2015 11 03 soutenance these v4 -...
TRANSCRIPT
Méthodes pour la planification pluriannuelle des réseaux de distribution. Application à l’analyse technico-économique des solutions d’intégration
des énergies renouvelables intermittentes.
Héloïse DUTRIEUX
PhD defense
03/11/15
• Doctoral advisors• Bruno François (L2EP EC Lille)
• Gauthier Delille (EDF R&D)
• Julien Bect (L2S CentraleSupélec)
• Project ANR APOTEOSE (Analyse Probabiliste et nouveaux Optimums Technico-EcOnomiques des SystèmesElectriques en présence de taux de pénétration élevés d’énergies intermittentes)
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1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
3�� � ��� ��
1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
4���� ���� � � ������� �
HTB2 HTB3
HTB1
HTA (MV)
BT(LV)
HV/MV substation
MV/LV substation
Transmission network
Distribution network
Scope of the work
(HV)
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Historically, electricity was mainly produced by central plants connected to the transmission network.
Consumption
Generation
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Consumption
Generation
Since the 2000s, more and more Renewable Energy Sources (RES) have been connected to the distribution networks.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Consumption
Generation
RES
RES
RES
RESRES
In the near future, a massive integration of RES is expected in the distribution networks.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Risk of voltage/current constraints
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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+ !!!
Network investments to connect 25 GW by 2030: 300 k€/MW of photovoltaic generation100 k€/MW of wind generation
Network reinforcement
Risk of voltage/current constraints
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
10
• Advanced reactive power control of generators• Generation curtailment• Real-time voltage control in the substations• Energy storage• …
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Alternatives to reinforcement
Pg ↓
Qg ~
P, Q~
Uref ~
?Network reinforcement
Risk of voltage/current constraints
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
11
• Real-time voltage control in the HV/MV substations• Advanced reactive power control of generators
• Generation curtailment• Energy storage• …
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Alternatives to reinforcement
Pg ↓
Qg ~
P, Q~
Uref ~
?Network reinforcement
Risk of voltage/current constraints
Which are the best solutions to reduce costs of integrating RES in the medium/long termat an acceptable level of risk and quality?
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
12
Current planning Requirements with RES
RES-integrationsolutions
Operatinglimits?
No Yes, in energy and/or duration
Operating costs?
Power lossesEnergy not supplied
+ Non-negligible costs depending on the constraints
Network planning methods
Studied scenarios
« Worst case »Multi-year profiles of
generation/consumption
Constraint indicators
Boolean (yes or no)
Statistical(frequency, severity, duration)
ObjectivePrevent 100 % of the networks constraints
Reach a tradeoff between costs and quality of supply
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1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
13
Current planning Requirements with RES
RES-integrationsolutions
Operatinglimits?
No Yes, in energy and/or duration
Operating costs?
Power lossesEnergy not supplied
+ Non-negligible costs depending on the constraints
Network planning methods
Studied scenarios
« Worst case »Multi-year profiles of
generation/consumption
Constraint indicators
Boolean (yes or no)
Statistical(frequency, severity, duration)
ObjectivePrevent 100 % of the networks constraints
Reach a tradeoff between costs and quality of supply
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1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
14
Current planning Requirements with RES
RES-integrationsolutions
Operatinglimits?
No Yes, in energy and/or duration
Operating costs?
Power lossesEnergy not supplied
+ Non-negligible costs depending on the constraints
Network planning methods
Studied scenarios
« Worst case »Multi-year profiles of
generation/consumption
Constraint indicators
Boolean (yes or no)
Statistical(frequency, severity, duration)
ObjectivePrevent 100 % of the networks constraints
Reach a tradeoff between costs and quality of supply
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1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Need to modify the current planning methods to assess the techno-economic impacts of the RES-integration solutions
Extensive literature review on the planning approaches taking into account RES-integration solutions
Main limits of the existing planning approaches:
• Inappropriate time sampling: too small time period and/or time step
• Future events sometimes assumed to be perfectly known
• Interactions between MV and LV networks generally neglected
• Economic analysis sometimes incomplete or missing
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
16���� ���� � � ������� �
Need to modify the current planning methods to assess the techno-economic impacts of the RES-integration solutions
Extensive literature review on the planning approaches taking into account RES-integration solutions
Main limits of the existing planning approaches:
• Inappropriate time sampling: too small time period and/or time step
• Future events sometimes assumed to be perfectly known
• Interactions between MV and LV networks generally neglected
• Economic analysis sometimes incomplete or missing
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
The limits of the existing planning approaches can distort the assessment of the techno-economic
impacts of the RES-integration solutions.
