Active Distribution System Resilience Quantification

1. Proposing a Framework for Resilient Active Distribution Systems using
Withstand, Respond, Adapt, and Prevent Element
Natural Disaster
HATE
Love

2. Outline
1
2
3
4
5
Introduction
Resilience
Active Distribution
System
Resilience Evaluation
Method
Conclusion
A
Outage
(History and Cause)
B Motivation
A Definition
B Triangle and Trapezoid
A
Microgrid and
Multi-Microgrid I will be
talking ..
A
WRAP Model
and Flowchart
B Case study
Energy-not-supplied
(Evaluation Index)

3. Introduction
 The electric power system is considered as the backbone of modern society. Thus, its operation should be safe, reliable, and
efficient to maintain stability in terms of social and economic aspects [1].
 However, the frequency of natural disasters and man-made attacks has been increasing over the decades, thereby increasing
power outages[2].
Fig. 2. Causes of power outages (Source: Author)
Fig. 1. Major Power outages (Source: Ref. [3] )

4. Motivation
Grid “ that meets the needs of the present without compromising the ability of future
generations to meet their own needs”
Grid “that keeps the light on during normal operation conditions ”
Grid “that keeps the light on even during and after extreme events”
Sustainable Grid
Reliable Grid
Resilient Grid
• Faults (Controllable )
• Any high probability event
 Earthquakes, Tsunamis
 Wildfires
 Hurricanes, Flood
 Cyber-Physical attacks
 Any Low probability –high
impact event
The greatest motivation to prevent damages the electric infrastructure, and subsequently reduces the recovery time and cost.

5. Active Distribution Networks (ADNs) are distribution networks that have systems in place to control a combination of distributed
energy source (generators, loads and storage) [4].
Active Distribution Network
Advanced distributed management system (DMS) for supervision, control & operational planning
Active & Reactive Power Support (ancillary services)
Islanding and Black-start capabilities
Microgrids
Active Distribution Network operation
STATCOM and
It s Controller
DG Relay
Wind Farm
Feeder Protection
Relay
Solar Farm DG Relay
STATCOM and
It s Controller
AVR
AVR
DG Relay
Synchronous
generator
COS ɸ
Power factor
meter
Network Information
System Relay Setting
Tool
SCADA
Meter Reading System
Diff. relay
Co-ordinated Voltage
Control
Co-ordinated protection
planning method
Fig. 3. Active distribution network (Source: Ref. [4] )

6. Fig. 4. Microgrid architecture (Source: Ref.[5] )
PV Wind Storage
MC
LC
LC
LC
MC
Load
DGs Storage units
MC MC
LC
MGCC
Local control
Microgeneration control
Industrial Load Offices
Residential load Diesel genetaor
Feeder
Communication and control
MC
LC
MGCC: Microgrid system central control
MC
Fuel cell
Fig. 5. Multi-Microgrid (Source: Author )
• The most important distinguishing feature of MMGs is the rapid power-sharing capabilities between the interconnected distributed
generators [6].
• However, a standalone microgrid can also be attained in tandem through voltage or frequency control by a centralized or
decentralized manner [7].
Multi-Microgrid
Microgrid

7. Resilience
Resilience: The word resilience originates from the Latin word resilio, which means to “spring back” [8].
However, the dictionary meaning indicates that this term refers to the capability to immediately recover from disruptive
events.
 High probability, and low impact
 Static, not dynamic (either reliable or not)
 Concerned with customer interruption time
Reliability
 Low probability, and high impact
 Adaptive, ongoing, long term, time dependent
 Concerned with customer interruption time,
and infrastructure recovery time.
Resiliency
Magnitude
Probability
Reliability
Resilience
Risk
Fig. 6. Reliability vs Resiliency (Source: Author)
Something that a system does, not what it has
• Sustained adaptive capacity
• Continuous adaptability
• Graceful extensibility
 Unforeseen
 Unanticipated
 Unexpected
 Fundamentally surprising

8. Resilience Triangle and Trapezoid
(1-Robustness)
Disturbance
Pre-
Disturbance
Event hits
Functionality
(%)
Time
Phase-I Phase-II Phase-III
Emergency
Restoration
Post-
Restoration
Resilience
Trapezoid
Resilience
operation
Resilience enhancing
Conventional system
Resilience system
Conventional system
Resilience system
Phase-I, Phase-II, and Phase-III
concerning the resilience system.
Fig. 9. Resilience performance curve (Source: Author)
Fig. 7. Resilience Triangle
(Source: Ref [9] )
(1-Robustness)
Robustness
Resilience
Triangle
(1-Robustness) / Rapidness
Event hits
Functionality
(%)
Time
0 t1 t2
(1-Robustness)
Disturbance
Pre-
Disturbance
(1-Robustness) / Rapidness
Event hits
Functionality
(%)
Time
Resilience
Trapezoid
Phase-I Phase-II Phase-III
Emergency Restoration
Post-
Restoration
Fig. 8. Resilience Trapezoid
(Source: Ref [10] )

