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1 Control Strategies for Resilience Enhancement in Networked Microgrids Professor Mohammad Shahidehpour Galvin Center for Electricity Innovation Illinois Institute of Technology

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Page 1: Control Strategies for Resilience Enhancement in Networked …peac.ece.iit.edu/wp-content/uploads/2019/11/1031_1400_Mohamma… · Control Strategies for Resilience Enhancement in

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Control Strategies for Resilience Enhancement in Networked Microgrids

Professor Mohammad ShahidehpourGalvin Center for Electricity Innovation

Illinois Institute of Technology 

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Outline

• Distributed Power Systems

• Applications of Distributed Power Systems

• Microgrids in Power Systems

• Networked Microgrids 

• Conclusions

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Edison Transformation 

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Building Block of Smart Grid: Microgrids

Locally integrate renewables and other distributed generationsources and provide reliable power to customers.

Protect critical infrastructure from power outages in theevent of physical or cyber disruptions in the bulk electric grid.

Ensure that critical operations can be sustained duringprolonged utility power outages.

Electric power grid will be the “grid of grids” in the future.

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Solar Canopy, Flow Battery

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Loop‐based microgrid topology introduces additional benefits in enhancing the reliability and resilience of energy supplies.

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AC/DC Nanogrid at IIT 

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Multi‐Microgrid In Chicago

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Frequency Domain Analysis 

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IndependentOperation of Microgrids

CoordinatedOperation

Loci of dominant eigenvalues when ‐P droop gains increase

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Weather‐Related Power Outages

• The climate change has increased both the frequency and the severity of adverse weather throughout the world. 

• There were as many as 10 times more massive blackouts each year in the 2000s compared to those reported each year in the 1980s and early 1990s. 

• In 2018, the United States suffered six weather‐related disasters, including 4 severe storm events and 2 winter storm events, each of which incurred economic losses exceeding $1 billion. 

Blizzard Hurricane

Flood Earthquake

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Emergency Microgrid Operations

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Normal Degraded Normal

Extreme event occurrence

Conditiondeterioration

Restorationinitiation

On Outage Degraded

Restorationcompletion

Before During AfterTime

Normal Degraded Normal

Extreme event occurrence

Disconnectionfrom main grid

Sustained

Before During AfterTime

Reconnectionto main grid

withoutMicrogrid

withMicrogrid

Time

Robustness

Preparedness

Recoverability

Rapidity

Before During After

Perception Comprehension Projection

Situational Awareness

Elements of Resilience

Extreme Events

Hurricane Flood Solar Flare... Earthquake Cyberattack

Responsiveness

Survivability

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Distributed Power System Operation: Division and Unification

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Designated MC

... ... ......

... .........

(a)

(b)

...

Data FlowDERCoordinator

MG0 MG1

Utility

AC main bus

IC1

MGj

ICj ICN

MGN

MC0 MC1 MCj MCN

CMC

Utility feeders

IC0

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• The networked microgrid system can switch between two distinct modes for enhancing the power system resilience by exploiting the system operational flexibility in critical conditions.

‐ The division mode helps the networked microgrid system prepare more adequately for perceived extreme events by ensuring the operational flexibility of individual microgrids. In division mode, each microgrid can be islanded from or resynchronized with the system with limited impacts on the remaining system.

‐ The unification mode allows the system to adapt to changing conditions and incidental disturbances by the cooperation among participating microgrids. Accordingly, the operational flexibility of the entire system is improved as available resources are shared among participating microgrids.

‐ The switching between the two modes does not require additional communication and control infrastructures, indicating that the power system resilience is improved in a cost‐effective way

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Division and Unification of Networked Microgrid

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Dynamic Islanding Schemes

• In each aggregated island, interconnected microgrids are enabled totransact energy directly and flexibly for surviving utility grid disruptions.

• The islanding scheme could be adjusted on an on‐going basis accordingto temporal operation characteristics of individual microgrids and theprogression of failures in the utility grid.

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Upstream Network

Microgrid 1 Microgrid 2

Microgrid 3 Microgrid 4

XX

Single Island

Upstream Network

Microgrid 1 Microgrid 2

Microgrid 3 Microgrid 4

XX

Island 1

Island 2

Upstream Network

Microgrid 1 Microgrid 2

Microgrid 3 Microgrid 4

XX

Island 1 Island 2

Island 3 Island 4

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Decision for Optimal Islanding 

• Transactive energy facilitates the formation of aggregated islands ofmultiple microgrids, boosting the flexibility of the islanding process.

