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Using Active Customer Participation in Managing Distribution Systems
Visvakumar Aravinthan Assistant Professor
Wichita State University
PSERC Webinar
December 11, 2012
Outline
� Introduction to distribution advancement
� Limitations with current operation states
o Some examples
� Improving reliability of the systems
� Active consumer participation
� How to unify consumer participation with
distribution operation
2
Smart GridDistribution Advancement
Introduction
3
Smart Grid
� What would be new in smart grid1
o Self-healing from power disturbance events
o Enabling consumer active participation
o Resilient against physical and cyber attack
o Power quality for 21st century needs
o Accommodating all generation and storage
o New products, services, and markets
o Optimizing assets and operating efficiently
4
[1] Department of Energy, Online: http://energy.gov/oe/technology-development/smart-grid
Current State
5
Gen. Trans. Dis. Con.
Self-healing
Consumer participation
Physical and cyber attacks
Power quality
Generation and storage
Markets
Asset Management
Distribution: What Can Be Done?
6
ISO
New Operation- Measurements - Communication - Control paradigms- Components - Data management
Price Info.Emergency Operation
Load expectation State of operation
DSM- Consumer acceptance
- Price elastic load- Data sharing issues
Distributed Resources
Reliability - Component life- Consumer satisfaction
Self – healing - Consumer awareness- DSM to manage load
shedding
Emission Mitigation
Market enabled - Flexible grid - Efficiency
Directives- Distribution pricing- Direct control
Distribution
Advancement
Distribution: Advancement
7
Distribution
Advancement
Distribution: Advancement
8
Distribution
Advancement
Distribution: Advancement
9
Distribution
Advancement
Distribution: Advancement
10
Distribution
Advancement
Distribution: Advancement
11
Consumer Participation
DSM
Objectives
Load
Shape
Request
Data
Request
12
Consumer Participation
DSM
Objectives
Load
Shape
Request
Data
Request
Regulatory
Requirements
13
Consumer Participation
DSM
Objectives
Load
Shape
Request
Data
RequestC
on
sum
er
Priv
acy Regulatory
Requirements
14
Imp
act
An
aly
sis
Utility Cost Benefit
Analysis
Consumer Participation
DSM
Objectives
Load
Shape
Request
Data
RequestC
on
sum
er
Priv
acy Regulatory
Requirements
15
Distribution OperationExamples
16
Reliability
17
Low level
anomalies
for 6 days Animal Contact Power restored
in 1 hour
[1] R. Moghe, M. Mousavi, J. Stoupis, J. McGowan, “Field investigation and analysis of incipient faults leading to a catastrophic failure in an underground distribution feeder,” in Proc.
of Power Systems Conference and Exposition (PSCE), Seattle, Washington, May 2009
[2] D Russell, R. Cheney, T. Anthony, C. Benner, C. Wallis and W. Muston, “Reliability Improvement of Distribution Feeders”, In proc. 2009 IEEE PES General Meeting, Calgary
Canada, July 2009.
� In a power systemo Lots of data available
o Little information extracted
� Example 1: Moghe et. al.
� Example 2: Russell et. al.
Reliability
18[1] S. Argade, V. Aravinthan, and W. Jewell “Probabilistic Modeling of EV Charging and its Impact on Distribution Transformer Loss of Life ,” in Proc. 1st IEEE International Electric
Vehicle Conference, March 2012
� Electric Vehicle Charging o Different charging loads on a distribution transformer
o Loss of life of distribution transformers
0
0.5
1
1.5
0:00 6:00 12:00 18:00 0:00
Po
we
r C
on
sum
pti
on
(kW
)
No Electric VehiclesAll Charging at Same Time1/2 hour delay in chargingRandom Charginglate Night (Controlled)
Reliability
19[1] V. Ravindran, V. Aravinthan, and W. Jewell “Impacts of High Penetration Distributed PV Sources on Voltage Regulation,” in Proc. 43rd Frontiers of Power Conference, Oct. 2012
� Voltage regulator operations
o Distributed generation at feeder/lateral level
o IEEE 13 bus system
o Distributed solar PV at 40% penetration
0.0
0.2
0.4
0.6
0.8
1.0
0.9
0 500000 1000000 1500000
BASE CASE
Electrotek Concepts® TOP, The Output Processor®
SIT
E1-V
A_W
F (V
)
SIT
E1-V
A_W
F (V
)
Time (ms)
LOAD PROFILE TAP CHANGES
0.0
0.2
0.4
0.6
0.8
1.0
0.9
1.1
0 500000 1000000 1500000
Electrotek Concepts® TOP, The Output Processor®
SIT
E1-V
A_W
F (V
)
SIT
E1-V
A_W
F (V
)
Time (ms)
PV LOADSHAPE TAP CHANGE
0.0
0.5
1.0
1.5
2.0
0.9
0 500000 1000000 1500000
BASE CASE WITH PV
Electrotek Concepts® TOP, The Output Processor®
LO
AD
SH
APE
-VA
_W
F (V
)
TA
PC
HA
NG
E-V
A_W
F (V
)
Time (ms)
PV LOADSHAPE TAPCHANGE
Increase
Distributed Generation
� Impacts of geographically scattered DGs
