From Theory to Smart Grid Simulator for Assessing and Demonstrating The
for Assessing and Demonstrating The Potentials of New Technologies
Jhi‐Young (J.Y.) Joo, Zhijian Liu and Marija Ilić
Electric Energy Systems Group
gy y
p
Electrical and Computer Engineering
Carnegie Mellon University
Acknowledgements
™Le Xie
e e
ƒ Former PhD student, Assistant Professor at Texas A&M University
™Marija Prica
ƒ PhD student, EESG, ECE CMU
h
d
™Niklas Rotering
ƒ Former visiting Master’s student, PhD student at RWTH Aachen, Germany
,
y
2
DYMONDS — Future electric energy system
DYMONDS Future electric energy system
3
Dynamic Monitoring and Decision‐
making System (DYMONDS) k
(
) [1]
™ Dynamic Monitoring Decision‐making System (DYMONDS)
ƒ Distributed decision making system
™Computationally feasible
p
y
ƒ Individual decisions submitted to ISO (as supply/demand bids)
™Individual inter‐temporal constraints internalized
p
™ The pieces we’ve got
ƒ SSystem operator
t
t
ƒ Wind generation, price‐responsive demand, PHEVs, planning and PMU(phasor measurement unit)s, and more
3
Concepts of DYMONDS Simulator
Concepts of DYMONDS Simulator
5
IEEE 24‐bus Reliability Test System (RTS) in GIPSYS [2]
Jovan Ilić
™11 generator buses (including a slack bus)
(including a slack bus)
ƒ Nuclear, coal, oil, natural gas
™Inelastic demand
4
Current electric power systems
Current electric power systems
5
DYMONDS Simulator
Scenario 1: + Wind generation d
[3,4]
Le Xie
™20% / 50% penetration to
penetration to the system
6
DYMONDS Simulator Scenario 1: + Wind generation
d
7
DYMONDS Simulator i
i
i d
d [3‐5]
Scenario 2: + Price‐responsive demand $
kWh
J.Y. Joo
™Elastic demand ™Elastic
demand
that responds to time‐varying y g
prices
8
DYMONDS Simulator i
i
i d
d
Scenario 2: + Price‐responsive demand
9
DYMONDS Simulator Scenario 3: + Electric vehicles i
l
i
hi l [6]
Niklas Rotering
™Interchange supply / demand
supply / demand mode by time‐
varying prices
y gp
10
DYMONDS Simulator Scenario 3: + Electric vehicles i
l
i
hi l [4]
11
DYMONDS Simulator SScenario 4: + long‐run decision making i 4 l
d ii
ki [4]
?
Marija Prica
j
™ Long‐run planning of new generation capacity installation
ƒ Long‐run marginal bids
ƒ For the next 10 years
14
DYMONDS Simulator Scenario 4: + long‐run decision making
l
d
k
™ Long‐term load forecast
g
™ Coal power plant
p
p
15
DYMONDS Simulator Scenario 4: + long‐run decision making
l
d
k
™ Natural gas power plant
g p
p
™ Long‐run bidding functions
g
g
16
DYMONDS Simulator
S
Scenario 5: + PMU‐Based Robust Control i 5 PMU B d R b
C
l [7]
P
Zhijian Liu
™ Automated Voltage C t l (AVC) d
Control (AVC) and Automated Flow Control (AFC)
P
P
ƒ Design
Design Best Locations Best Locations
of PMUs
ƒ Design Feedback Control Gains
P
17
PMU‐based Automatic Voltage and Flow Regulation
™System Demand Curve [8]
y
[ ]
Every 10 min Real Time Load of NYISO in Jan 23, 2010
220
Forecasted Load
Actual Load with Disturbance
Upper Bound
Lower Bound
200
160
10 min Real Time Load of NYISO in Jan 23, 2010
140
210
Real Power Load P(p.u.)
Real Power Load P
P(p.u.)
180
120
100
200
190
180
170
160
0
0
5
Upper Bound
10
Lower Bound
2
15
Time (hours)
4
6
Time (min)
8
10
20
25
18
PMU‐based Automatic Voltage and Flow R
Regulation
l i
™Robust AVC Illustration in NPCC System [7, 9]
™Robust AVC Illustration in NPCC System [7, 9]
™ Limited System Observability
™ Pilot Point: Bus 76663 il
i
6663
Automatic Voltage Control for ONE Pilot Point Control Case
System Worst V
Voltage Deviation
n (p.u.)