17
• Methods for medium-term distribution network planning (10 years)
• Network constraints: slow voltage/current variations (10 minutes)
• Quality of supply: voltage limits and weakly-supplied LV consumers
• Network and communication facilities assumed reliable
• Provide a suitable framework for the study of RES-integration solutions in the medium/long run
• Address the main limits of the existing planning approaches:‒ Distribution System Operator (DSO)’s behavior‒ Uncertainty on the arrivals of new RES‒ Interactions between MV and LV networks
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OBJECTIVES
SCOPE
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
18
1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
19
Question
How can we address the main limits of the existing approaches?
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Initial state Final state
?
Interactions between
MV and LV networks
DSO’s behavior
Uncertainty on the arrivals of
new RES
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
20�� � ������� �� � ��
Initial state Final state
?
Interactions between
MV and LV networks
Define multi-variable planning strategies
including RES-integration solutions
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
Question
Proposed approach
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
21�� � ������� �� � ��
Proposed tool
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
Simulate DSO’s analyses / actions+ Compute the network state
Define the DSO’s decision tree / strategy
Compute the total costs of the strategy over the given scenario
Consider stochastic arrivals of RES + load and generation uncertainties
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Scenario builder
Generate multi-year scenarios:
• Features of new RES producers
• 10-minute profiles of generation, consumption and voltage reference
∆Uref
Pc
Pw
Ppv
RE
S p
ower
to b
e ac
com
mod
ated
1 2 3 4 5 6 7 8 9 100
48
12
Scé
nario
1P
[M
W/a
]
0
102030
Pto
t [M
W]
1 2 3 4 5 6 7 8 9 100
48
12
Scé
nario
2P
[M
W/a
]
0
102030
Pto
t [M
W]
1 2 3 4 5 6 7 8 9 10048
12
Scé
nario
3P
[M
W/a
]
0102030
Pto
t [M
W]
1 2 3 4 5 6 7 8 9 100
48
12
Scé
nario
4P
[M
W/a
]
0
102030
Pto
t [M
W]
1 2 3 4 5 6 7 8 9 100
48
12
Scé
nario
5P
[M
W/a
]
Année
0
102030
Pto
t [M
W]
PBT
PHTA
Ptot
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/12-0.02
0
0.02
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
11.2
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Planning strategy
Model the Distribution System Operator (DSO)’s behavior.
Planning strategy the sequence of the decision analyses made by the DSO in everyday life,
as well as the associated decisions, when the DSO notices an eventthat may require some adaptations of the existing network.
Examples of event connection of a new user, too many weakly-supplied consumers,
constraint limit violations, expected load growth in the coming years, etc.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
25�� � ������� �� � ��
Planning strategy
Model the Distribution System Operator (DSO)’s behavior. • Each planning strategy is a sequence of rules.
• Each rule describes the RES-integration solutions to remove constraints.
Example of the current French strategy:If the connection of a new MV producer to a feeder supplying loads may cause overvoltages, then it is necessary to:
R1: decrease the fixed tan(φ) reference of the new producer, with tan(φ) ≥ tan(φ)min.
R2: decrease the fixed tan(φ) reference of the existing MV producers, with tan(φ) ≥ tan(φ)min,DSO.
R3: decrease the fixed voltage reference of the on-load tap changer of the HV/MV transformer UOLTC, with UOLTC ≥ UOLTC,min.
R4: reinforce the MV network.
minmin,min ,)tan(,)tan( OLTCDSO U
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
26�� � ������� �� � ��
Planning strategy
Model the Distribution System Operator (DSO)’s behavior. • Each planning strategy is a sequence of rules.
• Each rule describes the RES-integration solutions to remove constraints.
Example of a new strategy including generation curtailment:If the connection of a new MV producer to a feeder supplying loads may cause overvoltages, then it is necessary to:
R1: decrease the fixed tan(φ) reference of the new producer, with tan(φ) ≥ tan(φ)min.
R2: decrease the fixed tan(φ) reference of the existing MV producers, with tan(φ) ≥ tan(φ)min,DSO.
R3: decrease the fixed voltage reference of the on-load tap changer of the HV/MV transformer UOLTC, with UOLTC ≥ UOLTC,min.
R5: limit the active power of the MV producers considering a maximal curtailment rate τcurt ≤ τcurt,max.
R4: reinforce the MV network. max,minmin,min ,,)tan(,)tan( curtOLTCDSO U
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
27�� � ������� �� � ��
Network + DSO simulator
Simulate the network evolutions on a year-by-year basis:
1. Apply the planning strategy at the beginning of the year to remove limit violations and accommodate all LV and MV producers.
2. Estimate the network state over the whole year on the basis of 10-minute generation/consumption profiles.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
28�� � ������� �� � ��
Network + DSO simulator
Simulate the network evolutions on a year-by-year basis:
1. Apply the planning strategy at the beginning of the year to remove limit violations and accommodate all LV and MV producers.
