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Security of Supply Issues: Technical & Economic Aspects Chen-Ching Liu Advanced Power Technologies Center University of Washington
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Security of Supply Issues:Technical & Economic Aspects
Chen-Ching Liu
Advanced Power Technologies Center
University of Washington
Outline
Blackout and cascaded events Shortage of transmission enhancement
Defense system technology Flexible grid configuration
Future areas - Transmission economics and Microgrids
U.S. Blackout (Aug. 14, 2003)
High temperature in Midwest. FE 870 MW nuclear power plant was down for maintenance.
MISO SE is ineffective from 12:15 to 16:04.
A sequence of lines outages westward and northward across Ohio and into Michigan, and then eastward, splitting New York from Pennsylvania and New Jersey.
Eastlake unit 5 in northern Ohio tripped due to an exciter failure @ 13:31.
A series of 138-kV lines tripped in the vicinity of Akron @ 15:39.
345-kV line (Stuart – Atlanta) tripped @ 14:02.
Voltages in the Akron area fell below low limits.
Loss of the FE Control Center function shortly after 14:14.
345-kV line (Sammis – Star) tripped @ 16:05
Star-South Canton tie line between FE and AEP tripped and reclosed @ 14:27.
Transient instability began after 16:10, and large power swings occurred.
The system becomes unstableThe system become vulnerable Total blackout
Some NERC Recommendations
• Strengthen the NERC compliance enforcement program.• Evaluate vegetation management procedures and results.• Evaluate reactive power and voltage control practices.• Improve system protection to slow or limit the spread of future
cascading outages.• Install additional time-synchronized recording devices as
needed. • Re-evaluate system design, planning, and operating criteria.
Load Shedding
According to the Final NERC Report on August 14, 2003, Blackout, at least 1,500 to 2,500 MW of load in Cleveland-Akron area has had to be shed to prevent the blackout.
Planned Generation Capacity & Transmission Enhancement in U.S.
Actual data (1999~2000)
Planned Capacity ( 25% increase )
Projected Demand ( 18% increase )
Planned Transmission ( 3.5% increase )
Estimated Capacity Margin
(5% increase )
Source: “Reliability Assessment 2001-2010 Report” by NERC, 2001.Information Administration Website: “http://www.eia.doe.gov/cneaf/electricity/page/fact_sheets/transmission.html”
Defense Plans
Coordination of a number of special protection schemes for the entire system
Modification is required (e.g., BPA needs to update the SPSs regularly)
Build a defense system that performs self-healing control actions in an adaptive manner
Strategic Power Infrastructure Defense (SPID)
– Design self-healing strategies and adaptive reconfiguration schemes
To achieve autonomous, adaptive, and preventive remedial control actions
To provide adaptive/intelligent protection To minimize the impact of power system vulnerability
Research consortium with UW, Iowa State, Arizona State and Virginia Tech, sponsored by EPRI, U.S. DoD and 4 institutions, $ 3M total, 1998-2001
Vulnerability Assessment
Vulnerability Regions
AB
CBA CBB
Protection
Voltage Stability
Oscillatory Stability
Transient Stability
Pi
Dynamics and Control
Communication
Intelligent System Models for the Complex Networks
•Physics•Model-based reasoning•Rule-based system•Evolutionary algorithm
•ANN•Generic tasks
•Agent•Multi-agent System
•Decision-making •Forecasting•Learning•Self-healing
•Adaptation•Team work•Coordination•Negotiation
Multi-Agent System for SPID
RE
AC
TIV
E L
AY
ER CO
OR
DIN
AT
ION
LA
YE
R
DE
LIB
ER
AT
IVE
LA
YE
RKnowledge/Decision
exchange
Protection Agents
GenerationAgents
Fault Isolation Agents
FrequencyStabilityAgents
ModelUpdate Agents
CommandInterpretation
Agents
Planning Agent
Restoration Agents
HiddenFailure
Monitoring Agents Reconfiguration Agents
VulnerabilityAssessment
Agents
Power System
Controls
Inhibition Signal
