Considering impacts of PEVs in planning optimal hybrid systems
Hamed V. HAGHIM. A. GOLKARS. M. HAKIMI
Frankfurt (Germany), 6-9 June 2011
General Outline
Hybrid Active System with PEVs - Modeling
The Stochastic-Heuristic Algorithm
Results
Conclusion
Haghi – Iran – RIF Session 4 – Paper 0664
Main Topics
2
Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Studied Problem: Optimal Sizing - both the generation side and the load side are distributed
H OH
HH
H+ - H
Wind Generation
Converter/Controller
PEVs
Base Load
Electrolyzer H2 Tank Fuel Cell Reactor/Reformer
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Frankfurt (Germany), 6-9 June 2011
High penetration of stochastic energy flows spatially distributed throughout Microgrid
Variable generation (Wind, PV, etc) Variable load demand (PEVs, etc)
Representation of PEV load variations daily load shape locational displacement
Haghi – Iran – RIF Session 4 – Paper 0664
General Outline
4
Frankfurt (Germany), 6-9 June 2011
Strong dependence structure of load, generation and storage behavior over a year
Time dependence
Wind power autoregressive behavior impacts in planning storage (Markov chain fails for example)
Multivariate dependence
Correlation between load and generation
Haghi – Iran – RIF Session 4 – Paper 0664
General Outline
5
Frankfurt (Germany), 6-9 June 2011
Planning for net load capture both spatial and temporal diversity of PEV
Stochastic simulation (Monte Carlo approach) variability of PEV load on a multivariate modeling
Particle swarm optimization (PSO) optimization subroutine
Haghi – Iran – RIF Session 4 – Paper 0664
General Outline – Scenario-based Optimization
6
Frankfurt (Germany), 6-9 June 2011
General Outline
Hybrid Active System with PEVs - Modeling
The Stochastic-Heuristic Algorithm
Results
Conclusion
Haghi – Iran – RIF Session 4 – Paper 0664
Main Topics
7
Frankfurt (Germany), 6-9 June 2011
Multi-objective optimization problem - weighted sum method
Haghi – Iran – RIF Session 4 – Paper 0664
Hybrid Active System - Optimization
tan ( ) max
_ ( ) _ ( ) ( )
Minimize
with respect to
subject to 0
0
/
nx
n
n
n
k i
wt comv i fc conv i load i conv
NPC
N
N
E E
P P P
_ ( ) fc conv i fc fcP N P 8
Frankfurt (Germany), 6-9 June 2011
By inserting impacts of PEVs on net load of system through a multivariate modeling
PEVs can cause a reversal of power flow through the distribution system
distribution network rely on a coincidence factor of loads for sizing all of the system’s components
Haghi – Iran – RIF Session 4 – Paper 0664
Hybrid Active System with PEVs
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Frankfurt (Germany), 6-9 June 2011
Probability of coincident operation of PEVs is much higher
PEV controlled charging Actual demands are quite modest compared to
normal electricity demands Additional benefits as some kind of DSM controlled charging, with 20% randomness
Haghi – Iran – RIF Session 4 – Paper 0664
Hybrid Active System with PEVs
10
Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
PEVs Impact – Scenario-based Representation
0.2 0.4 0.6 0.8 10
200
400
600
800
Peruint Net Load With 20% Controlled PEV
Pro
babi
lity
0.2 0.4 0.6 0.8 10
100
200
300
400
500
600
Peruint Net Load without any PEV
Pro
babi
lity
11
Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Modeled planning dataset
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
0.75
0.8
0.85
0.9
0.95
1
0.75
0.8
0.85
0.9
0.95
1
-0.5
0
0.5
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
0.75
0.8
0.85
0.9
0.95
1
0.75
0.8
0.85
0.9
0.95
1
-0.5
0
0.5
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
500 1000 1500
500
1000
1500
0.75
0.8
0.85
0.9
0.95
1
0.75
0.8
0.85
0.9
0.95
1
-0.5
0
0.5
Net load with no PEV
Net load with 20% partially controlled PEV demand based on DSM indexes
wind speed
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Frankfurt (Germany), 6-9 June 2011
General Outline
Hybrid Active System with PEVs - Modeling
The Stochastic-Heuristic Algorithm
Results
Conclusion
Haghi – Iran – RIF Session 4 – Paper 0664
Main Topics
13
Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Scenario-based Optimization
Base load
Modeling PEV Load
Net Load ModelModeling wind
generation
Correcting for errors
Select scenario
Optimization
Save size set
No
Correlation analysis
All scenarios covered?
