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Transactive Energy in Network MicrogridsProfessor Mohammad Shahidehpour

Galvin Center for Electricity InnovationIllinois Institute of Technology

Outline

• Distributed Power Systems

• Microgrids in Power Systems

• Adaptive Islanding and Networked Microgrids

• Transactive Energy in Network Microgrids

• Conclusions

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Extreme Events

• A more intense climate change has increased the frequency and theseverity of weather-related extreme events throughout the world.

• In 2017, there have been 5 extreme events with losses exceeding $1 billioneach across the U.S.

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• Resilient Power system will prepare adequately for, respondcomprehensively to, and recover rapidly from major disruptions.

Dynamic Islanding of Power Systems

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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

Distributed Power System: Unification and Compartmentalization

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Designated MC

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

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

(a)

(b)

...

Data Flow

DER

Coordinator

Formation of Microgrids

• Microgrids are small-scale self-controllable power systems thatinterconnect DERs and loads within clearly-defined electrical boundaries.

• Each microgrid interacts with the utility grid through a point of commoncoupling (PCC) at its boundary.

• Each microgrid is deployed with a microgrid master controller (MGMC) forcentrally monitoring and controlling on-site resources.

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Loop-Based Microgrid Control

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North

substation

South

substation4.16kV

12.47kV

4.16kV

PCC

Utility grid

Vis

ta1E

Battery

Gas-turbine

Synchronous

Generators

Loo

p 1

Loo

p 3

Loo

p 7

PV

==

Loo

p 2

=

S

=

S

==

=

SPV

===

S

PV

PV

==

=

S

Eng.1

Vis

ta1D

Wind

Vis

ta1BLS

Stuart

VanderCook

Machinery

CTA1

CTA2

Vis

ta1A

Vista1C

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

Operation of Control of Nanogrid

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Islanded Power Systems: Peer-to-Peer Power Transactions

• Networked microgrids may often feature diversified profiles of renewable energy generation and power demand.

• Microgrids should be allowed to trade energy directly with their peers (especially when the utility grid is disrupted) in addition to exchanging energy at pre-specified rates with the utility grid.

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MGMC 2

Microgrid 2

MGMC 1

Microgrid 1

tPower

Surplus

Power

Deficitt

Power

Surplus

Power

Deficit

Peer-to-Peer

Power

Exchanges

• With non-discriminatory peer-to-peer energy transactions, microgrids would not only gain an additional degree of flexibility in their operations but also maintain a dynamic power balance in their service territories with reduced dependency on the utility grid.

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

• During the extreme event, the system performance suffers a continuous degradation as the disruptions are prolonged.

Microgrid Emergency Operating Conditions

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Normal Degraded Normal

Extreme event

occurrence

Condition

deterioration

Restoration

initiation

On Outage Degraded

Restoration

completion

Before During AfterTime

Normal Degraded Normal

Extreme event

occurrence

Disconnection

from main grid

Sustained

Before During AfterTime

Reconnection

to main grid

Outside

Microgrid

Inside

Microgrid

Networked Microgrid Operation

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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

DSO for Networked Microgrids

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Microgrid 2

Upstream Network

Microgrid 1

Microgrid 3

load grid-forming source grid-following source

DSO

MGMC 2 MGMC 3

MGMC 1

LC LC LC LC... ...

LC LC ... LC

Microgrid 2

Microgrid 1

Microgrid 3

ISO

Bulk Generators Bulk Loads

DSO DSO DSO

Microgrid

Microgrid

Microgrid

... ...

...

... ...

Networked Microgrids

Field Devices

DERs Lines Loads

Power Distribution System Operations

Wholesale Power Market Operations

Microgrids for Distribution System Enhancement

• The unique benefits of networked microgrids are manifested insupporting power system resilience are further explored.

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Resilience

Enhancement

Integrated Decision Framework

• In order to determine the most cost-effective solution to achieving the power system resilience to an extreme event, we consider the interdependency of infrastructural and operational measures, and the interactions of preventive and corrective actions.

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Preventive Actions

Before the Extreme Event

Corrective Actions

During and After the Extreme Event

Short-term Operational Enhancement

Long-term Infrastructural Reinforcement

PlanningHardening

• This multi-layer framework presents an inherent leader-follower relationship between infrastructural reinforcement and operational enforcement.

• In order to discover the optimal combination and the sequences for implementing the resilience enhancement measures, stochastic optimization or robust optimization problems can be formulated within this framework.

System Resilience Enhancement

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Time

Per

form

an

ce

Per

form

an

ce

Infrastructure Integrity

Power System Level

With Operational Enhancement

Temporal

Integration

Pro

bab

ilit

y

Functionality Time

Fu

ncti

on

ali

ty

Power Component Level

With Infrastructural Reinforcement

Probability

Analyses

Conclusions

• The development of networked microgrids will drive the conventionally centralized systems to migrate toward distributed localized systems, together with the significant changes in power system management.

• Resilience, as a key attribute of Smart Grid, is central to the efforts into modernizing power systems in the face of the growing number of widespread outages due to extreme events.

• Data analytics will play a key role in managing the power systems resilience via distributed power systems.

• Additional methods and solutions would have to be developed for managing the myriad of data presented in microgrid operation and planning.

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