an intelligent restoration system for a self-healing smart ......university of central florida 10...

An Intelligent Restoration

System for a Self-healing

Smart Grid

Dr. Wei Sun, Assistant Professor

ECE Department, University of Central Florida

SmartGridComm 2015

Nov. 2, Miami, FL

University of Central Florida2

Could a smart grid have prevented

the Super Bowl blackout?


Motivation: cascading failures,

blackouts, nature disasters

Efficient

Recovery


Self-healing and Resilience

Fault

Identification

Protection

Decisions

Event

Identification

Potential Failure

Identification

Vulnerability

Assessment

Post-Restoration

Status Evaluation

Power System

Restoration Strategies

Remote and

Field Control


Power System Restoration

PreparationSystem

Restoration

Load

Restoration

• Determine system status

• Restart generators

• Network preparation

• Energize transmission

paths

• Restore sufficient load

• Resynchronize islands

• Minimize unserved load

• Schedule load pickup


Restoration Decision Support Tool


On-line Decision Support Tool

for Power System Restoration


System Restoration Procedure


• Blackstart generators hydro or combustion turbine units

• Non-blackstart generators steam turbine units

Optimal Start-up Sequence

of all generators

Generation Capability Optimization

istarttistart ictpt T

maxiP

(MW)igenP

tmaxiistart ictp

ri

Pt T

R T

Generation Capability Curve

0

Gen. Characteristics:

Maximum generator MW output

Generator ramping rate

Cranking time to ramp up and parallel

with system

Critical maximum/minimum time interval

Specified system restoration period


Generator Start-Up Sequencing

Mathematically, it is a nonlinear combinatorial optimization problem.

Developed Mixed Integer Linear Programming (MILP)-based strategies

Max Overall System Generation Capability

subject to

Critical Minimum & Maximum Time Intervals

Start-Up Power Requirements

Discrete Optimization:

• Integer variables of generator starting time

• Binary variables of generator commitment

• Continuous variables of generation level

W. Sun, C. C. Liu, and L. Zhang, “Optimal Generator Start-up Strategy for Bulk Power System

Restoration,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1357-1366, August 2011.


Transmission Path Search

Find optimal transmission path to:

• Crank non-blackstart generating units

• Build transmission network

Motivation:

Max Total Generation Output

subject to

Transmission Line Thermal Limit Constraint

Logic Constraints of Bus/Line Energization

There are Generic Restoration Actions (GRAs) that exist in

diverse restoration strategies.

W. Sun, and C. C. Liu, “Optimal transmission path search in power system restoration,” Bulk Power System

Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid, 2013 IREP

Symposium, Aug. 2013.


Time to Take Restoration Actions

Time to Complete Each GRA

GRA Time (min)

GRA1: start_black_start_unit TGRA1=15

GRA2: find_path TGRA2=N/A

GRA3: energize_line TGRA3=5

GRA4: pick_up_load TGRA4=10

GRA5: synchronize TGRA5=20

GRA6: connect_tie_line TGRA6=25

GRA7: crank_unit TGRA7=15

GRA8: energize_bus TGRA8=5

• Blackstart Unit Module

• Non-blackstart Unit

Module

• Bus/Line Module

• Load Module

Reference: J. S. Wu, C. C. Liu, K. L. Liou, and R. F. Chu, “A Petri Net algorithm for

scheduling of generic restoration actions,” Power Systems, IEEE Transactions on., vol. 12,

no. 1, pp. 69-76, February 1997.


MILP Optimization Modules

Overall System Generation Capability

. . Critical Time Intervals Constraint

MW Start-up Requirement Constraint

Generator Capability Function Constraints

Generato

Max

s t

r Start-up Power Function Constraints

Decision Variables Constraints

Transmission Path Search Logic Constraints

Transmission Line Thermal Limits Constraints

Restoration Action Time Constraints

Modules Connection Constraints

I

II

III

IV


Case Studies

AEP System

With 24 seconds of computational time, find the optimal starting

sequence of total 21 NBS and 16 BS generating units

Lewis Creek

Frontier

• Entergy System

– June 15, 2005 Western Region

disturbance.

– Two 260 MW units tripped off line,

and Western Region separated

from rest of system.

– There was blackstart power from

outside to start 1 generator.