2. Estimate the network state over the whole year on the basis of 10-minute generation/consumption profiles. a. MV network state: voltages, currents, losses and total apparent power
b. LV network state: extreme voltages and number of weakly-supplied LV consumers
Approximation method
n (fast) approximateload-flows
)( mnX )(~
lnY
n* (slow) exact load-flows
Sampling method
)*(* mnX )*(* lnY
n* << n
PART 3
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Economic analysis
Compute the investments and operating costs over the studied period.
Stake-holders
Investment and operating costs
DSO
Upgraded and new MV lines Upgraded HV/MV transformersUpgraded MV/LV transformersMV line losses
MV producers
Upgraded and new MV linesUpgraded HV/MV transformersPower Conversion System (PCS) oversizing PCS maintenance
LV producers
Only if connected to dedicated feeders:Upgraded MV/LV transformers New LV dedicated feeders(New MV lines)
T
kT
kk
kk
k
i
V
i
C
i
INPC
111 111
Gross Present Cost
Net Present Cost
T
kk
kk
k
i
C
i
IGPC
111 11
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
31
S
Example of application: the current French planning strategy
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Year 10
Load: +2 %/y
Gen.: 20 MW
Year 0
Load: 11 MVA
Generation: 0
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
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Example of application: the current French planning strategy
Studied scenario
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/12-0.02
0
0.02
Ure
f [pu
]
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
11.2
Pc [
pu]
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
Pw [
pu]
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
Pp
v [pu
]
Features of the new producers10-minute profiles of generation,
consumption and voltage reference
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
33
S
Upgraded lineUpgraded transformer
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Year 0
1 2 3 4 5 6 7 8 9 100
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
New lineNew MV producer
Example of application: the current French planning strategy
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
34
S
�� � ������� �� � ��
Year 1
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
35
S
�� � ������� �� � ��
Year 2
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
36
S
�� � ������� �� � ��
Year 3
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
37
S
�� � ������� �� � ��
Year 4
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
38
S
�� � ������� �� � ��
Year 5
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
39
S
�� � ������� �� � ��
Year 6
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
40
S
�� � ������� �� � ��
Year 7
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
41
S
�� � ������� �� � ��
Year 8
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
42
S
�� � ������� �� � ��
Year 9
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
43
S
�� � ������� �� � ��
Year 10
Upgraded lineUpgraded transformer
New lineNew MV producer
Example of application: the current French planning strategy1 2 3 4 5 6 7 8 9 10
0
4
8
12
Scé
nario
1P
[M
W/a
]
0
10
20
30
Pto
t [M
W]
12 30
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
44
Example of application: the current French planning strategy
Technical results
1 2 3 4 5 6 7 8 9 10048
12
P [
MW
/a]
PLV
PMV
1 2 3 4 5 6 7 8 9 100
250
500
750
1000
1250
1500
1750
Year
Cos
ts [
k€/a
]
IMVDSO
IMVMP
ILVDSO
ILVLP
ClossDSO
IPCSMP
CPCSMP
�� � ������� �� � ��
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Total
Length of upgraded/new
MV lines [km]0 1,2 0 0 1,9 1,2 4,3 15 3,3 2 28,9
Number of upgraded/new
MV/LV transformers9 0 2 2 1 9 4 0 0 1 28
Power losses [MWh] 308 331 353 352 340 352 293 596 528 549 4001
Level of weakly-supplied
LV consumers [%]0,73 0,41 1,05 0,63 0,63 1,81 0,03 0,10 0,19 1,01 –
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
45
1 2 3 4 5 6 7 8 9 10048
12
P [
MW
/a]
PLV
PMV
1 2 3 4 5 6 7 8 9 100
250
500
750
1000
1250
1500
1750
Year
Cos
ts [
k€/a
]
IMVDSO
IMVMP
ILVDSO
ILVLP
ClossDSO
IPCSMP
CPCSMP
�� � ������� �� � ��
MV network investments
Power losses
Power conversion supply of MV
producers
LV network investments
Example of application: the current French planning strategy
Economic results
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
46
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
�� � ������� �� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
New opportunities to study RES-integration solutions
Compare different strategies
Multi-year scenario builder
Take into account the scenario uncertainty
0 0.5 1 1.5 2 2.50
0.5
1
1.5
2
Net Present Cost NPC [pu]
Em
piric
al p
roba
bilm
ity d
ensi
ty
MeanMedian10 % quantile90 % quantile
Optimize a strategy
Multi-variable planning strategy
Multi-year scenario builder
Multi-year scenario builder
Multi-variable planning strategy
Multi-variable planning strategy
Statistical results
47�� � ������� �� � ��
In a nutshell
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
Define multi-variable planning strategies
including RES-integration solutions
Use stochastic scenarios of RES
arrivals
Use a LV network model
Uncertainty on the arrivals of
new RESInteractions
between MV and LV networks
DSO’s behavior
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
48
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
Interactions between
MV and LV networks
�� � ������� �� � ��
Define multi-variable planning strategies
including RES-integration solutions
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
In a nutshell
?Computation
time
49
1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
50������ ��� ���� � � ��� � �
Problem of the computation time in network planning
Which time step size for studying RES-integration solutions?
