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© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 1
Power system resilience to extreme events:
A stronger, bigger, or a smarted grid?
MEI Symposium 2018
Melbourne, Australia, 12 Dec 2018
Prof Pierluigi Mancarella
Chair of Electrical Power Systems
The University of Melbourne
veski Innovation Fellow
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 2
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 3
Reliability vs Resilience
• Power systems have been traditionally designed to be reliable to
the so-called credible events, i.e., N-1 or N-2 outages
• However, what if a “black swan”, high impact low probability
(HILP) event occurs?
• N-X outages
• Cascaded failures
M. Panteli, D. N. Trakas, P. Mancarella and N. D. Hatziargyriou, "Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies," Proceedings of the IEEE, vol. 105, no. 7, pp. 1202-1213, July 2017
M. Panteli and P. Mancarella, “Influence of Extreme Weather and Climate Change on the Resilience of Power Systems: Impacts and Possible Mitigation Strategies”, Electric Power Systems Research, vol. 127, pp. 259–270, October 2015
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 4
4
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 5
5
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 6
Reliability vs Resilience
• Power systems have been traditionally designed to be reliable to
the so-called credible events, i.e., N-1 or N-2 outages
• However, what if a “black swan”, high impact low probability
(HILP) event occurs?
• N-X outages
• Cascaded failures
Need to move towards resilience-thinking and engineering
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 7
Power Systems Resilience
• UK Energy Research Centre:
“A resilient energy system can speedily recover from shocks and can provide alternative means of satisfying energy service needs in the event of changed external circumstances”
• Power Systems Engineering Research Centre (PSERC), USA:
“Ability to degrade gradually under increasing system stress and then to recover to its pre-disturbance secure state”
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 8
8
Conceptual resilience curve
R
Time
Operational Resilience
Ro
Rpe
Rpr
to te trtpe tpr
Robustness/
Resistance
Resourcefulness/Redundancy/
Adaptive Self-organization
Response/
Recovery
Robustness/
Resistance
Resilient
State
Event
progress
Post-event degraded state
Restorative
state
Post-
restoration
state
Infrastructure
Resilience
Infrastructure
Recovery
tir tpir
M. Panteli and P. Mancarella, The Grid: Stronger, Bigger, Smarter? Presenting a conceptual framework of power system resilience, IEEE Power and Energy Magazine, May/June 2015, Invited Paper.
M. Panteli, D. N. Trakas, P. Mancarella and N. D. Hatziargyriou, "Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies," Proceedings of the IEEE, vol. 105, no. 7, pp. 1202-1213, July 2017
M. Panteli, P. Mancarella, D. N. Trakas, E. Kyriakides and N. D. Hatziargyriou, "Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems," IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4732-4742, November 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 9
Modelling operational resilience: Probabilistic impact assessment of extreme events
Hazard
Profile
0
10
20
30
0 5000
Win
d S
peed
(m
/s)
Hour
Time- and Hazard-Dependent
Status of the Components:
“Fragility Curves”
Simulation:
- Sequential Monte Carlo
- Spatiotemporal analysis
Outputs
Calculation of
resilience metrics
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 10
Example: climate change-driven windstorms
a) Transmission network b) Weather regions
Fig. 4: The reduced 29-bus Great Britain transmission network
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 11
Example: climate change-driven windstorms
a) Transmission network b) Weather regions
Fig. 4: The reduced 29-bus Great Britain transmission network
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 12
Example: climate change-driven windstorms
a) Transmission network b) Weather regions
Fig. 4: The reduced 29-bus Great Britain transmission network
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 13
Example: climate change-driven windstorms
a) Transmission network b) Weather regions
Fig. 4: The reduced 29-bus Great Britain transmission network
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 14
Example: climate change-driven windstorms
a) Transmission network b) Weather regions
Fig. 4: The reduced 29-bus Great Britain transmission network
M Panteli, C Pickering, S Wilkinson, R Dawson, P Mancarella, “Power system resilience to extreme weather: Fragility modelling, probabilistic impact assessment, and adaptation measures”, IEEE Transactions on Power Systems 32, 3747-3757, 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 15
Resilience thresholds and nonlinear cascading failures
Reliable
=
Highly
Resilient
Less
Resilient
- Expected Energy Not Served (EENS)
- Loss of Load Frequency (LOLF)
M. Panteli and P. Mancarella, “Modelling and evaluating the resilience of critical power infrastructure to extreme weather events”, IEEE Systems Journal, vol. 11, no. 3, pp. 1733-1742, Sept. 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 16
Resilience Enhancement
Smarter?
