optimal network reconfiguration - binghamton university · 2014-04-09 · optimal network...
TRANSCRIPT
Optimal Network Reconfiguration
Solar Energy Integration and
Multi-objective Power Flow Optimization
G o k t u r k P o y r a z o g l u , H y u n g Seo n O h
S U N Y C o n v e r s a t i o n s i n t h e D i s c i p l i n e s
B I N G H A M P T O N U N I V E R S I T Y
U n i v e r s i t y a t B u f f a l o , S U N Y
Case Study
• Reconfigure the network
• Keep the radial network structure
1. Keep the current operation policy: One directional flow
2. Allow bidirectional power flow
• Single Objective Optimization
• Multi-objective Optimization
1,500
1,700
1,900
2,100
2,300
2,500
2,700
2,900
3,100
0.8 0.85 0.9 0.95 1 1.05 1.1
Op
era
tin
g C
ost
($
)
Load factor
Minimum Operating Cost of the System with Different Topologies
Original Topology 18 Topology 14 Topology 24
TOPOLOGY 18 TOP. 14 TOP. 24
Load Level
Topology in Operation
Original Topology
80% 100%
107%
110% 103%
1,500
2,000
2,500
3,000
0.8 0.85 0.9 0.95 1 1.05 1.1
Op
era
tin
g C
ost
($
)
Load factor
Minimum Operating Cost of the System with Different Topologies
Original Topology 18 Topology 14 Topology 24
SOLAR POWER INTEGRATION with CURRENT POLICY
6
9
15
𝑉6 < 𝑉4 𝑉15 < 𝑉13 𝑉9 < 𝑉8
0
200
400
600
800
1,000
1,200
1,400
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Op
era
tin
g C
ost
($
)
Pe
rce
nta
ge (
10
0%
)
Various Network Topologies
High Demand - High Solar- Current Policy
Consumed Energy Spilled Energy Operating Cost
SOLAR POWER INTEGRATION with PROPOSED POLICY
6
9
15
𝑉6 < 𝑉4 𝑉15 < 𝑉13 𝑉9 < 𝑉8
0
200
400
600
800
1000
1200
1400
75%
80%
85%
90%
95%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Op
era
tin
g C
ost
($
)
Pe
rce
nta
ge(1
00
%)
Various Network Topologies
High Demand - High Solar - Proposed Policy
Consumed Energy Spilled Energy Operating Cost
ANNUAL DATA ANALYSIS
0
2
4
6
8
10
12
14
16
18
Current Policy -Best Topologies
Current Policy -Original
Proposed Policy -Best Topologies
Proposed Policy -Original
Op
era
tin
g C
ost
(M
illio
n $
) Annual Operating Cost Comparison
17% Cost Reduction 29%
Cost Reduction
MULTI-OBJECTIVE OPTIMIZATION
• Minimize
– Operating Cost
– Spilled Energy
• Minimize
– Operating Cost
– Real Power Losses
Multi-objective Optimization
0
200
400
600
800
1,000
1,200
1,400
0 0.5 1 1.5 2 2.5 3 3.5 4
Op
era
tin
g C
ost
($
)
Spilled Energy (MW)
Multi-objective Optimization
Min Cost Min Spilled
Energy
MULTI-OBJECTIVE OPTIMIZATION
• Minimize
– Operating Cost
– Spilled Energy
• Minimize
– Operating Cost
– Real Power Losses
0
200
400
600
800
1,000
1,200
1,400
0 2 4 6 8 10 12 14 16 18 20
Op
era
tin
g C
ost
($
)
Real Power Losses (MW)
Multi Objective Optimization Operating Cost - Real Power Losses
MIN cost MIN loss
297.07
347.07
397.07
447.07
497.07
547.07
597.07
647.07
8.16 8.66 9.16 9.66 10.16 10.66 11.16
Op
era
tin
g C
ost
($
)
Real Power Losses (MW)
Multi Objective Optimization Operating Cost - Real Power Losses
Top.1 Top.3 Top. 10 Top.24 Top.27
THANK YOU
Contact Info: [email protected]
S U N Y C o n v e r s a t i o n s i n t h e D i s c i p l i n e s
B I N G H A M P T O N U N I V E R S I T Y
U n i v e r s i t y a t B u f f a l o , S U N Y