The smallest as possible because:
• network constraints are defined over 10 minutes
• RES power can vary in a few seconds/minutes
Time step size considered here: ΔT = 10 minutes
Load-flow process f
)( mnX )( lnY
m = number of input variablesl = number of output variablesn = number of time steps = number of load-flows
n = 52560 load-flows per year
tcomput ≈ 3 minutesper year
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
51������ ��� ���� � � ��� � �
Problem of the computation time in network planning
tcomput ≈ 3 minutesper year
Study a strategy over a 10-year scenario ≈ 30 minutes
Study a strategy over 200 10-year scenarios ≈ 4 days
Optimize a multi-variable strategy with 50 candidate points ≈ 6 months
Compare 10 optimized strategies ≈ 5 years
Load-flow process f
)( mnX )( lnY
m = number of input variablesl = number of output variablesn = number of time steps = number of load-flows
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
52������ ��� ���� � � ��� � �
Problem of the computation time in network planning
tcomput ≈ 3 minutesper year
Study a strategy over a 10-year scenario ≈ 30 minutes
Study a strategy over 200 10-year scenarios ≈ 4 days
Optimize a multi-variable strategy with 50 candidate points ≈ 6 months
Compare 10 optimized strategies ≈ 5 years
Load-flow process f
)( mnX )( lnY
m = number of input variablesl = number of output variablesn = number of time steps = number of load-flows
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Performing exact load-flows is not suitable.It is necessary to reduce computation time while
providing accurate results.
53������ ��� ���� � � ��� � �
Options to reduce computation time in network planning
Option 1: increase the time step size.
• Commonly used to study RES-integration solutions with ΔT = 30 min or 1 hour.
• Easy to be implemented.
• High loss of accuracy compared with the time saving.
n exactload-flows
)( mnX )( lnY
n* exactload-flows
)*( mnX )*( lnY
n* factor of n
Subsampling
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
54������ ��� ���� � � ��� � �
Options to reduce computation time in network planning
Option 2: simplify the load-flow equations using hypotheses and/or intrusive approximation techniques.
• Often used to study network stability and the statistical impacts of input variables.
• Efficiency depending on:– the hypotheses,
– the intrusive approximation techniques.
n exactload-flows
)( mnX )( lnY
n simplifiedload-flows
)( mnX )(~
lnY n* exactload-flows
)*( mnX )*( lnY
n* factor of n
Intrusive approximationSubsampling
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
55
Options to reduce computation time in network planning
Option 3: build a surrogate model of the load-flow process using non-intrusive approximation techniques.
• Often used in application domains when the observed phenomenon is not explicit,but rarely used in network studies.
• Efficiency depending on:‒ the sampling method used to select the points where the exact model has be evaluated,
‒ the approximation method used to build the surrogate model based on the evaluation points.
������ ��� ���� � � ��� � �
Approximation method
n exactload-flows
)( mnX )( lnY
n approximateload-flows
)( mnX )(~
lnY
n* exact load-flows
Sampling method
)*(* mnX )*(* lnY
n* << n
n simplifiedload-flows
)( mnX )(~
lnY n* exactload-flows
)*( mnX )*( lnY
n* factor of n
Non-intrusive approximation
Intrusive approximationSubsampling
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
56
Subsampling Intrusive approximation
Options to reduce computation time in network planning
Option 3: build a surrogate model of the load-flow process using non-intrusive approximation techniques.
• Often used in application domains when the observed phenomenon is not explicit,but rarely used in network studies.
• Efficiency depending on:‒ the sampling method used to select the points where the exact model has be evaluated,
‒ the approximation method used to build the surrogate model based on the evaluation points.
������ ��� ���� � � ��� � �
Approximation method
n exactload-flows
)( mnX )( lnY
n approximateload-flows
)( mnX )(~
lnY
n* exact load-flows
Sampling method
)*(* mnX )*(* lnY
n* << n
n simplifiedload-flows
)( mnX )(~
lnY n* exactload-flows
)*( mnX )*( lnY
n* factor of n
Non-intrusive approximation
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
57
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
Select a samplingmethod and an approximation
method
STEP 1
) (~
lnY ) ( mnX
?
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
58
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) (~
lnY ) ( mnX
Build an experimental
design using the sampling method
STEP 2
Select a samplingmethod and an approximation
method
STEP 1
?
with n* << n
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
59
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) (~
lnY ) ( mnX
Build an experimental
design using the sampling method
STEP 2
Select a samplingmethod and an approximation
method
STEP 1
?
)*(* lnY STEP 3 Perform n* exact
calculations
f
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
60
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) (~
lnY ) ( mnX
Build an experimental
design using the sampling method
STEP 2
Select a samplingmethod and an approximation
method
STEP 1
?