o Voltage rise with 30% PV penetration on IEEE 123 test feeder
20[1] V. Ravindran, V. Aravinthan, and W. Jewell “Impacts of High Penetration Distributed PV Sources on Voltage Regulation,” in Proc. 43rd Frontiers of Power Conference, Oct. 2012
Distributed Generation
21[1] V. Ravindran, V. Aravinthan, and W. Jewell “Impacts of High Penetration Distributed PV Sources on Voltage Regulation,” in Proc. 43rd Frontiers of Power Conference, Oct. 2012
Note: Red – below 1 p.u, Green – 1-1.02 p.u, Blue – above 1.02 p.u
Base Case
30% PV Penetration
Distribution Automation
22
� Location on the Feeder and the Frequency
o 5 houses connected to a single transformer
5 houses connected to a transformer
Hot Summer day in Kansas
1 minute average
Distribution Automation
� Location on the Feeder and the Frequency
o 5 houses connected to a single transformer
5 houses connected to a transformer
Hot Summer day in Kansas
5 minute average
23
Distribution Automation
� Location on the Feeder and the Frequency
o 5 houses connected to a single transformer
o Is the missed information useful …
24
Future Needs
25
� How to connect distribution necessities with
active consumer participation
o Utility
• Improve distribution system operation with better
observability
• Connection between DG to load
o Consumer
• Looks for maximum satisfaction
• Would not like to share the information
Distribution Reliability
System Requirements Dynamic Pricing
Consumer Participation
Smart GridDistribution Operation
Reliability Based Operations
26
Condition Assessment
27
� To improve distribution
reliability requires a tool
to determine condition of
components
o Lack of communication limits assessments
� Observing failure modes
improve assessment
o Identify criteria that are observable
Criterion
Gen
eral
Age of the Transformer
Experience with Transformer
Noise Level
Loading Condition
Core & Winding Losses
Win
din
g
Condit
ion Winding Turns Ratio
Condition of Winding
Condition of Solid Insulation
Partial Discharge (PD) Test
Oil
Condit
ion Gas in Oil
Water in Oil
Acid in Oil
Oil Power Factor
Physi
cal
Condit
ion Condition of Tank
Condition of Cooling System
Condition of Tap Changer
Condition of Bushing
[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
Condition Assessment
28
� Develop a failure rate function for each
criterion using
o Historic data if available
o Else, standards or guidelines if available
o Else, hypothetical functions (experience)
� Historic Data (Transformer)
o Example: Age of the component
[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
Condition Assessment
29
� Develop a failure rate function for each
criterion using
o Historic data if available
o Else, standards or guidelines if available
o Else, hypothetical functions (experience)
� Historic Data (Transformer)
o Example: Gas in the oil
[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
Status TDCG (ppk) Remarks
1 < 0.72 Normal aging of oil
2 0.72 – 1.92 excess oil aging
3 1.92 – 4.63 Excessive oil aging
4 > 4.63 Very poor oil condition
StandardsEg: IEEE std. C57.104-2008
• Define R(t) for 2 status or
• Define R(t) for 1 status and 1
parameter
Find the unknown parameters
Condition Assessment
30
� Develop a failure rate function for each
criterion using
o Historic data if available
o Else, standards or guidelines if available
o Else, hypothetical functions (experience)
� Historic Data (Transformer)
o Example: Location of the transformer
[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
No enough
informationF – Total no of transformers failed
s – Total no of similar transformers handled
SF – Total no of similar transformers failed
SU – Total no of similar transformers with unknown
cause
Condition Assessment
31[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
� Problem: Not all criteria have equal influence on
component failure !!!
� Solution: Use weighted reliability function
o Weighted Reliability Function
� Once the weighted reliability functions are known
o Series parallel topology for component
� Quantitative: Component Condition Score
� Qualitative: Component Condition Report:
Example: Distribution Transformer
Condition Assessment
32[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
No
rmal
10
0-9
0 %
Defective 90–80 %
Fau
lty 2
0–
10
%
Fai
led
10
–0
%
Fai
r
Mil
d
Sat
isfa
cto
ry
Sta
ble
Ser
iou
s
Cri
tica
l
Ex
trem
ely C
riti
cal
Age: 18 yrs TDCG: 1.8 ppk SF=40, S
U=10, F=90 & s=60
Electric Vehicle Charging
24 Bus IEEE Reliability
Test System
13 Bus IEEE Test Feeder
• Assumed 20% EV Penetration in
Busses Zone 3, 4, 5.
