Smart Grid EMS with Source-Grid-Demand Synergy
Boming Zhang, Ph D, Prof., IEEE Fellow Dept. of Electrical Engg.
Tsinghua University
12th EPCC Workshop, Bedford Springs, PA USA, 2-5 Jun 2013
2
• Smart Grid EMS is needed for modern EPCC which manages energy and controls power flow in a source-grid-demand coordinated way so as to accommodate more renewables in the network.
• Distributed autonomy and centralized coordination will be the key feature of the Smart Grid EMS.
• Distributed autonomy(Agent): many distributed μ-EMSs are built to control local objectives such as substation, wind farm, EV charger, μ-network, etc.
• Centralized coordination: a higher level EMS to coordinate μ-EMSs to achieve an overall benefit of security, economy and electrical quality.
• All these tasks will be implemented by the family of Smart Grid EMS.
Summary
Outline
Background and key scientific problems 1
Contents and methods
2
Preliminary results 3
4
3
Outline
Background and key scientific problems 1
Contents and methods
2
Preliminary results 3
4
4
5
2 1 3
6
7
•
•
8
•
•
9
EMS
10
(3) EMS suitability
11
12
• This proposal has been approved by Special Fund of the National Priority Basic Research of China (973 Project)
• Fund: YMB 38 million Ruans • Duration: Jan 2013 -- Aug 2017
Outline
Background and key scientific problems 1
Contents and methods
2
Preliminary results 3
4
13
2.1 What we have to do
14
Title
Title
Title
Title
Title
Title
15
EMS
W-EMS
SVC/STATCOMSVC/STATCOM
P-EMS
SVC/STATCOMSVC/STATCOM
minU
J (U ),U ={u0,u
1,u
2,...,,u
M}
s.t. xk+1
= f (xk,u
k,r
k),k = 0,1,..., N -1
g(xk,u
k) £ 0,k = 0,1,..., N -1
MPC
16
g1
T ¶J1
*
¶x+¶L
1
¶u1
= 0
gN
T ¶JN
*
¶x+¶L
N
¶uN
= 0
ì
í
ïïï
î
ïïï
1
10
min ( , , )
= ( ( ), ( ), , ( ))
ii N
u
i N
J u u
L x t u t u t dt
L
L
f (x)+ gi(x)u
ii=1
N
å = 0
Game model
17
1
1 1
1 11 1
1 1
1 11 1
TT T T
T T
Y Y Y YI Y Y I
1
1 11 1 1T T
V Y I Y V
1
1
1
Ki
TT TT TT
i
Ki
T T T
i
T TT T
Y Y Y
I I I
V Y I
2
1
1
i
TT Ti ii iT
i
T Ti ii i
Y Y Y YI Y Y I
1
i ii i iT T
V Y I Y V
k
1
1
K
TT TK KK KT
K
T TK KK K
Y Y Y YI Y Y I
1
K KK K KT T
V Y I Y V
1 1,TT T Y I
TV
,i i
TT T Y I
TV
K
TIK
TTY
TV
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0
( )[ ( ), ( ), , ] [ ( ), , ] ( )
(0)
( ) [ ( ), , ] [ ( ), , ] ( )
[ ( ), ( ), , ]
x x
y y
xy
d tt t t t t t
dt
t t t t t t
t t t
X F X U θ G X θ ε
X X
Y F X θ G X θ ε
0 F X Y θ
% % %
%
% % %
%
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 533
33.5
34
34.5
35
35.5
36
t(s)
/(°
)
19
PV Wind
Thermal Hydro
EV
1 2min [ ( ), ( )]
s.t. ( )
( )
J J
00
x xg xc x
20
Distributed autonomy
Centralized coordination
Distributed autonomy
21
D-EMS S-EMS T-EMS V-EMS
B-EMS
H-EMS
W-EMS
μ-EMS
P-EMS
EMS Suitability
22
2.2
•
EMS with S-G-D synergy
Outline
Background and key scientific problems 1
Contents and methods
2
Preliminary results 3
4
23
3.1 Autonomic Voltage Control in Hebei
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May 12th, 2012
5 kV higher
Wind EMS— C
Hebei Province
Hongda, with control 67 1.5MW WGs 1 MVR SVC
Batou, without control
Guyuan area: installed wind generation capacity >3GW daily generation 1GW
Autonomic control points: 21 wind farms
Coordinated control point: Area control center
Coordinated Voltage Control in Hebei
Before control (11-13-2011)←→ After control (10-18-2012)
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• online rolling MW schedule
3.2 MW dispatch and control to accommodate large scale wind power integration
Real-time control Day ahead Rolling schedule Real-time
dispatch
24h 4h 15m 10s
• Model Predictive Control(MPC) is used in each time level
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Close Loop MW Control in Qian-An Wind Farms in Jilin Province
1
2
Qian-An WF • 27 WFs, installed wind
generation capacity 2936MW, 36% of max load and 62% of min load
(2011 ) 2012
Wind power Spillage
3.3 Wind
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NC
PC
Abandoned wind energy is reduced by 43%
IEE
E 14 nodes case
UC CC
UC CC
3.4
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S-SE Time Consuming(ms)
FC 23.8
YQ 10.9
SZ 5.2
DT 17.3
Pic. Trans. <5.9s
Model Trans. <4.6s
RT Data Import 0.2s
CC-SE Time Consuming 0.7s(1260 nodes)
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
估计前 估计后
0.00%
0.05%
0.10%
0.15%
0.20%
估计前 估计后
Monte Carlo simulation results( 1000,RTDS)
Before SE After SE
Before SE After SE
Conclusion
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Distributed Autonomy - Centralized Coordination
Synergy
New G of EMS
Thanks!