)*(* lnY STEP 3 Perform n* exact
calculations
f
Estimate the parameters of the surrogate model usingthe approximation method
STEP 4
*f
) *(* mnX ) *(* lnY
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
61
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) (~
lnY ) ( mnX
Build an experimental
design using the sampling method
STEP 2
Select a samplingmethod and an approximation
method
STEP 1
)*(* lnY STEP 3 Perform n* exact
calculations
f
Estimate the parameters of the surrogate model usingthe approximation method
) *(* mnX ) *(* lnY
STEP 4
) ( mnX Perform napproximatecalculations
STEP 5
*f
*f
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
62
General procedure to estimate a scalar variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX )*(* lnY
) ( mnX ) (~
lnY ) ( mnX
STEP 3
Estimate the parameters of the surrogate model usingthe approximation method
Perform napproximatecalculations
STEP 4
STEP 5
Build an experimental
design using the sampling method
STEP 2 Perform n* exact calculations
*f
Select a samplingmethod and an approximation
method
STEP 1
f
*f
) *(* mnX ) *(* lnY
Problem: in our work, y is 2(b+1)-
dimensional where bis the number of
buses in the network.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
63
General procedure to estimate a vector variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) *(* qnZ
) ( mnX ) (~
lnY ) ( mnX
STEP 3
Order
Compute the parameters
W
q
Do PCA
Perform n approximatecalculations
STEP 5
STEP 6
Build an experimental
design using the sampling method
STEP 2 Perform n* exact calculations
Select a samplingmethod and an approximation
method
STEP 1Find the principal
componentsWYYZ ).*(* 1
f
2
*
2
* %9,99 YZ
) *(* mnX
STEP 4
)*(* lnY
Estimate the parameters of the surrogate model usingthe approximation method
Perform napproximatecalculations
*f
*f
Principal Component Analysis (PCA) to reduce the
number of variables to be estimated:
q << l
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
64
General procedure to estimate a vector variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX
) *(* qnZ
) ( mnX ) (~
lnY ) ( mnX
STEP 3
Order
Compute the parameters
W
q
Do PCA
Do reverse PCA
STEP 5
STEP 6
Build an experimental
design using the sampling method
STEP 2 Perform n* exact calculations
*...,*,*,...,1 qk fff
STEP 7
Select a samplingmethod and an approximation
method
STEP 1Find the principal
componentsWYYZ ).*(* 1
f
2
*
2
* %9,99 YZ
TWZYY .~
.~ 1
) *(* mnX
Perform n approximatecalculations
*...,*,*,...,1 qk fff
Reverse PCA to compute the final
variable y based on its first principal
components z.
STEP 4
)*(* lnY
Principal Component Analysis (PCA) to reduce the
number of variables to be estimated:
q << l
Estimate the parameters of the surrogate model usingthe approximation method
) (~
qnZ
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
65
General procedure to estimate a vector variable y = f(x)
������ ��� ���� � � ��� � �
) *(* mnX )*(* lnY
) *(* qnZ
) ( mnX ) (~
lnY ) ( mnX
STEP 3
Order
Compute the parameters
W
q
Do PCA
Do reverse PCA
STEP 4
STEP 5
STEP 6
Build an experimental
design using the sampling method
STEP 2 Perform n* exact calculations
*...,*,*,...,1 qk fff
STEP 7
Select a samplingmethod and an approximation
method
STEP 1Find the principal
componentsWYYZ ).*(* 1
f
2
*
2
* %9,99 YZ
TWZYY .~
.~ 1
) *(* mnX
Perform n × q approximatecalculations
*...,*,*,...,1 qk fff
Estimate the parameters of the q surrogate models using
the approximation method
) (~
qnZ
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
66
General procedure to estimate a vector variable y = f(x)
������ ��� ���� � � ��� � �
Approximation method
(Fast) approximateload-flow process)( mnX )(
~lnY
(Slow) exact load-flow process
Samplingmethod
)*(* mnX )*(* lnY
n* << n
• Nearest-Neighbor Interpolation (NNI)• Polynomial Regression (PR)• Kriging (K)
• Pruned Factorial Designs (PFD)• Mean factorial-based designs (MFD)• Latin Hypercube Samples (LHS)
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
67
The most efficient techniques
������ ��� ���� � � ��� � �
Results of the comparison of the approximation techniques
Nearest-Neighbor Interpolation + Mean factorial-based designPolynomial Regression + Latin Hypercube SampleKriging + Latin Hypercube Sample
Case 1Case 2Case 3
Approximation error
Computation time
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
68������ ��� ���� � � ��� � �
Results of the comparison of the approximation techniques