Scenarios, all together, represent long-term behaviour of PEV load and wind
Optimal set, considering uncertain variables space, to be analysed
14
Frankfurt (Germany), 6-9 June 2011
General Outline
Hybrid Active System with PEVs - Modeling
The Stochastic-Heuristic Algorithm
Results
Conclusion
Haghi – Iran – RIF Session 4 – Paper 0664
Main Topics
15
Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Results – Benefits of Adding Controlled PEV
-0.2 0 0.20
1000
2000
3000
4000
Load Difference Distribution
Pro
babi
lity
-4 -2 0 2 4
x 107
0
10
20
30
X: 2.196e+006Y: 9.347
Pro
babi
lity
Optimal Costs Difference
Distributions of differences when the results of scenarios without PEV are subtracted from the results of scenarios with 20% PEV penetration
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Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Differences – Optimal Sizes with and without PEVs
-400 -200 0 200 4000
5
10
15
20
25
30
X: 5.355Y: 12.4
WT Size Difference
Pro
babi
lity
-1000 0 10000
10
20
30
40
50
X: 82.01Y: 14.66
FC Sizes Difference
Pro
babi
lity
-2 -1 0 1 2
x 104
0
5
10
15
20
X: 258.7Y: 8.706
Pro
babi
lity
EL Sizes Difference-4000 -2000 0 2000 40000
5
10
15
20
25
X: -18.72Y: 9.967
Tank Size Difference
Pro
babili
ty
WT FC
EL HT
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Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
Results – Optimal Sizes Correlation
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Frankfurt (Germany), 6-9 June 2011
Haghi – Iran – RIF Session 4 – Paper 0664
0 2000 4000 6000 8000 10000 120000
2
4x 10
7
Opt
imal
Cos
t
0
200
400
WT
Siz
e
0
5000
10000
EL
Siz
e
0
5000
HT
Siz
e
0 2000 4000 6000 8000 10000 120000
500
1000
FC
Siz
e
Simulated size sets for all 12,000 samples
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Frankfurt (Germany), 6-9 June 2011
General Outline
Hybrid Active System with PEVs - Modeling
The Stochastic-Heuristic Algorithm
Results
Conclusion
Haghi – Iran – RIF Session 4 – Paper 0664
Main Topics
20
Frankfurt (Germany), 6-9 June 2011
A PSO-embedded stochastic simulation
Realistic modeling of the wind power and load demand data
A set of optimal sizes are obtained as final outputs which is then analyzed to provide a measure for making the optimal decision
Haghi – Iran – RIF Session 4 – Paper 0664
Conclusions
21
Frankfurt (Germany), 6-9 June 2011
A worthwhile optimal selection would be the mean values of all scenarios at the cost of reducing the reliability, but to an acceptable level most of the time
Sensitivity analysis of optimal sets
Other relationships could also be implied to help decision-maker
Haghi – Iran – RIF Session 4 – Paper 0664
Conclusions
22
Frankfurt (Germany), 6-9 June 2011
Contact:Hamed VALIZADEH HAGHIPhDc, P.EngFaculty of Electrical and Computer EngineeringK. N. Toosi University of Technology, Tehran 16315-1355, Iran+98 (21) 2793 [email protected]
Thank You!
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