9. Introduction to WRAP
Withstand any sudden inclement weather or human attack on the infrastructure.
Respond quickly, to restore balance in the community as quickly as possible, after an inevitable attack.
Adapt to abrupt and new operating conditions, while maintaining smooth functionality, both locally and
globally.
Predict or Prevent future attacks based on patterns of past experiences, or reliable forecasts.
W
R
A
P
Resilient system
Reliable forecast to
prevent system damage
Stable performance
( during and post events)
Low restoration time
Low Energy not supplied
outcome
W
Withsand
R A P
Recover Adapt Prevent
Survibability Rapidity Adaptability Predictability
Resilient power system
Fig. 10. WRAP flow (Source: Author)

10. WRAP Concepts
Reliability
W
R
A
P
Robustness
W Energy-not-supplied
Withstand
Survivability &
Sustainability
R
Restoration
Cost of recovery &
Time of recovery
Recover
Rapidity &
Vulnerability
P
Future attack
Failure of probability
Prevent
Reliable forecast &
Decision making
A
To change
Frequency deviation
& Voltage deviation
Adapt
Interdependencies &
Resourcefulness
Fig. 11. WRAP outline (Source: Author)

11. WRAP Flowchart
Reliability
W
R
A
P
Robustness
W Energy-not-supplied
Withstand
R
Restoration
Cost of recovery &
Time of recovery
Recover
P
Future attack
Probability of Failure
Prevent
A
To change
Frequency deviation
& Voltage deviation
Adapt
Fig. 12. WRAP flowchart (Source: Author)
W
(Withstand)
R
(Recover)
P
(Prevent)
A
(Adapt)
F > desired F &
V > desired V
Acceptable performance ?
Yes
No
Start
Stop
No
Yes
Yes
No
Power system parameter
initialization
Minimize energy not supplied
Measure frequency change( F)
& voltage change ( V)
Resilience with WRAP
scheme is satisfied
Measure the time of recovery (ToR) and cost
of recovery (CoR) for traditional system
Model reconfiguration
CoR > desired CoR &
ToR > desired CoR
Evaluate the current state of the system
Calculate probability of failure (PoF)
PoF < ε
Modify data
Minimization ( F) & ( V)
using controller with solar-
wind and FACTs
Optimization for first
recovery of critical loads
(using tie and reclosure)
No
Yes
Planning
stage
Expansion
planning
stage
Operation
stage
New ToR & CoR
New F & V
Resilient system
planning and
operation
Existing system

12. Case Study
• Energy-not-supplied, is evaluated here through the IEEE-33 bus system with PV units.
Fig. 14. Energy-not-supplied in 33-bus system
Fig. 13. Optimal location of PVs in a 33-bus test system
1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18
19 20 21 22
23 24 25
26 27 28 29 30 31 32 33

13. Takeaways
• The proposed WRAP model can effectively measure the resilience of the power system and enhance its resiliency
characteristics in terms of survivability, rapidity, adaptability, and predictability.
• The power system is already experiencing significant impacts from extreme eventualities.
• While resilience investments are in progress, the speed, measure and extent of these investment needs to be improved.
Challenges - - - - - - - -
All hazard
approach
Resilience

14. Reference
1. Z. Bie, Y. Lin, G. Li, and F. Li, "Battling the extreme: A study on the power system resilience," Proceedings of the IEEE, vol. 105, no. 7, pp. 1253-1266, 2017.
2. F. H. Jufri, V. Widiputra, and J. Jung, "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative
assessment methodologies, and enhancement strategies," Applied Energy, vol. 239, pp. 1049-1065, 2019.
3. H. Haes Alhelou, M. E. Hamedani-Golshan, T. C. Njenda, and P. Siano, "A survey on power system blackout and cascading events: Research motivations and
challenges," Energies, vol. 12, no. 4, p. 682, 2019.
4. A. Kulmala, K. Mäki, S. Repo, and P. Järventausta, "Network interconnection studies of distributed generation," IFAC Proceedings Volumes, vol. 42, no. 9, pp.
434-439, 2009.
5. L. Mariam, M. Basu, and M. F. Conlon, "Microgrid: Architecture, policy and future trends," Renewable and Sustainable Energy Reviews, vol. 64, pp. 477-489,
2016.
6. S. Chanda and A. K. Srivastava, "Defining and enabling resiliency of electric distribution systems with multiple microgrids," IEEE Transactions on Smart Grid, vol.
7, no. 6, pp. 2859-2868, 2016.
7. S. D. Manshadi and M. E. Khodayar, "Resilient Operation of Multiple Energy Carrier Microgrids," IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2283-2292,
2015.
8. C. S. Holling, "Resilience and stability of ecological systems," Annual review of ecology and systematics, vol. 4, no. 1, pp. 1-23, 1973.
9. 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, 2017.
10. A. Hussain, V.-H. Bui, and H.-M. Kim, "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied energy, vol. 240,
pp. 56- 72, 2019.

15. Any question please …