Case Studies

PECO System

Based on PECO-Energy system data, and assume a complete

shutdown

New-England system topology

G10 was BSU and G1 – G9 were NBSUs

Step-by-step restoration actions


Case Studies

Time

(hr)Action Target

Time

(hr)Action Target

t=0:15 Energize Bus 30 t=0:50 Energize Bus 20,22,23,32,33,38

t=0:20 Energize Bus 2Branch 19-20,21-22,24-23,10-

32,19-33,29-38

Branch 30-2 Crank G8,G1

t=0:25 Energize Bus 25,1,3 t=0:55 Energize Bus 34,35,36

Branch 2-25,2-1,2-3 Branch 20-34,22-35,23-36

t=0:30 Energize Bus 37,39,26,4,18 Crank G9

EnergizeBranch 25-37,1-39,25-26,3-

4,3-18t=1:00 Crank G2,G3,G5,G6,G7

Connect G10 t=1:10 Crank G4

t=0:35 Energize Bus 27,5,14,17 t=1:25 Connect G1,G8

Branch 26-27,4-5,4-14,18-17 t=1:30 Connect G9

t=0:40 Energize Bus 6,13,16 t=1:35 Connect G2,G3,G5,G6,G7

Branch 5-6,14-13,17-16 t=1:45 Energize Bus 9,8,7,11,15,12

t=0:45 Energize Bus 10,19,21,24,28,29,31Branch 39-9,5-8,6-7,6-11,14-15,13-

12,22-23

Branch13-10,16-19,16-

21,16-24,26-28,26-29,6-31

t=1:45 Connect G4

t=1:50 EnergizeBranch 29-28,10-11,17-27,16-15,9-

8,8-7,11-12


Optimal Blackstart Capability

Tool for System Restoration

Planning


Blackstart Resources

• NERC Standard EOP-005-2 “System Restoration from

Blackstart Resources”

• Define “Blackstart Resource” as “a generating unit(s) … to be

started without support from the system or … to remain

energized without connection to the remainder of the System,

with the ability to energize a bus….”

• ISO-New England is changing its system restoration

strategy from “Bottom-Up” to “Top-Down”

• Add large frame combustion turbine units (150+ MW range)

located on or near the 345 kV transmission system to establish

a high-voltage transmission backbone


Blackstart Capability Assessment

Installing

Additional

Black Start

Generators

7-Task-Updated

Restoration Plan

Criteria of

Restoration Time

and System Gen.

Capability

Optimal

Installation

Strategy

Restoration Tools

Transmission Path

Generator Start-up

Sequence

Load Pick-up

Optimal Power Flow

•Task 1: Update generator start-up sequence

•Task 2: Update cranking path

•Task 3: Update critical load pick-up sequence

•Task 4: Update transmission path

•Task 5: Update dispatchable load pick-up

sequence

•Task 6: Update islands resynchronization

•Task 7: Update load pick-up sequence


Optimization Modules

Overall System Generation Capability

. . Total Restoration Time

. .

Max

s t Min

s t Critical Time Intervals Constraint

MW Start−up Requirement Constraint

Generator Capability Function Constraints

Generator Start−up Power Function Constraints

Decision Variables Constraints

Transmission Path Search Logic Constraints

Transmission Line Thermal Limits Constraints

GRAs Time Constraints

Modules Connection Constraints

Total

Restoration

Time &

System

Generation

Capability

W. Sun, C. C. Liu, and S. Liu, “Black Start Capability Assessment in Power

System Restoration,” Proc. IEEE PES General Meeting, 2011.


Case Study

A total blackout is hypothesized for one zone in NE ISO system

There are 13 generators, 139 buses and 172 branches

The generation system can be restored in 260 minutes, and the

system generation capability is 4630 MWh

21


Power System Restoration

using Smart Grid Technologies


Renewable Sources Integration

U.S. Department of Energy’s goal: 20% wind by

2030.

Traditional restoration excludes renewable

sources:

Cannot be dispatched like conventional generators

Large scale wind farm penetration challenges:

BSU or NBSU ?

Uncertainty and variability

Load pickup and dynamic reserve

23


Motivation and Challenges

Lack of knowledge in handling the variability and uncertainty

of wind ( due to inaccurate forecasting tools).