Nearest-Neighbor Interpolation + Mean factorial-based designPolynomial Regression + Latin Hypercube SampleKriging + Latin Hypercube Sample
Case 1Case 2Case 3
10-3
10-2
10-1
10010
-2
100
102
Tr
err Q
95(U
) [V
]
Voltage
10-3
10-2
10-1
10010
-2
100
102
Tr
err Q
95(I
) [A
]
Current
10-3
10-2
10-1
100
10-2
100
102
104
Tr
err Q
95(S
0) [k
VA
]
Total apparent power
10-3
10-2
10-1
100
10-2
100
102
Tr
err Q
95(P
loss
) [k
W]
Power losses
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
69������ ��� ���� � � ��� � �
Results of the comparison of the approximation techniques
Nearest-Neighbor Interpolation + Mean factorial-based designPolynomial Regression + Latin Hypercube SampleKriging + Latin Hypercube Sample
Case 1Case 2Case 3
10-3
10-2
10-1
10010
-2
100
102
Tr
err Q
95(U
) [V
]
Voltage
10-3
10-2
10-1
10010
-2
100
102
Tr
err Q
95(I
) [A
]
Current
10-3
10-2
10-1
100
10-2
100
102
104
Tr
err Q
95(S
0) [k
VA
]
Total apparent power
10-3
10-2
10-1
100
10-2
100
102
Tr
err Q
95(P
loss
) [k
W]
Power losses
In this case study, very satisfactory results are obtained by Polynomial Regression and Kriging when combined with Latin Hypercube Samples.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
70
Final procedure to estimate the network state over one year
������ ��� ���� � � ��� � �
Surrogate load-flow process
)( mnX )(~
lnY
+ validation on a 200-point sample
= 400 exact load-flows
+ validation on the same sample
= 200 exact load-flows = 52 560 exact load-flows
1st trial 2nd trial Exact calculations
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
71������ ��� ���� � � ��� � �
General performances of the proposed procedure
Proposed procedure To be compared with:
Error
Voltage < 150 V Un = 20 kV
Current < 5 A 185 A < In < 615 A
Power losses < 1 % Eloss ≈ 200-600 MWh
Total apparent power < 200 kVA 20 MVA < Sn < 72 MVA
Time saving 8 to 35! 3 or 6 with time subsampling
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
72
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
Interactions between
MV and LV networks
������ ��� ���� � � ��� � �
Define multi-variable planning strategies
including RES-integration solutions
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
In a nutshell
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Use approximation methods to obtain fast and accurate load-flows
Computation time
73
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
Interactions between
MV and LV networks
Computation time
������ ��� ���� � � ��� � �
Define multi-variable planning strategies
including RES-integration solutions
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
In a nutshell
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Use approximation methods to get fast and
accurate load-flows
For a given computation time, about 20 times more scenarios can be studied
with a very limited loss of accuracy.
74
1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
75
Considered studies
1. Impact of the minimal tangent phi of the MV producers
2. Impact of the “Last In First Out” generation curtailment
3. Optimization of the current French planning strategy
�������� ���
Year 10
Load: +2 %/y
Gen.: 20-22 MW
Simulations over200 scenarios
Use of an optimization
algorithm
Year 0
Load: 11 MVA
Generation: 0
S
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
76
Study 1: impact of the minimal tangent phi of the MV producers
Variables: θ = tan(φ)min = tan(φ)min,DSO
Possible impacts
Less network reinforcement
More MV producers connected to feeders supplying loads
More or less power losses
Bigger power conversion chain for all the MV producers
�������� ���
Fixed strategy
θ
R1: decrease the fixed tan(φ) reference of the new producer, with tan(φ) ≥ tan(φ)min.
R2: decrease the fixed tan(φ) reference of the existing MV producers, with tan(φ) ≥ tan(φ)min,DSO.
Current planning strategy
tan(φ)min Which impact on the Net Present Cost?
T
kT
kk
kk
k
i
V
i
C
i
INPC
111 111
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
77
-0.6 -0.5 -0.4 -0.3 -0.25 -0.2 -0.1 00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
tan(phi)minR
[pu
, ba
se N
PC
m(-
0.25
)]
RRm
Rq25
Rq75
Rm*
Rq25*
Rq75*
Study 1: impact of the minimal tangent phi of the MV producers
�������� ���
-0.6 -0.5 -0.4 -0.3 -0.25 -0.2 -0.1 0
0.5
1
1.5
2
2.5
tan(phi)min
NP
C [
pu,
base
NP
Cm
(-0.
25)]
NPCNPCm
NPCq25
NPCq75
NPCm*
),(min),(),( SNPCSNPCSR Θ
High dispersion of the scenario costscompared with the average cost: impossible
to say which value of tan(φ)min is optimal.
The values of tan(φ)min between –0.2 and 0 have a similar average impact on the costs.