Controls
Plans/Decisions
EventIdentification
Agents
Triggering Events
Event/AlarmFiltering Agents
Events/Alarms
Inputs
Update Model CheckConsistency
Comm.Agent
Adaptive Self-healing:Load Shedding Agent
A control action might fail Reinforcement Learning
– Autonomous learning method based on interactions with the agent’s environment
– If an action is followed by a satisfactory state, the tendency to produce the action is strengthened
Adaptive Self-healing:Load Shedding Agent
The 179 bus system resembling the western U.S. system
ETMSP simulation Remote load shedding scheme based on
frequency decline + frequency decline rate Temporal Difference (TD) method is used for
adaptation: Need to find the learning factor for convergence
Adaptive Self-healing:Load Shedding Agent
179 bus system
Adaptive Self-healing:Load Shedding Agent
0 50 100 150 200 25058.6
58.8
59
59.2
59.4
59.6
59.8
60
frequency with 20 % load shedding
10% load shedding frequ
ency
Time (multiples of 0.02 sec)
Adaptive Self-healing:Load Shedding Agent
0 20 40 60 80 100 120 140 160 1800
0.5
1
1.5
2
2.5
a=0.55
a=0.75
Number of trials
Norm
aliz
ed f
requency Expected normalized system
frequency that makes the system stable
“The load shedding agent is able to find the proper control action in an adaptive manner based on responses from the real power system”
“The load shedding agent is able to find the proper control action in an adaptive manner based on responses from the real power system”
Flexible Grid Configuration to Enhance Robustness
Flexible grid configuration can play a significant role in defense against catastrophic events
Power infrastructure must be more intelligent and flexible
– To allow coordinated operation and control measures to absorb the shock and minimize potential damages caused
by radical events
Area-Partitioning Algorithm
To develop a k-way partitioning algorithm, which uses the information available in network matrices and divides the power grid into k disjoint areas while minimizing load-generation imbalance for each area
Area-Partitioning Algorithm
5
2 1
6 4
3 1
0.1 0.1 0.1
2
1 1
Flexible Grid Configuration by Partitioning
Risk Management of Power Infrastructure
Self-sufficient Sub-networks
Flexible Grid Configuration by Partitioning
Normal Configuration (Wide Area Grid)
High Risk Alert
Lower the Risk Level
Alert is Over
Cascaded Events - Simulation
Compute Power Flows after Tripping– Six lines are found with limit violations – Trip these lines, Bus170 and 171 become isolated buses
Identify New Network Configuration and Solve Power Flows Again
– Fifteen lines are found with limit violations – Trip these lines, seven buses become isolated buses
Continue This Simulation Procedure– Finally the system collapses: most transmission lines are
tripped and most loads are lost
Flexible Grid Configuration to Absorb the Shock
Split the System into Two Areas– Seeds=[Bus 83, Bus 47]– Area One: 92 buses, 117 branches– Area Two: 87 buses, 140 branches
WECC 179-bus System Example
33 32
31 30
35
80
78
74
79 66
75
77
76 72
82
81
86 83
84
85 156 157 161 162
167 165
158
159 155 44
45 160
166
163
5 11
6
8
9
18 17
4
3
7
14
12 13
138 139
147
15
19
16
114
115
118 119
103
107
108
110 102
104
109
142
37 64 63
153 145 151 152
136 49 48
146 154
149
143
43
230 kV 345 kV 500 kV
34
65
71
69
70
87 88
99 36
73
89 90
124 125
168 169 171
170 172 173
111
120
121 122
123
91 - 94 95 - 98
132 133
135 134 104
174, 176, 178 175, 177, 179
48 38
57 58
54 51
52 53 42
55 41 62 56
40
39
150
137
61
148 22
23 25
24
20 21
29 28
2
10
164
113 100
101
105 106
117
50
42
47
46
59 60(3)
116
27 26
68
67
112
141 140
144
180
48
48
48
Split System into Two Areas
33 32
31 3 0
35
80
78
74
79 66
75
77
76 72
82
81
86 83
84
85
114
115
118 119
103
107
108
110 102
104
109
230 kV 345 kV 500 kV
34
65
71
69
70
87 88
99 36
73
89 90
124 125
169 171
170 172 173
111
120
121 122
123
91 - 94 95 - 98
13 2 133
135 134 104
174, 176, 178
113 100
101
105 106
117 116
68
