Harnessing Renewables in Power System Restoration

Dr. Wei Sun, and Amir Golshani Assistant Professor, EECS Dept.

University of Central Florida (South Dakota State University)

Panel: Cascading Failures: Advanced Methodologies, Restoration and Industry Perspectives 2015 IEEE PES General Meeting, Denver, July 28, 2015

Resilient Smart Grid

• Allows power disturbances to be instantly detected and handled with minimal customer impact.

• Real-time monitoring and reaction using high performance IT infrastructure: • System to constantly tune itself to an optimal state

• SCADA to PMU (2‐4 times/sec to 20‐50 times/sec)

• Rapid isolation and restoration without human intervention: • Isolate parts of the network that experience failure from the rest of

the system.

• Enables a more rapid restoration to reduce outage time.

2

Power System Restoration

• Developed Power System Restoration strategy (PSERC, EPRI projects):

Partitioning power grid into islands

Start up black start units

Establish transmission lines

Crank non-black start units

Serving Loads

Synchronize restored islands

Connect with neighboring systems

3

Adaptive Restoration Tool

t=0

Restoration planning

t=4

Real-time security check

Total Blackout

Restoration time (p.u.)

Cplex Python PSS/E

- Gen. start-up seq.

- Line energ. seq.

- Load pick up step

- Dynamic reserve calc.

- Voltage stability check

- Renewable sources participation

- Energy storages contribution

MILP robust

optimization

Dynamic Simulation

Generate restoration cases &

run PSS/E

- Load pick up step calculation

t=15BSU is connected and first load/line

to be energized

Major contingency or equipment failure

occurred

New restoration planning

- Lines switching and overvoltage check

t=9

Real-time Security check

- Load pick up step calculation

First NBSU is connected

- Contingency analysis- Voltage stability margin calculation

- Optimal load flow

- Line energ. seq.

- Gen. start-up seq.

- Dynamic reserve calc.

- Connection to the neighboring island

G1

G10

G2

G3G4

G5

G6

G7

G8

G9

PMU

PMUPMU

PMU

PMU

PMUPMU

PMU

.

Power system under restoration

System Data Base (sav,dyr, snap, xls,...)

PDCSEL-3373

Real-time phasor measurement data

Measuremet & record

4

Case Study – Test System

5

• IEEE 39-bus system with one BSU (G10) and 9 NBSUs (G1-G9)

• The total generation capacity is 6,250 MW, and total active and reactive loads are 6,150 MW and 1,800 Mvar, respectively

• Frequency is regulated between 59.5 Hz and 61.0 Hz

• Voltage is maintained between 90% to 105% of nominal value

• The load was modeled as 25% constant current, 25% constant impedance, and 50% constant power

