1 agip kco introduction to exploration activities 1 agip kco piping and long distance pipelines 1...
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1Agip KCO Introduction to exploration activities 1Agip KCO Piping and long distance pipelines 1Agip KCO Introduction to exploration activities 1Agip KCO Piping and long distance pipelines
Chaire Sciences des Systèmes et Défis
Energétiques
Fondation Europeenne pour les Energies de Demain Electricite de France (EDF)
2Agip KCO Introduction to exploration activities 2Agip KCO Piping and long distance pipelines 2Agip KCO Introduction to exploration activities 2Agip KCO Piping and long distance pipelines
Systems Energy
Research subject
3Agip KCO Introduction to exploration activities 3Agip KCO Piping and long distance pipelines 3Agip KCO Introduction to exploration activities 3Agip KCO Piping and long distance pipelines
Methodological
Exploratory
Systemic
Research spirit
4Agip KCO Introduction to exploration activities 4Agip KCO Piping and long distance pipelines 4Agip KCO Introduction to exploration activities 4Agip KCO Piping and long distance pipelines
Complex (Energy) Systems
Risk (failures, aging, reliability-availability-maintainability-safety-security (RAMSS), vulnerability, resilience)
Uncertainty
Research topics
5Agip KCO Introduction to exploration activities 5Agip KCO Piping and long distance pipelines 5Agip KCO Introduction to exploration activities 5Agip KCO Piping and long distance pipelines
Modeling
Simulation
Optimization
Research areas
COMPUTATIONAL METHODS
6Agip KCO Introduction to exploration activities 6Agip KCO Piping and long distance pipelines 6Agip KCO Introduction to exploration activities 6Agip KCO Piping and long distance pipelines
Research linesAging and failure processes in components of energy production
plants
Predictions and prognostics Ronay AK, Jie LIU, Valeria VITELLI
Component degradation, maintenance modeling and simulation Yan-Hui LIN, Yan-Fu LI
Energy network systems
Agent-based modeling Elizaveta KUZNETSOVA, Carlos Ruiz MORA, Yan-Fu LI
Complexity Science Yi-Ping FANG, Tairan WANG
System-of-Systems approach to external events risk assessment Elisa FERRARIO, Chung-Kung LO
Optimization under uncertainty Rodrigo MENA, Carlos Ruiz MORA, Yan-Fu LI
7Agip KCO Introduction to exploration activities 7Agip KCO Piping and long distance pipelines 7Agip KCO Introduction to exploration activities 7Agip KCO Piping and long distance pipelines
Research Thematic Posters
(The CHAIRE)
8Agip KCO Introduction to exploration activities 8Agip KCO Piping and long distance pipelines 8Agip KCO Introduction to exploration activities 8Agip KCO Piping and long distance pipelines
Research linesAging and failure processes in components of energy production
plants
Predictions and prognostics Ronay AK, Jie LIU, Valeria VITELLI
Component degradation, maintenance modeling and simulation Yan-Hui LIN, Yan-Fu LI
Energy network systems
Agent-based modeling Elizaveta KUZNETSOVA, Carlos Ruiz MORA, Yan-Fu LI
Complexity Science Yi-Ping FANG, Tairan WANG
System-of-Systems approach to external events risk assessment Elisa FERRARIO, Chung-Kung LO
Optimization under uncertainty Rodrigo MENA, Carlos Ruiz MORA, Yan-Fu LI
9Agip KCO Introduction to exploration activities 9Agip KCO Piping and long distance pipelines 9Agip KCO Introduction to exploration activities 9Agip KCO Piping and long distance pipelines
Predictions and prognostics
Research Thematic Posters (Research line 1)
10Agip KCO Introduction to exploration activities 10Agip KCO Piping and long distance pipelines 10Agip KCO Introduction to exploration activities 10Agip KCO Piping and long distance pipelines
Component degradation, maintenance modeling and simulation
Research Thematic Posters (Research line 1)
11Agip KCO Introduction to exploration activities 11Agip KCO Piping and long distance pipelines 11Agip KCO Introduction to exploration activities 11Agip KCO Piping and long distance pipelines