67
112
180
156 157 161 162 168
167 165
158
159 155 44
45 160
166
163
5 11
6
8
9
18 17
4
3
7
14
12 13
138 139
147
15
19
16
142
37 64 63
153 145 151 152
136 49 48
146 154
149
143
43
175, 177, 179
48 38
57 58
54 51
52 53 42
55 41 62 56
40
39
150
137
61
148 22
23 25
24
20 21
29 28
2
10
164
50
42
47
46
59 60(3)
27 26
141 140
144
48
48
48
Flexible Grid Configuration to Absorb the Shock
Use “Power Redispatching & Load Shedding” in Area Two
– Totally, 188 + 64.4 + 60 = 312.4 MW load are shedBus
#Original Load
(MW)Load Shed
(MW)Load Supplied
(MW)
8 239 188 51
16 793.4 64.4 729
154 1066 60 1006
Alert is Over (Wide-Area Grid)
33 32
31 30
35
80
78
74
79 66
75
77
76 72
82
81
86 83
84
85 156 157 161 162
167 165
158
159 155 44
45 160
166
163
5 11
6
8
9
18 17
4
3
7
14
12 13
138 139
147
15
19
16
114
115
118 119
103
107
108
110 102
104
109
142
37 64 63
153 145 151 152
136 49 48
146 154
149
143
43
230 kV 345 kV 500 kV
34
65
71
69
70
87 88
99 36
73
89 90
124 125
168 169 171
170 172 173
111
120
121 122
123
91 - 94 95 - 98
132 133
135 134 104
174, 176, 178 175, 177, 179
48 38
57 58
54 51
52 53 42
55 41 62 56
40
39
150
137
61
148 22
23 25
24
20 21
29 28
2
10
164
113 100
101
105 106
117
50
42
47
46
59 60(3)
116
27 26
68
67
112
141 140
144
180
48
48
48
Investment & Return Analysis of Transmission Expansion
Economics of transmission expansion should be analyzed from an investment / return point of view. - Economic value of transmission capacity improvement- Economic incentives of transmission owners- Economic incentives of generators
Techniques for electricity price forecasting can be used for economic analysis of transmission
expansion.
Example: Investment & Return Analysis of Transmission Expansion
The expected present value of the discounted stream of revenues ($100) exceeds the investment cost ($84).
Invest now
0 1 2 ... T ... Period
Initial Investment
$15
$5
$15 $15
$5 $5
...
...
...
...
Revenueif Priceis high
Revenue
if Priceis low
50%
50%
Discount rate = 10%
Cost of investment = $84
Wait for One Period to Decide
If price is low, $50 < $ 84 Don’t invest Don’t invest
If price is high, $150 > $ 84 Invest Invest
Expected payoff (= 0.5*($150-$84)/1.1 = $30). Waiting for one period is better than investing now since $30 >
$16.
0 1 2 ... T ... Period
Initial Investment
$15
$5
$15 $15
$5 $5
...
...
...
...
Revenueif Priceis high
Revenue
if Priceis low
50%
50%Discount rate = 10%
Cost of investment = $84
Value of Investment Opportunity
An investment project whose revenue follows a geometric Brownian motion
Value of waiting
Overall value of the investment opportunity
Micro Grid Concept
Distant or remote locations
-Islands
-Regions with no pre-existing infrastructure Special power needs
-Chip fabrication plants
-Financial centers
Wind Generators
CADET Technical Brochure 68 “33.6 MW Wind Farm near Carno” EA, OECD, 1998
Example
Load BusGS-1
Load Bus GS-2 Load Bus GS-3 Load Bus GS-4
Load Bus GS-5
Frequency Response with no Control Agent
0 5 10 15 20 25 3059.92
59.94
59.96
59.98
60
60.02
60.04
Time (seconds)
Fre
quency (
hert
z)
GS-3GS-5
Inter-Machine Oscillations
With no control agent there are clear indications of inter-machine oscillation
Results:
•Unnecessary flow oscillations of power along tie lines
•Unnecessary stress on the machines
•Excessively large overshoots
•Excessively long settling times
Control Agent Scheme
Measure the load at each of the load buses. Measure the frequency at each of the load
buses. When a load change is sensed the control
agent generates control signals based on the current rotational energy of the rotors.
Frequency Response with a Control Agent
0 5 10 15 20 25 3059.96
59.965
59.97
59.975
59.98
59.985
59.99
59.995
60
60.005
60.01
Time (seconds)
Fre
quen
cy (
hert
z)
GS-3GS-5
Grand Challenges
Prevention of Major Blackouts Energy Crisis in Western U.S. Evolution of Electricity Markets Alternative Energy and Distributed Generation
Engineering / Technology, Economics,
Public Policy