G1

G10

G2

G3G4

G5

G6

G7

G8

G9

1BUS 1

1.01.0

2BUS 2

1.01.0

39BUS 39

1.01.0

-173.6

-40.2

174.6

-24.4

76.0

-4.0

-76.0

-74.7

97.6

44.2

1104.0

250.0

1

1000.0

78.5

R

30BUS 30

1.01.0

-250.0

-147.4

1.025

1

250.0

162.0

10

250.0

162.0

R

25BUS 25

1.11.1

26BUS 26

1.11.1

37BUS 37

1.01.0

65.5

-17.3

-65.3

-38.5

-538.3

63.9

1.025

1

540.0

0.1

82.9

248.9

-93.8

224.0

47.2

139.0

8

540.0

0.1

R

17.0

-244.6

28BUS 28

1.11.1

29BUS 29

1.11.1

-347.6

28.5

349.2

-39.2

-140.8

-21.4

-56.1

206.0

27.6

-190.2

-25.1 192.1

-67.6

283.5

26.9

141.6

38BUS 38

1.01.0

-824.8

79.9

1.025

1

830.0

22.2

830.0

22.2

R

9

3BUS 3

1.01.0

4BUS 4

1.01.0

18BUS 18

37.2

113.2

-37.0

-132.7

-40.6

-14.4

40.6

-8.1

319.9

89.0

-318.6

-101.2

322.0 2.4

30.0

184.0

500.0

158.0

1.01.0

27BUS 27

1.01.0

16BUS 16

10.8

-198.6

24.6

-43.4-2

4.6

9.1

223.8

-42.6

-223.5

32.5

257.3

68.1

-256.4

-84.6

281.0

75.5

32.3

198.9

1.01.0-2

1.9

17BUS 17

5BUS 5

1.01.0

6BUS 6

1.01.0

8BUS 8

1.01.0

-537.3

-44.6

537.9

47.7

339.2

47.6

-338.2

-49.4

-197.8

-5.6

198.1

-2.9

522.0

176.0

329.4

7BUS 7

1.01.0

218.8

-10.8

-218.6

5.3

453.9

82.1

-452.6

-74.2

233.8

85.0

9BUS 9

1.01.02

8.1

-31.1

-28.0

-96.8

34.9

-131.9

-34.6

97.6

6.5

-66.5

31BUS 31

1.01.0

-669.1

-88.4

1.07

1

669.1

216.6

4.6

678.3

9.2

221.2

R

2

11BUS 11

1.01.0

12BUS 12

1.01.0

10BUS 10

3.6

30.2

1.006

1

-3.5

-29.8

-322.7

-41.4

323.4

35.7

327.4

63.0

-327.0

-65.9

8.5

88.0

34BUS 34

1.01.0

20BUS 20

1.01.0

-505.5

-116.6

1.009

1

508.0

166.9

5

508.0

680.0

103.0

166.9

R

32BUS 32

1.01.0

-650.0

-111.4

1.07

1

650.0

207.5

650.0

207.5

R

3

13BUS 13

1.01.0

14BUS 14

1.01.0

317.2

-8.3

-316.3

0.5

32

2.6

-32

2.2

-51

.4-5

.0

-58.2

1.006

1

59.7

-265.2

-45.6

265.8

5.0

1.01.04

8.3

19BUS 19

1.11.1

33BUS 33

1.01.0

-629.1

-49.9

1.07

1

632.0

108.7

174.7

-9.3

1.06

1

-174.5

13.6

-451.3

-54.7

454.4

59.3

4

632.0

108.7

R

23BUS 23

1.01.0

24BUS 24

1.01.0

36BUS 36

1.11.1

22BUS 22

1.11.1

353.8

1.5

-558.6

-22.4

1

1

560.0

100.3

42.8

41.9

-42.8

-61.7

247.5

-42.7

-97.4

42.7

90.7

308.6

-92.2

1

560.0

100.3

R

84.6

21BUS 21

1.01.0-6

04.4

-87.4

108.3

14.4

330.4

-27.6

274.0

115.0

607.2

-329.6

-0.4

35BUS 35

1.01.0

-650.0

-150.2

1.025

1

650.0

210.8

6

650.0

210.8

R

-351.3

1.01.0

15BUS 15

1.01.0

50.5

-41.3

-50.4

4.3

-269.6

-157.3

270.4

148.0

1

320.0

153.0

40.8

1

Case Study – Steady-State Performance

6

Voltage Stability Index Load Flow and Bus Voltage

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

Restoration Time (p.u.)

Vo

lta

ge

Sta

bilit

y In

de

x

25

44

4

12

1212

1212

12

1212

1515

2323

20202020202020202020

20

202121212121

Case Study – Dynamic Performance

7

Switching Transient Voltage at

Step 4

Pick up 92.5 MW and 35 MVar

load at t=10

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

Dynamic reserve constraint

Load pick up limit

8

Restoration Using Large-scale Wind Farm

• Robust optimization for planning stage:

Wind profile

Uncertainty set

Impacts of budget of uncertainty

• Objective function:

Maximizing the total load pickup and harnessing renewable sources

9

Testing Restoration Planning Strategy

• Probabilistic analysis: Development of multi-run

simulation tools using PSS/E power system software and Python language.

Probabilistic load flow and voltage stability analysis.

Study the effects of wind variability on power system operation during the restoration process.

10

Multi-Run Simulation Platform

Calculate

Correlation

Factor Matrix

& Total Power

Output

Wind Farm Speed Model

1-4

CSV File

Format

Matlab Environment

Total Power Ouput

LHS

Sampling

Method

PSSE

Environment

Python Programming

Environment

Power system Static and

Dynamic Analysis Tools

CSV File

Format

Matlab Analytical

Tools

Coordination between PSH and Wind

• Pumped Storage Hydro (PSH) can be employed to address challenges associated with large scale wind integration.

Compensating ramping events

Coping with wind uncertainty

Providing dynamic reserve

Minimizing wind curtailment

Reduced time of self-healing process

11

12

• Using pumped-storage hydro:

Store wind farm energy spillage at initial steps of restoration.

Utilizing this energy to pick up load and expedite restoration process.

Coordination between PSH and Wind

• Modified IEEE-39 bus with one 500 MW wind farm operated as a NBSU and two 180 MW PSH units

13

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

Case Study Results

Deterministic vs. stochastic wind dispatching

5 10 15 20 25 300

1

2

3

4

5

6

Restoration time (p.u.)

Win

d f

arm

po

wer

(p.u

.)

Forecasted power

Case 2- scenario 1- scheduled power

Case 3- scenario 1- scheduled power

Robust Wind-PSH Coordination

Offshore Wind Farms

• Large offshore wind farms can be used as BSUs in restoration Supply energy and provide ancillary services, with voltage control and

frequency regulation.

• VSC-HVDC Technology Can be connected to the weak power network and control the voltage

and frequency

• Black start capability Connection to the neighboring grid, and provide enough inertia for

self-healing process

Connection to the onshore/offshore wind farm, and provide negligible inertia

14

Self-healing Process with HVDC

• Communication-based Strategy

Perceive the change of the onshore grid frequency with the proposed inertia support control strategy.

Real-time reliable communication links enable remote offshore wind farms to participate in primary frequency control.

• Communication-less Strategy

The relationship between the WF-VSC output frequency and the onshore grid frequency is based on the change of the DC voltage.

Using droop characteristics on both the onshore and offshore converters, and frequency variation on the offshore side is proportional to that on the onshore side.

On-going collaboration with Dr. Nilanjan Chaudhuri at NDSU.

15

Conclusions

• Adaptive restoration tool is designed to introduce flexible restoration strategies that can be updated and guarantee power system security.

• Using wind farm as a BSU necessitate to activate inertial and droop control.

• Wind-PSH unit can mitigate wind variability and uncertainty during the self-healing process.

• Offshore-wind farm together with VSC-HVDC can be used to start up power system as sources after blackouts.

16

References

• Project funded by NSF

• ECCS-EPCN #1408486, “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.

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