Bigger? Stronger?
Planning for Resilience: The Resilience Trilemma
Upgrade
existing
infrastructure,
asset life
extension, etc.
Build new
infrastructure,
e.g. transmission
lines,
substations, etc.
Make the network more
responsive (e.g. faster
restoration), self-
adaptive, resourceful,
etc.
Need for advanced mathematical modelling (simulation and optimization)
M. Panteli and P. Mancarella, The Grid: Stronger, Bigger, Smarter? Presenting a conceptual framework of power system resilience, IEEE Power and Energy Magazine, May/June 2015, Invited Paper.
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 17
Coordinated operation-investment resilience analysis
Resilience assessment (operation)
- Natural hazard simulation
- Outage simulation
- Pre-fault and post-fault operation
Investment
- New infrastructure
(min risk, s.t. budget constraints)
-
Coordination mechanism depends on the level of simplification of the risk assessment module:
- Decomposition techniques
- Optimisation via Simulation
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 18
R4 model: Resilience by Redundancy, Robustness, Response
M. Panteli and P. Mancarella, “Modelling and evaluating the resilience of critical power infrastructure to extreme weather events”, IEEE Systems Journal, vol. 11, no. 3, pp. 1733-1742, Sept. 2017
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 19
Case study example: 2010, Chile earthquake and tsunami, 8.8 Mw
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 20
Investment strategies in Chile
• Adequacy
• N-1 security
• Probabilistic
security
(reliability)
• Resilience
∅ Ciruelos - Pichirropulli
Investment candidate EENS [MWh] Crucero_Encuentro - CerroNavia_LoAguirre (HVDC) 348 Laberinto_Domeyko - Cumbre 392 Ciruelos - Pichirropulli 523 Cautín - Charrúa 580 Ciruelos - Cautín 617 Baseline 696
Investment candidate EENS [GWh] Crucero_Encuentro - CerroNavia_LoAguirre (HVDC) 41.8 CerroNavia_LoAguirre 43.0 Alto_Jahuel 43.2 Charrúa 43.5 Crucero_Encuentro 44.5 Laberinto-Domeyko - Cumbre 44.6 Baseline 45.0
Lin
es
Substa
tions
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 21
Case study example: wildfires in Greece
D Trakas, M. Panteli, N. Hatziargyriou, P. Mancarella, “A distribution system resilience framework against wildlfires”, Book Chapter, under review
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 22
Resilience to wildfires through distributed energy resources (DER)
Wildfire
1
2
3
4
5
6
7
8
9
10
11
12
13
23
24
25
27
28
29
30
31
32
33
19
20
21
22
26
WTPV
WT MT
MT
MT
ESS
ESS
PCC
14
18
17
16
15WT
MT
Upstream
System
Case I: wildfire impact
Case II: with coordinated network support from DER
D Trakas, M. Panteli, N. Hatziargyriou, P. Mancarella, “A distribution system resilience framework against wildlfires”, Book Chapter, under review
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 23
Case study example: Heatwaves and impact of cooling water availability
0
0.2
0.4
0.6
0.8
1
0 60 120 180
Usa
ble
Cap
acit
y i
n %
of
Max
C
apac
ity
Hour
Full Capacity
CCGT w/ CLC
CCGT w/ OLC (ω=1.0)
CCGT w/ OLC (ω=1.25)
CCGT w/ OLC (ω=0.75)
CLC: closed-loop cooling
OLC: open-loop cooling (with different water availability)
Y. Zhou, B. Wang, M. Panteli, P. Mancarella, “Quantifying the System-level Resilience of Electric Power Generation to Extreme Temperature and Water Availability”, IEEE Systems Journal, under review
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 24
What’s the cost and value of resilience?