Research linesAging and failure processes in components of energy production
plants
Predictions and prognostics Ronay AK, Jie LIU, Valeria VITELLI
Component degradation, maintenance modeling and simulation Yan-Hui LIN, Yan-Fu LI
Energy network systems
Agent-based modeling Elizaveta KUZNETSOVA, Carlos Ruiz MORA, Yan-Fu LI
Complexity Science Yi-Ping FANG, Tairan WANG
System-of-Systems approach to external events risk assessment Elisa FERRARIO, Chung-Kung LO
Optimization under uncertainty Rodrigo MENA, Carlos Ruiz MORA, Yan-Fu LI
12Agip KCO Introduction to exploration activities 12Agip KCO Piping and long distance pipelines 12Agip KCO Introduction to exploration activities 12Agip KCO Piping and long distance pipelines
Research Thematic Posters (Research line 2)
Agent-based modeling
13Agip KCO Introduction to exploration activities 13Agip KCO Piping and long distance pipelines 13Agip KCO Introduction to exploration activities 13Agip KCO Piping and long distance pipelines
Research Thematic Posters (Research line 2)
Complexity Science
14Agip KCO Introduction to exploration activities 14Agip KCO Piping and long distance pipelines 14Agip KCO Introduction to exploration activities 14Agip KCO Piping and long distance pipelines
Research Thematic Posters (Research line 2)
System-of-Systems approach to external events risk assessment
15Agip KCO Introduction to exploration activities 15Agip KCO Piping and long distance pipelines 15Agip KCO Introduction to exploration activities 15Agip KCO Piping and long distance pipelines
Research Thematic Posters (Research line 2)
Optimization under uncertainty
16Agip KCO Introduction to exploration activities 16Agip KCO Piping and long distance pipelines 16Agip KCO Introduction to exploration activities 16Agip KCO Piping and long distance pipelines
Research lines
Aging and failure processes in components of energy production plants
Simulation-based stochastic Petri Nets model for component degradation with time-dependent transitions
Numerical solutions of inhomogeneous continuous time Markov chain (ICTMC) for degradation process modeling
Works:
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Case study
Two types of cracks form in dissimilar metal welds, i.e. radial and circumferential, and can grow into a guillotine style
Case study: Alloy 82/182 dissimilar metal weld in a PWR primary coolant system
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Multistate physics model (MSPM)
Stephen D. Unwin, Peter P. Lowry, Robert F. Layton, Jr., Patrick G. Heasler, and Mychailo B. Toloczko. 2011. MULTI-STATE PHYSICS MODELS OF AGING PASSIVE COMPONENTS IN PROBABILISTIC RISK ASSESSMENT. ANS PSA 2011 International Topical Meeting on Probabilistic Safety Assessment and Analysis Wilmington, NC, March 13-17, 2011.
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Key quantities
Holding time probability distribution with influencing factors
Transition probability
The conditional probability density function (PDF) that the process will depart state i at time t, given that the process is at state i at time t’ and the values of the external influencing factors θ
20Agip KCO Introduction to exploration activities 20Agip KCO Piping and long distance pipelines 20Agip KCO Introduction to exploration activities 20Agip KCO Piping and long distance pipelines
Inclusion of uncertain external factors
C: Circumferential crackD: Radial CrackL: Leak StateM: Micro CrackR: Ruptured stateS: Initial state
θ1: Parameter 1θn: Parameter n
P(θ1)
S
L
D
R
C
M
2*10-2/yr 2*10-2/yr
1*10-3/yr
8*10-1/yr
1*10-5/yr
2*10-2/yr
Time dependent transition rates
constant
constant
constant
P(θn)
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Inclusion of uncertain external factors
Initial transition rate:
Scale parameter:
is the explicit stress (MPa), T is the absolute temperature (oK).