0.25
No Control
One Pilot Point Control
5% Reliability
y Criteria
02
0.2
0.15
0.1
0.05
0
0
20
40
60
Time (sec)
80
100
19
PMU‐based Automatic Voltage and Flow Regulation
™Robust AVC Illustration in NPCC System
™Robust AVC Illustration in NPCC System
™ Full System Observability
A t
Automatic
ti Voltage
V lt
Control
C t l for
f Unlimited
U li it d Information
I f
ti
Control
C t l Case
C
System Wors
st Voltage Deviattion (p.u.)
0.25
0.2
No Control
Full Information Control
5% Reliability Criteria
0.15
0.1
0.05
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
20
PMU‐based Automatic Voltage and Flow Regulation
™Robust AFC Illustration in NPCC System [7 9]
™Robust AFC Illustration in NPCC System [7, 9]
™ Limited System Observability
™ Pilot Point: Bus 75403 il
i
03
Automatic Flow Control for ONE Pilot Point Control Case
System Worst Flow Deviation (p.u.)
0.7
0.6
No Control
One Pilot Point Control
5% Reliability Criteria
0.5
0.4
03
0.3
0.2
0.1
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
21
PMU‐based Automatic Voltage and Flow Regulation
™Robust AFC Illustration in NPCC System
™Robust AFC Illustration in NPCC System
™ Full System Observability
Automatic Flow Control for Unlimited Information Control Case
System Wo
orst Flow Deviation (p.u.)
0.7
0.6
No Control
Full Information Control
5% Reliability Criteria
05
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
Time (sec)
70
80
90
100
22
Concluding remarks
Concluding remarks
™Current power systems simulator
Cu e t po e syste s s u ato
ƒ Centralized optimization
ƒ Information and decision‐making concentrated on ISO
™Future energy systems simulator (DYMONDS)
ƒ Distributed optimization Æ modularized components and decision‐makings
ƒ Appropriate information exchange between the Appropriate information exchange between the
components
ƒ Balance of the system by ISO
23
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
Marija Ilić, Dynamic Monitoring and Decision Systems (DYMONDS) and Smart Grids: One and The Same, EESG Working Paper R WP 21 2009
EESG Working Paper R‐WP‐21, 2009
Jovan Ilić, Interactive Power Systems Simulator, Carnegie Mellon University, https://www.ece.cmu.edu/~nsf‐education/software.html
Marija Ilić, Le Xie and Jhi‐Young Joo, Efficient Coordination of Wind Power and Price‐Responsive Demand Part I: Theoretical Foundations Part II: Case Studies IEEE Transactions on Power Systems
Demand Part I: Theoretical Foundations, Part II: Case Studies, IEEE Transactions on Power Systems, under review, Jan 2010
Marija Ilić, Jhi‐Young Joo, Le Xie, Marija Prica and Niklas Rotering, A Decision Making Framework and Simulator for Sustainable Electric Energy Systems, under review in Feb 2010
g
and Marija
j Ilić, A Multi‐Layered Adaptive Load Management (ALM) System: Information ,
y
p
g
(
) y
Jhi‐Young Joo
exchange between market participants for efficient and reliable energy use, 2010 IEEE PES Transmission & Distribution Conference, April 2010, accepted
Niklas Rotering and Marija Ilić, Optimal Charge Control of Plug‐In Hybrid Electric Vehicles In Deregulated Electricity Markets, IEEE Transactions on Power Systems, accepted in Aug 2009
Zhijian Liu and Marija Ilić, Toward PMU‐Based Robust Automatic Voltage Control (AVC) and Automatic Flow Control (AFC), 2010 IEEE PES General Meeting, July 2010, accepted in Feb 2010 and to be presented
New York ISO Website Data available at http://www.nyiso.com/
E. Allen, J. Lang, and M. Ilic, “A Combined Equivalenced‐Electric, Economic, and Market Representation of the Northeastern Power Coordinating Council U.S. Electric Power System,” IEEE Transactions On f h
h
d
l
l
”
Power Systems, vol. 23, no. 3, pp. 896‐907, Aug 2008.
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