• Type 1 charging assumed, slow
charging will contribute to
minimum impact on the system
• Renewable generation / storage is
included to at Bus 8 for the 3rd part
33
Electric Vehicle Charging
� Two levels of optimization,
o Level 1: Schedule day ahead charging (request sent by consumers in advance)
• Objective: Minimize the system average
interruption duration index (SAIDI) (maximize
performance)
• Constraints:
• Transmission congestion
• All vehicles requesting charging are charged
• All vehicles are charged when they are available
• None of the system components are overloaded
34
Electric Vehicle Charging
� Two levels of optimization,
o Level 2: Find the maximum number of vehicles charged in real time
• Objective: Maximize the number of vehicles that
could be charged
• Constrains:
• Acceleration of loss of life of the transformer
• Maximum cap on the CO2 emission
• Optimum number of vehicles from level 1 is charged
35
Zone 5: Moderately loaded feeder section
Part 1: No renewable, same level of CO2 emission as traditional vehicles allowed
Electric Vehicle Charging
36
Zone 5: Moderately loaded feeder section
Part 2: With renewable 80% of CO2 emission as traditional vehicles allowed
Electric Vehicle Charging
37
Smart GridDistribution Advancements
Consumer Participation
38
Active Consumer Participation
39
� Coordinating EV charging
o Develop a price model to control the EV charging time• Assume that there are ��
�number of vehicles that could be
charged without degrading the performance at time i
� Vehicles could schedule charging time one day ahead
• What if there are more vehicles wanting to be charged
o Two level of pricing one for vehicles scheduled other of the additional vehicles
• Objective is to minimize both the prices
Active Consumer Participation
40
� Limiting Factors
o Consumers prefer to charge at convenience
• Generally consumer
anxiety increases if the
charging is delayed
o Limit consumers who are not satisfied
• More charge more
anxiety
• More availability less
anxiety
Active Consumer Participation
41
� Limiting Factors
o Price:
o Component Condition
• Most critical component: Transformer
• based on IEEE std. C51.97 transformer hotspot
temperature should be limited to
�� > ������� + � ��� + ���������
Reference PriceAdditional Power Loss
due to Large Loads
Distribution
Overloading
�� + ���� + ��� < ����
Ambient Temp.
.Top oil temp. rise
over ambient
Hot spot temp. rise
over top oil
Active Consumer Participation
42
� How consumer anxiety affects additional
EVs connected to the grid
Active Consumer Participation
43
� Distributed generation for improvement in
performance
o Example: Minimize the feeder power loss with the DG penetration
• Using exact lumped model
o Allow DGs with active power control mode
o Reactive power is supplied to minimize power loss
o But maximum power factor is limited at generation
Active Consumer Participation
44
� For the IEEE 13 bus feeder
� For the IEEE 34 bus feeder
0
25
50
75
100
6 11 16
Po
wer
Lo
ss (
Kw
)
Time (h)
At 0.9pf limit
At 0.95pf limit
0
10
20
30
6 11 16
Po
wer
Lo
ss (
Kw
)
Time (h)
At 0.9pf limit
At 0.95pf limit
Smart GridDistribution Operation
Connecting Both Together
45
� Consumer participation
� EV charging
� Condition (Reliability) based reconfiguration
� Operating beyond IEEE 1547 (reactive power control)
Unification
46
Time of the Day Time of the Day
Lo
ad
Lo
ad
Load Shifting Flexible Loading
Distribution Transformer
Support Slides
48
Condition Assessment
49[1] V. Aravinthan, W. Jewell, and W. Jewell “Identifying worst performing components in a distribution system using Weibull distribution,” in Proc. 11th International Conference on
Probabilistic Methods Applied to Power Systems, June. 2010
• Lets assume oil is bad
and TDCG 4 ppk
Criterion Weight R(t)
Faults seen by the transformer 0.70 0.80 0.860
Geographical location 0.60 0.90 0.940
loading 0.80 0.80 0.840
Age 0.90 0.76 0.784
Noise 0.40 0.90 0.960
Condition of winding 0.90 0.80 0.820
PD test 0.50 0.82 0.910
Core and winding loss 0.80 0.80 0.840
Condition of solid insulation 0.80 0.88 0.904
Tap changer condition 0.60 0.91 0.946
Winding turns ratio 0.70 0.95 0.965
Gas in oil 0.90 0.12 0.108
water in oil 0.90 0.87 0.883
Acid in oil 0.90 0.89 0.901
Oil PF 0.90 0.90 0.910
Tank condition 0.90 0.92 0.928
bushing condition 0.90 0.90 0.910
hot spot temperature 0.70 0.80 0.860
cooling system 0.70 0.80 0.860
Experience 0.50 0.03 0.515
No
rmal
10
0-9
0 %
Defective 90–80 %
Fau
lty
20
–1
0 %
Fai
led
10
–0
%
Fai
r
Mil
d
Sat
isfa
cto
ry
Sta
ble
Ser
iou
s
Cri
tica
l
Ex
trem
ely C
riti
cal
Zone 5: Moderately loaded feeder section
Part 2: No renewable 80% of CO2 emission as traditional vehicles allowed
Electric Vehicle Charging
50
Active Consumer Participation
51
� How location would influence the price
Active Consumer Participation
52
� For the IEEE 13 bus feeder
0
0.2
0.4
0.6
0.8
1
0 4 8 12 16 20 24
Po
wer
(p.u
.)
Time (h)
Load1 PV
0
50
100
150
200
6 11 16Cu
rren
t (A
mp
ere
)
Time (h)
Iag1 Iag2 Iag3
0
50
100
150
200
6 11 16
Cu
rren
t (a
mp
ere
s)
Time (h)
Iag1 Iag2 Iag3