• DSO determines and triggers the islanding of networked microgrids in aholistic manner by considering the role of transactive energy, instead offorcing islanded microgrids to rely solely on themselves.

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Flexible Energy Trading

Upper-Level Problem (DSO)

Determine: Optimal Islanding Scheme

Constrained by: Security of Distribution System Operations

Lower-Level Problem (Networked Microgrids)

Determine: Minimized Generation and Load Shedding Costs

Constrained by: Transactive Energy Principles

Distribution System Configuration

Total Operation Costs

Two‐Layer Decision‐Making Scheme

Upstream Network

Microgrid 1 Microgrid 2

Microgrid 3 Microgrid 4

XX

Island 1 Island 2

Island 3 Island 4

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The networked microgrid system is composed of three interconnected microgrids, including MG1 (containing DERs 1‐3), MG2 (containing DERs 4‐7), and MG3 (containing DERs 8‐9). The loads are located at Buses 1, 9, 12, 14, 18, 21, 25, 27, 30, and 32, respectively. The corresponding communication network is depicted, which is a fully connected graph. The rated frequency and voltage are set as 60Hz and 1 p.u.

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Case Study

1 2 3 4 5 6 7 8 9 10 11 12 13 14

23 24 25 26 27 28 29 30 31 32 33

15 16 17 18

DER1

S12

19 20 21 22

S23S13

DER2

DER3

DERs Loads Closed switch Opened switch

DER7DER6

DER4 DER5

DER8 DER9

MG1 MG2

MG3

7

6 5

1

2 3MC1

4

8 9

MG1

MG2 MG3

DER Pinned DER

Pinning control signal

MC

MC3

MC2

Intra-communication linkInter-communication link

33‐bus distribution system composed of three interconnected microgrids

Communication network of the networked microgrid system

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(1) At t = 10s, the networked microgrid system runs in division mode when the system operation is divided into several self‐controlled entities with different active power sharing strategies while all the MGs are still interconnected in terms of power and communication networks;

(2) At t = 30s, an additional load (135 kW+j15kVar) is added to the MG1 and an additional load (120 kW+j15kVar) is added to MG2;

(3) At t = 50s, MG2 is islanded from the distribution network and operated in islanded mode;

(4) At t = 70s, MG2 is resynchronized with the distribution network.

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Case Study: Division Mode

10 20 30 40 50 60 70 80Time(s)

0.5

1

1.5

2

10 20 30 40 50 60 70 80Time(s)

59.4

59.6

59.8

60

60.2

60.4

Freq

uenc

y (H

z)Po

wer

Sha

ring

(miP

i)

(a)

(b)

DERs in MG1

DERs in MG2

DERs in MG3

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

10 20 30 40 50 60 70 800.9

0.95

1

1.05

Time(s)

Vol

tage

(p.u

.)

(c)

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

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(5) At t = 90s, the networked MGs system is switched to the unification mode which allows all DERs in participating MGs work together for maintaining the unified active power sharing and rated operating frequency;

(6) At t = 110s, an additional load (150 kW+j15kVar) is added to the distribution network;

(7) At t = 130s, DER9 is disconnected from MG2 with the loss of communication links 3‐9 and 8‐9;

(8) At t = 150s, DER9 is reconnected to MG2.

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Case Study: Unification Mode

80 90 100 110 120 130 140 150 160Time(s)

1

1.5

2

80 90 100 110 120 130 140 150 160Time(s)

59.6

59.8

60

60.2

60.4

Freq

uenc

y (H

z)Po

wer

Sha

ring

(miP

i)

(a)

(b)

DER9 is disconnected

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

80 90 100 110 120 130 140 150 1600.9

0.95

1

1.05

Time(s)

Vol

tage

(p.u

.)

(c)

DER1DER2DER3

MG1:DER4DER5DER6DER7

MG2:DER8DER9

MG3:

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• The myriad of extreme events such as severe weather conditions and potential cyber‐attacks have posed great challenges to the power system resilience.

• The proliferation of microgrids, which could play a key role in resilience enhancement, has changed the paradigm in power system operations.

• The networking of geographically close microgrids is a promising and all‐encompassing solution to accommodate more DERs and further enhance the power system resilience.

• Flexible control strategy could enhance the networked microgrid system resilience against extreme events which is essential for the operation of power distribution systems.

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Conclusion

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Thanks

Dr. Mohammad Shahidehpour