As the penetration level of wind power increases, it is critical

to coordinate wind generators with conventional generation

units (ramping events,…)

Need to consider extra dynamic reserve of conventional

generator or utilizing storages (pumped-hydro, …).

Decrease the total inertia of power system which can

threaten the stability of system ( wind turbine type 3 and 4).


Harnessing Renewables in

Restoration

Cases Wind ProfileRestoration Time

(pu)

Wind Power

Spillage (MWh)

1 No wind 36 N/A2 Low wind 36 62.983 High wind 34 308.79

4Constant Wind

Power29 559.04

0 5 10 15 20 25 30 350

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Restoration Time (p.u.)

Po

wer

(p.u

.)

Wind forecasting value

Deterministic solution

Stochastic solution

• A two-stage stochastic MILP-based wind harnessing

strategy to handle wind variability and uncertainty

• Test in IEEE 39-bus system with one 540 MW wind farm

• Wind can contribute in restoration, but with large wind

power spillage


Optimal Distribution System

Restoration using PHEVs PHEVs can accelerate the load pickup by compensating the

imbalance between available generation and distribution

system load

A bottom-up restoration strategy to use PHEVs for reliable

load pickup and faster restoration process

Transmission

Restoration

Obj.: Max Generation Capability

Tasks:

• Gen. Startup Sequence

• Trans. Line Energization

Sequence

Distribution

Restoration

Obj.: Max Served Energy

Tasks:

• Load Pickup Sequence

• PHEVs Charging/Discharging

Sequence

Gen.

Capability

Curve

Load Pickup

Curve

N. Kadel, W. Sun, and Q. Zhou, "On Battery Storage System for Load

Pickup in Power System Restoration," Proc. IEEE PES General Meeting,

2014.


Optimal Distribution System

Restoration using PHEVs Test in a 100-feeder system with PHEV penetration of 0.02%, 0.2%, 2%

0 2 4 6 8 10 12 14 16 18 200

50

100

150

200

250

300

350


Re

sto

red

Lo

ad

(M

W)

Scenario 1 - No PHEV

Scenario 2 - 217 PHEVs



0 2 4 6 8 10 12 14 16 18 200.2

0.3

0.4

0.5

0.6

0.7

0.8

Restoration Time (pu)

Sta

te o

f C

ha

rge

(%

)




0 2 4 6 8 10 12 14 16 18 20 220

0.5

1

1.5

2

2.5

3

3.5


Dif

fere

nc

e b

etw

ee

n G

en

era

tio

n a

nd

Lo

ad

(M

W)

2 4 6 8 10 12 14 16 18 200

100

200

300

400

500

600


Re

sto

red

Lo

ad

(M

W)

Scenario 4 - W/O Tran. & Distr. Coordination

Scenario 4 - W/ Tran. & Distr. Coordination


PMU-based Protection and

Restoration Use PMU data to enhance situational awareness in power

system restoration

Multi-area parallel restoration

Communication-assisted adaptive protection

W. Sun, et al., “Design and implementation of IEC 61850 in communication-

assisted protection strategy,” Proc. IEEE PES T&D, 2014.

MILP optimization

Generator start up

Line Eenrgization

Load Pick up

Parallel Restoration

Multi-area Restoration

Strategy

Area 3 Area 4

Generator start up

Line Eenrgization

Load Pick up

Area 1 Area 2

….

MILP optimization


References

Project funded by NSF

• ECCS-EPCN #1552073, “Collaborative Research: An Intelligent

Restoration System for a Self-healing Smart Grid (IRS-SG)”

• Further Information

• A. Golshani, W. Sun, and Q. Zhou, “Coordination of Wind and Pumped-

Storage Hydro Units in Power System Restoration,” IEEE Transactions on

Sustainable Energy, in revision.

• W. Sun, C. C. Liu, and L. Zhang, “Optimal Generator Start-up Strategy for

Bulk Power System Restoration,” IEEE Transactions on Power Systems,

vol. 26, no. 3, pp. 1357-1366, August 2011.

• N. Kadel, W. Sun, and Q. Zhou, "On Battery Storage System for Load

Pickup in Power System Restoration," Proc. IEEE Power & Energy

Society General Meeting, 2014, National Harbor, MD, 27-31 July 2014.

29

an intelligent restoration system for a self-healing smart ......university of central florida 10...

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