),( SNPC
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
78
-0.6 -0.5 -0.4 -0.3 -0.25 -0.2 -0.1 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
tan(phi)min
NP
Cm
[pu
]
ICDSO
ICMP
ILP
Study 1: impact of the minimal tangent phi of the MV producers
�������� ���
Average allocation of the costs
-0.6 -0.5 -0.4 -0.3 -0.25 -0.2 -0.1 00
0.2
0.4
0.6
0.8
1
1.2
1.4
tan(phi)min
NP
Cm
[pu
]
IMVDSO
IMVMP
ILVDSO
ILVLP
ClossDSO
IPCSMP
CPCSMP
-0.6 -0.5 -0.4 -0.3 -0.25 -0.2 -0.1 0 0
0.1
0.2
0.3
0.4
0.5
tan(phi)min
NP
Cm
[pu
]
IMV
ILV
Closs
ICPCS
Average cost per category Average cost per stakeholder
Current value
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
79
0 2 4 6 8 100
0.1
0.2
0.3
0.4
0.5
curt,max
[%]
NP
Cm
[pu
]
ICDSO
ICMP
IPB
0 2 4 6 8 100
0.1
0.2
0.3
0.4
0.5
curt,max
[%]
NP
Cm
[pu
]
IMV
ILV
Closs
ICPCS
Ccurt
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
curt,max
[%]
NP
Cm
[pu
]
IMVDSO
IMVMP
ILVDSO
ILVLP
ClossDSO
IPCSMP
CPCSMP
CcurtMP
CcurtDSO
Study 2: impact of the “Last In First Out” generation curtailment
�������� ���
Average allocation of the costs
Average cost per category Average cost per stakeholder
Current value
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
80
0 2 4 6 8 100
0.1
0.2
0.3
0.4
0.5
curt,max
[%]
NP
Cm
[pu
]
ICDSO
ICMP
IPB
0 2 4 6 8 100
0.1
0.2
0.3
0.4
0.5
curt,max
[%]
NP
Cm
[pu
]
IMV
ILV
Closs
ICPCS
Ccurt
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
curt,max
[%]
NP
Cm
[pu
]
IMVDSO
IMVMP
ILVDSO
ILVLP
ClossDSO
IPCSMP
CPCSMP
CcurtMP
CcurtDSO
Study 2: impact of the “Last In First Out” generation curtailment
�������� ���
Average allocation of the costs
Average cost per category Average cost per stakeholder
Current value
The proposed planning approach makes it possible to assess the techno-economic impacts of the
RES-integration solutions.
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
81�������� ���
Study 3: optimization of the current planning strategy
Why should we use an optimization process?
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.5
1
1.5
2
2.5
tan(phi)min
NP
C [pu
, ba
se N
PC
m(-
0.25
)]
NPCNPC
m
NPCm*
N = 200 scenarios per candidate point
tcomput ≈ 4 hours per candidate point
Accuracy on the average cost: δ = 2 %
Considering 10 candidate values for each decision variable:
• Optimize a 1-variable strategy ≈ 40 hours
• Optimize a 2-variable strategy ≈ 16 days
• Optimize a 3-variable strategy ≈ 5 months
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
82�������� ���
Study 3: optimization of the current planning strategy
General problem
Studied problem
Objective f• Economic: Net Present Cost, Regret...
• Quality: Number of weakly-supplied consumers...
• Scenario uncertainty: Mean, Quantile…
Constraints g• Finite space of the decision variables,
• Quality on the network…
Decision variables θ• Number of variables
• Continuous or discrete
)(min f
0)(s.t. g
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
83�������� ���
Study 3: optimization of the current planning strategy
General problem
Studied problem
Objective f• Economic: Net Present Cost, Regret...
• Quality: Number of weakly-supplied consumers...
• Scenario uncertainty: Mean, Quantile…
Constraints g• Finite space of the decision variables,
• Quality on the network…
Decision variables θ• Number of variables: 1
• Continuous or discrete
)(min f
0)(s.t. g
0;6.0)tan( min
),()( SNPCEf S S
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
84�������� ���
Study 3: optimization of the current planning strategy
Studied problem
Problem particularities?