Truncated normal distributions:
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Influence of uncertain external factors
Uncertain temperature T Uncertain stress
Mean curves with 95% confidence intervals
Both factors have significant impacts on the mean probability value of initial state, P(s). At t=80, with uncertain temperature P(s) = 0.012 is 236% smaller than P(s) without uncertain factors; with uncertain stress P(s) = 0.011 is 196% smaller than P(s) without uncertain factors.
Both factors have significant impacts on the variances of all state probabilities. Mean variance caused by uncertain temperature is 107.81% higher than without uncertain factors.Mean variance caused by uncertain stress is 119.81% higher than without uncertain factors
23Agip KCO Introduction to exploration activities 23Agip KCO Piping and long distance pipelines 23Agip KCO Introduction to exploration activities 23Agip KCO Piping and long distance pipelines
Research lines
Energy network systems
A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function
Uncertainty Propagation in the Adequacy Assessment Model of a Distributed Generation System
Environmental Power Unit Commitment Optimization
Works:
24Agip KCO Introduction to exploration activities 24Agip KCO Piping and long distance pipelines 24Agip KCO Introduction to exploration activities 24Agip KCO Piping and long distance pipelines
Research lines
Energy network systems
A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function
Uncertainty Propagation in the Adequacy Assessment Model of a Distributed Generation System
Environmental Power Unit Commitment Optimization
Works:
25Agip KCO Introduction to exploration activities 25Agip KCO Piping and long distance pipelines 25Agip KCO Introduction to exploration activities 25Agip KCO Piping and long distance pipelines
Distributed generation reliability
Distributed Generation Reliability = Pr(PG >PL)
Loads
Wind turbines
Solar generators
Electric Vehicles
Transformers
Distribution network
Electrical power flow
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Multi-state model of wind turbine
0
1
nWS-1
Wind speed states Mechanical States
0
1
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Multi-state model of distributed generation system
Transformer
1SG 1 SG 2 SG 3 SG 4 SG 5
2WG 1
WG 2
WG 5
WG 4
WG 3
EV aggregation5 solar irradiation states, 2 mechanical states (working and
failed, Markov model of mechanical state transition)
5 wind speed states, 2 mechanical states (working and
failed, Markov model of mechanical state transition)
3 operating states: charging, disconnected, and discharging.
2 mechanical states: working and failed, Markov
model of multi-state transition
10 load states, by clustering
IEEE 34 nodes distribution test feeder modified
Generation UGF: Consumption UGF:
Reliability indices:
28Agip KCO Introduction to exploration activities 28Agip KCO Piping and long distance pipelines 28Agip KCO Introduction to exploration activities 28Agip KCO Piping and long distance pipelines
Research lines
Energy network systems
A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function
Uncertainty Propagation in the Adequacy Assessment Model of a Distributed Generation System
Environmental Power Unit Commitment Optimization
Works:
29Agip KCO Introduction to exploration activities 29Agip KCO Piping and long distance pipelines 29Agip KCO Introduction to exploration activities 29Agip KCO Piping and long distance pipelines
Different uncertainties in the distributed generation system
Aleatory uncertainties
Aleatory uncertainties
Epistemic uncertainties
Distribution Network Operator
Loads
Wind turbines
Solar generators
Electric Vehicles
Transformers
Distribution network
Electrical power flow
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Uncertainty representation and propagation
Aleatory uncertainties Epistemic uncertainties
x21 3 40
α
1.5 3.5
0.5
1
Possibility
Possibility distributionProbability distribution
Evidence Theory
Able to hybridize two different uncertainties
