blackouts: upgrade
lines involved in blackouts; upgrade generation
DC load flow model(linear, no losses, real power only)
Power injections at buses P
Line flows F
generators have max power P max
line flow limits + Fmax
Slow and fast timescales
• SLOW : load growth and responses to blackouts. (days to years)slow dynamics indexed by days
• FAST : cascading events.(minutes to hours)fast dynamics happen at daily peak load; timing neglected
Response to blackout by engineers
increase line limit F by a fixed percentage.maxFor lines involved in the blackout,
Also, when total generation margin drops below threshold,increase generator power limit Pat selected generators coordinated with line limits.
max
Fast cascade dynamics1 Start with daily flows and injections
2 Outage lines with given probability (initial disturbance)
3 Use LP to redispatch
4 Outage lines overloaded in step 3 with given probability
5 If outage goto 3, else stop
Objective: produce list of lines involved in cascade consistent with system constraints
Conventional LP redispatch to satisfy limits
Minimize change in generation and loads (load change weighted x 100) subject to:
overall power balanceline flow limitsload shedding positive and less than total loadgeneration positive and less than generator limit
Model
Are any overload lines?
Yes, test for outage
Line outage
no
No outage
If power shed,it is a blackout
LP calculation
Yes
Secular increase on demandRandom fluctuation of loadsUpgrade of lines after blackoutPossible random outage
1 day loop
1 minuteloop
Is the total generation margin below critical?
No Yes
A new generatorbuild after n days
Possible Approaches to Modeling Blackout Dynamics
Complexity(nonlinear dynamics, interdependences)
Model detail(increase detailsin the models,structure of networks,…)
OPA model
By incorporating the complex behavior, the OPA approach aimsto extract universal features (power tails,…).
OPA model results include:
• self-organization to a dynamic equilibrium
• complicated critical point behaviors
Time evolution• The system evolves to steady state.• A measure of the state of the system is the average
fractional line loading.
M =1
Numberlines
FijFij
maxLines∑
Fij ≡Power flow between
nodes i and j
0.40.50.60.70.80.9
1
010203040506070
0 100 2 104 4 104 6 104 8 104
<M> Total overloads
Time (days)
0
5
10
15
20
6.06 104 6.07 104 6.08 104
Total overloads
Time (days)
200 days
Steady state
0
5
10
15
20
25
30
0
4000
8000
1.2 10
4
1.6 10
4
0 10
0
2 10
4
4 10
4
6 10
4
8 10
4
Blackouts
Power Served
Number Blackout per 300 days
Power Served
Time (days)
OPA/NERC results
10-2
10-1
100
101
102
10-4
10-3
10-2
10-1
100
NERC data
382-node Network
Probabilty distribution
Load shed/ Power served
Application of the OPA model
10-1
100
101
102
10-3 10-2 10-1 100
IEEE 118WSCCN = 382
Probability distribution
Load shed/ Power delivered
• The probability distribution function of blackout size for different networks has a similar functional form - universality?
Effect of blackout mitigation methods on pdf of blackout size
“obvious” methods can have counterintuitive effects
Mitigation
• Require a certain minimum number of transmission lines to overload before any line outages can occur.
A minimum number of line overloads before any line outages
• With no mitigation, there are blackouts with line outages ranging from zero up to 20.
• When we suppress outages unless there are n > nmax overloaded lines, there is an increase in the number of large blackouts.
• The overall result is only a reduction of 15% of the total number of blackouts.
• this reduction may not yield overall benefit to consumers.
100
101
102
103
104
105
0 10 20 30 40 50 60 70
Base case
nmax
= 10
nmax
= 20
nmax
= 30
Number of blackouts
Line outages
Forest fire mitigation
0.1
1
10
100
1000
104
20 40 60 80 100
Trees burned with efficient firefighters
Trees burned without firefighters
# of events of given size
Size of fire
Dynamics essential in evaluating blackout mitigation methods
• Suppose power system organizes itself to near criticality
• We try a mitigation method requiring 30 lines to overload before outages occur.
• Method effective in short time scale. In long time scale very large blackouts occur.
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0
20
40
60
80
100
0 5000 10000 15000 20000
<M>
outages
<M> Number of line outages per blackout
Time (days)
Standard
Suppress
n < 30
KEY POINTS• NERC data suggests power tails and power
system operated near criticality
• power tails imply significant risk of large blackouts and nonstandard risk analysis
• cascading loading-dependent failure
• engineering improvements and economic forces can drive to criticality
• in mitigating blackout risk, sensible approaches can have unintended consequences
BIG PICTURE• Substantial risk of large blackouts caused by
cascading events; need to address a huge number of rare interactions
• Where is the “edge” for high risk of cascading failure? How do we detect this in designing complex engineering systems?
• Risk analysis and blackout mitigation based on entire pdf, including high risk large blackouts.
• Developing understanding and methods is better than the direct experimental approach of waiting for large blackouts to happen!
Papers on this topic are available from
http://eceserv0.ece.wisc.edu/~dobson/home.html