• No explicit formulation of f
• Noisy evaluation results
• Expensive-to-evaluate model
Optimization algorithm used:
Informational Approach to Global Optimization (IAGO)
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
),()( SNPCEf S S
min)tan( )(min fΘ
0;6.0Θ
85
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*70
= 0.96 pu
*70
= -0.40
f ( )
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*70
= 0.96 pu
*70
= -0.40
f ( )
Study 3: optimization of the current planning strategy
After 70 evaluations:
�������� ���
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n ob
s
10 evaluations per point
86
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n obs
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*200
= 0.98 pu
*200
= -0.42
f ( )
�������� ���
Study 3: optimization of the current planning strategy
After 200 evaluations:
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
87
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n ob
s
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*500
= 0.93 pu
*500
= -0.28
f ( )
�������� ���
Study 3: optimization of the current planning strategy
After 500 evaluations:
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
88
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n obs
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*500
= 0.93 pu
*500
= -0.28
f ( )
�������� ���
Study 3: optimization of the current planning strategy
After 1 000 evaluations:
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*1000
= 0.93 pu
*1000
= -0.27
f ( )
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
89
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n ob
s
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*1000
= 0.93 pu
*1000
= -0.27
f ( )
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*5000
= 0.97 pu
*5000
= -0.25
f ( )
�������� ���
Study 3: optimization of the current planning strategy
After 5 000 evaluations:
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
83 scenarios per candidate point without optimization
90
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00
200
400
600
800
1000
1200
n obs
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*1000
= 0.93 pu
*1000
= -0.27
f ( )
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*5000
= 0.97 pu
*5000
= -0.25
f ( )
�������� ���
Study 3: optimization of the current planning strategy
After 5 000 evaluations:
Mean of the evaluationsMean of the kriging model95% CI of the kriging model
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Estimation of the objective function Allocation of the evaluations
83 scenarios per candidate point without optimization
Algorithms such as IAGO are suitable for the optimization of expensive-to-evaluate functions
in presence of noisy evaluations.
91
1. Scope and motivation
2. Novel framework for the study of RES-integration solutions in multi-year distribution planning
3. Approximation methods for computing the multi-year electrical network state
4. Case studies
5. Conclusion and further work
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
92
Need to modify the distribution planning methods to assess the techno-economic impacts
of RES-integration solutions.
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
93
Points often neglected in the existing planning approaches
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Interactions between
MV and LV networks
DSO’s behavior
Uncertainty on the arrivals of
new RES
94
Proposed planning approach
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Interactions between
MV and LV networks
Define multi-variable planning
strategies
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
95
Proposed planning approach
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Interactions between
MV and LV networks
Define multi-variable planning
strategies
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
Computation time
96
Proposed planning approach
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Interactions between
MV and LV networks
Define multi-variable planning
strategies
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
Computation time
Use approximation methods to obtainfast and accurate
load-flows
97
Proposed planning approach
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Interactions between
MV and LV networks
Define multi-variable planning
strategies
Use stochastic scenarios of RES
arrivals
Use a LV network model
DSO’s behavior
Uncertainty on the arrivals of
new RES
Computation time
Use approximation methods to obtainfast and accurate
load-flows
98
Proposed simulation tool
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
99
Possible case studies
-0.6 -0.5 -0.4 -0.3-0.25 -0.2 -0.1 0
0.5
1
1.5
2
2.5
tan(phi)min
NP
C [
pu,
base
NP
Cm
(-0.
25)]
NPCNPCm
NPCq25
NPCq75
NPCm*
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
Network reinforcement = Reinforcement + alternatives = ?
TODAY TOMORROW
S
1 2 3 4 5 6 7 8 9 10048
12
P [
MW
/a]
PLV
PMV
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 00.6
0.8
1
1.2
1.4
f*5000 = 0.97 pu
*5000 = -0.25
f ( )
Compare different strategies
Study parameter impacts Optimize a strategyStudy one RES scenario
0 10
0.2
0.4
0.6
0.8
1
NP
Cm
[pu
]
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
100
Adapt the proposed methods for an industrial application by the DSOs
Further work
Improve the procedure used to create profiles of consumption/generation
��� � ��� ��� � �� �� ��� ��� � ��
1. Scope 2. Framework 3. Approximation 4. Case studies 5. Conclusion
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
11.2
Pc [
pu]
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
Pw [
pu]
01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/09 01/10 01/11 01/12 31/120
0.20.40.60.8
1
Pp
v [pu
]
Take into account novel RES-integration solutions
Tackle complex optimization problems
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
?
?
?
)(min f
0)(s.t. g
?
? ?
Network + DSO simulator
DSO’s decision analysis
MV and LV network state
Initial network
Variables
Scenario
Costs
Scenario parameters
Fixed planning strategy
ConstraintsActions
Technical outputs
Multi-variable planning strategy
Economic analysis
Multi-year scenario builder
101�� �������� � �
• H. Dutrieux, G. Delille et B. François, “An innovative method to assess solutions for integrating renewable generation into distribution networks over multi-year horizons”, Proc. 23rd International Conference on Electricity Distribution (CIRED), article 1103, juin2015.
• H. Dutrieux, I. Aleksovska, J. Bect, E. Vazquez, G. Delille et B. François, “The Informational Approach to Global Optimization in presence of very noisy evaluation results. Application to the optimization of renewable energy integration strategies”, Proc. 47èmes Journées de Statistique de la SFdS (JDS), article 199, juin 2015.
• H. Dutrieux, G. Delille, B. François et G. Malarange, “Assessing the Impacts of Distribution Grid Planning Rules on the Integration of Renewable Energy Sources”, Proc. IEEE PowerTech, article 464270, juillet 2015.
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