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 25
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 26
Communities and resilience
Courtesy: Dr Jenny Moreno and Prof Duncan Shaw, The University of Manchester, UK
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 27
Cooperation and solidarity
Social networks
Alternatives to electricity
Use of natural sources of energy
Positive community resilience
Courtesy: Dr Jenny Moreno and Prof Duncan Shaw, The University of Manchester, UK
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 28
Completely rethinking planning:
– Communities are key in the practical development of long term resilience plans, especially in developing countries
– Integrating resilience in planning from a socio-techno-economic perspective
A new socio-technical framework
Disaster management and resilience
in electric power systems
UK-Chile Newton Prize 2018
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 29
Microgrid reliability and resilience services
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Microgrid
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
Microgrid
(Island)
NOP
(open)
(c) Emergency
Feeder 1 Feeder 2
Microgrid
E.A. Martinez-Cesena, N. Good, A. Syrri, and P. Mancarella, “Techno-Economic and Business Case Assessment of Multi-Energy Microgrids with Co-Optimization of Energy, Reserve and Reliability Services”, Applied Energy 210 (2018) 896–913.
A. L. Syrri and P. Mancarella, “Reliability and Risk Assessment of Post-Contingency Demand Response in Smart Distribution Networks”, Sustainable Energy, Grid and Networks (SEGAN), vol. 7, pp. 1-12, September 2016
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 30
Microgrid reliability and resilience services
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Microgrid
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
Microgrid
(Island)
NOP
(open)
(c) Emergency
Feeder 1 Feeder 2
Microgrid
E.A. Martinez-Cesena, N. Good, A. Syrri, and P. Mancarella, “Techno-Economic and Business Case Assessment of Multi-Energy Microgrids with Co-Optimization of Energy, Reserve and Reliability Services”, Applied Energy 210 (2018) 896–913.
A. L. Syrri and P. Mancarella, “Reliability and Risk Assessment of Post-Contingency Demand Response in Smart Distribution Networks”, Sustainable Energy, Grid and Networks (SEGAN), vol. 7, pp. 1-12, September 2016
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 31
Microgrid reliability and resilience services
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Microgrid
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
Microgrid
(Island)
NOP
(open)
(c) Emergency
Feeder 1 Feeder 2
Microgrid
E.A. Martinez-Cesena, N. Good, A. Syrri, and P. Mancarella, “Techno-Economic and Business Case Assessment of Multi-Energy Microgrids with Co-Optimization of Energy, Reserve and Reliability Services”, Applied Energy 210 (2018) 896–913.
A. L. Syrri and P. Mancarella, “Reliability and Risk Assessment of Post-Contingency Demand Response in Smart Distribution Networks”, Sustainable Energy, Grid and Networks (SEGAN), vol. 7, pp. 1-12, September 2016
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 32
Next: Sustainable and resilient electrification planning
Courtesy: Sarawak Energy; P. Mancarella, EPSRC TERSE project
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 33
Key take-aways
Resilience is typical of N-X outages caused by extreme
events
Requires new modelling approaches
State of the art integrated operation-investment
modelling
Results highlight key role of DER and communities
Need for new socio-techno-economic framework to
truly “value” resilience
© 2018 P. Mancarella - The University of Melbourne Resilience, MEI Symposium, Melbourne, 12 Dec 2018 34
Power system resilience to extreme events:
A stronger, bigger, or a smarted grid?
MEI Symposium 2018
Melbourne, Australia, 12 Dec 2018
Prof Pierluigi Mancarella
Chair of Electrical Power Systems
The University of Melbourne
veski Innovation Fellow