Prof Pierluigi Mancarella - Melbourne Energy Institute · IEEE Power and Energy Magazine, May/June 2015, Invited Paper. M. Panteli, D. N. Trakas, P. Mancarella and N. D. Hatziargyriou,

© 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

[email protected]

mailto:[email protected]



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


4


5


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


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”


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


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


Example: climate change-driven windstorms

a) Transmission network b) Weather regions

Fig. 4: The reduced 29-bus Great Britain transmission network























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


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.


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


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


Case study example: 2010, Chile earthquake and tsunami, 8.8 Mw


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


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


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


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)



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


What’s the cost and value of resilience?



Communities and resilience

Courtesy: Dr Jenny Moreno and Prof Duncan Shaw, The University of Manchester, UK


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


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


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



NOP

(open)


Feeder 1 Feeder 2

Microgrid

NOP

(open)

(b) Contingency

Feeder 1 Feeder 2

Microgrid

(Island)

NOP

(open)

(c) Emergency

Feeder 1 Feeder 2

Microgrid





NOP

(open)


Feeder 1 Feeder 2

Microgrid

NOP

(open)

(b) Contingency

Feeder 1 Feeder 2

Microgrid

(Island)

NOP

(open)

(c) Emergency

Feeder 1 Feeder 2

Microgrid




Next: Sustainable and resilient electrification planning

Courtesy: Sarawak Energy; P. Mancarella, EPSRC TERSE project


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


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

[email protected]

mailto:[email protected]

Prof Pierluigi Mancarella - Melbourne Energy Institute · IEEE Power and Energy Magazine, May/June 2015, Invited Paper. M. Panteli, D. N. Trakas, P. Mancarella and N. D. Hatziargyriou,

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