31Agip KCO Introduction to exploration activities 31Agip KCO Piping and long distance pipelines 31Agip KCO Introduction to exploration activities 31Agip KCO Piping and long distance pipelines
Component Parameter Source of uncertainty Type of Information available
Uncertainty representation
Solar generatorSolar irradiation Irradiation variability Historical data Probabilistic
(e.g. Beta)
Operation parameters Incomplete knowledge Experts’ judgments, users’ experiences
Possibilistic
Wind turbine Wind speed Speed variability Historical data Probabilistic
(e.g. Weibull)
Operation parameters Incomplete knowledge Experts’ judgments, users’ experiences
Possibilistic
EV aggregation Power output Incomplete knowledge, subjective decisions
Experts’ judgments, users’ experiences
Possibilistic
TransformerGrid power Power
fluctuationsHistorical data Probabilistic
Time to failure Mechanical degradation/failure date
Historical data Probabilistic
Loads Load value Consumption variability
Historical data Probabilistic
Different types of uncertainties in the distribution generation system
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Results: Probabilistic-possibilistic v.s. pure probabilistic
Penetration levels
Hybrid Pure probabilisticPlausibility Belief
15% 0.0537 0.0408 0.0480
25% 0.0482 0.0329 0.0410
35% 0.0472 0.0315 0.0350
33Agip KCO Introduction to exploration activities 33Agip KCO Piping and long distance pipelines 33Agip KCO Introduction to exploration activities 33Agip KCO Piping and long distance pipelines
Research lines
Energy network systems
A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function
Uncertainty Propagation in the Adequacy Assessment Model of a Distributed Generation System
Environmental Power Unit Commitment Optimization
Works:
34Agip KCO Introduction to exploration activities 34Agip KCO Piping and long distance pipelines 34Agip KCO Introduction to exploration activities 34Agip KCO Piping and long distance pipelines
Costs :
Emission:
Power balance:
System spinning reserve requirements:
Objective functions
Environmental power unit commitment problem (EUCP) formulation
Constraints
Unit minimum up/down times:
Unit generation limits:
EUCP is non-linear, and mixed combinatorial and continuous multi-objective optimization problem
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Priority list
Solution: evolutionary (memetic) algorithm
Multi-objective genetic algorithm (MOGA) (NSGA-II )+ Two local search strategies: 1) deep local search (DLS), 2) wide local search (WLS)
NSGA-II initial population generation: priority list
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Multi-Objective Memetic Algorithm (MOMA)
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50 100 150 200 250 300 350 400 450 500
5.64
5.66
5.68
5.7
5.72
5.74
x 105
Iteration
Opera
tion C
ost
($)
NSGA-II
NSGA-II+WLSNSGA-II+DLS
50 100 150 200 250 300 350 400 450 5003.3
3.35
3.4
3.45
3.5
3.55
3.6x 10
4
Iteration
Em
issio
n (
lb)
NSGA-II
NSGA-II+WLSNSGA-II+DLS
Convergence and comparisons
Methods 10-unit 100-unitELR 563977 5605678GA 565825 5627437SA 565828 5617876
UCC-GA 563977 5626514QEA-UC 563938 5609550
ICA 563938 5617913NSGA-II+DLS 563938 5605918
No of Units
Cost ($) NSGA-II NSGA-II + DLS
NSGA-II + WLS
10 Best 565898 563938 564096Average 567212 564240 564690Worst 569923 564723 566224
100 Best 5625616 5605918 5618657Average 5627541 5618970 5623096Worst 5630154 5623813 5625982
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Pareto-fronts of different multi-objective optimization algorithms
5.65 5.7 5.75 5.8 5.85 5.9 5.95
x 105
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6x 10
4
Operation Cost ($)
Em
issi
on (
lb)
NSGA-II
NSGA-II+WLSNSGA-II+DLS
minimum emission
minimum cost
39Agip KCO Introduction to exploration activities 39Agip KCO Piping and long distance pipelines 39Agip KCO Introduction to exploration activities 39Agip KCO Piping and long distance pipelines
Research team
40Agip KCO Introduction to exploration activities 40Agip KCO Piping and long distance pipelines 40Agip KCO Introduction to exploration activities 40Agip KCO Piping and long distance pipelines
MerciCпасибо
GrazieGracias
TeşekkürlerThanks谢谢