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Systems

Chaire Sciences des Systèmes et Défis Energétiques Fondation Europeenne pour les Energies de Demain Electricite de France ( EDF). Research subject. Energy. Systems. Research spirit. Methodological Exploratory Systemic. Research topics. Complex ( Energy) Systems

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Systems

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  1. ChaireSciences des Systèmes et DéfisEnergétiquesFondationEuropeennepour les Energies de DemainElectricite de France (EDF)

  2. Research subject Energy Systems

  3. Research spirit • Methodological • Exploratory • Systemic

  4. Research topics • Complex (Energy) Systems • Risk(failures, aging, reliability-availability-maintainability-safety-security (RAMSS), vulnerability, resilience) • Uncertainty

  5. Research areas • Modeling • Simulation • Optimization COMPUTATIONAL METHODS

  6. Research lines • Aging and failure processes in components of energy production plants • Predictions and prognosticsRonay AK, Jie LIU, Valeria VITELLI • Component degradation,maintenance modeling and simulationYan-Hui LIN, Yan-Fu LI • Energy network systems • Agent-based modelingElizaveta KUZNETSOVA, Carlos Ruiz MORA,Yan-Fu LI • Complexity ScienceYi-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

  7. Research ThematicPosters (The CHAIRE)

  8. Research lines • Aging and failure processes in components of energy production plants • Predictions and prognosticsRonay AK, Jie LIU, Valeria VITELLI • Component degradation,maintenance modeling and simulationYan-Hui LIN, Yan-Fu LI • Energy network systems • Agent-based modelingElizaveta KUZNETSOVA, Carlos Ruiz MORA,Yan-Fu LI • Complexity ScienceYi-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

  9. Research ThematicPosters (Research line 1) Predictions and prognostics

  10. Research ThematicPosters (Research line 1) Component degradation, maintenance modeling and simulation

  11. Research lines • Aging and failure processes in components of energy production plants • Predictions and prognosticsRonay AK, Jie LIU, Valeria VITELLI • Component degradation,maintenance modeling and simulationYan-Hui LIN, Yan-Fu LI • Energy network systems • Agent-based modelingElizaveta KUZNETSOVA, Carlos Ruiz MORA,Yan-Fu LI • Complexity ScienceYi-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

  12. Research ThematicPosters (Research line 2) Agent-based modeling

  13. Research ThematicPosters (Research line 2) Complexity Science

  14. Research ThematicPosters (Research line 2) System-of-Systems approach to external events risk assessment

  15. Research ThematicPosters (Research line 2) Optimization under uncertainty

  16. Research lines • Aging and failure processes in components of energy production plants Works: • 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

  17. Case study Case study: Alloy 82/182 dissimilar metal weld in a PWR primary coolant system Two types of cracks form in dissimilar metal welds, i.e. radial and circumferential, and can grow into a guillotine style

  18. 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.

  19. Key quantities Holding time probability distribution with influencing factors • 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 Transition probability

  20. Inclusion of uncertainexternalfactors S constant 1*10-3/yr 2*10-2/yr 2*10-2/yr 8*10-1/yr M constant D C Time dependent transition rates C: Circumferential crack D: Radial Crack L: Leak State M: Micro Crack R: Ruptured state S: Initial state θ1: Parameter 1 θn: Parameter n 1*10-5/yr P(θ1) P(θn) constant 2*10-2/yr L R

  21. Inclusion of uncertainexternalfactors Initial transition rate: Scale parameter: is the explicit stress (MPa), T is the absolute temperature (oK). Truncated normal distributions:

  22. Influence of uncertain external factors Meancurveswith 95% confidence intervals Uncertaintemperature T Uncertain stress 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

  23. Research lines • Energy network systems Works: • 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

  24. Research lines • Energy network systems Works: • 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

  25. Electrical power flow Wind turbines Electric Vehicles Solar generators Distribution network Loads Transformers Distributed generation reliability Distributed Generation Reliability = Pr(PG >PL)

  26. Wind speed states Mechanical States 0 0 1 nWS-1 1 Multi-state model of wind turbine

  27. 5 wind speed states, 2 mechanical states (working and failed, Markov model of mechanical state transition) WG 3 WG 4 2 mechanical states: working and failed, Markov model of multi-state transition 2WG 1 WG 5 WG 2 10 load states, by clustering Transformer SG 3 SG 5 SG 4 SG 2 1SG 1 5 solar irradiation states, 2 mechanical states (working and failed, Markov model of mechanical state transition) EV aggregation 3 operating states: charging, disconnected, and discharging. Multi-state model of distributed generation system Generation UGF: Consumption UGF: Reliability indices: IEEE 34 nodes distribution test feeder modified

  28. Research lines • Energy network systems Works: • 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

  29. Electrical power flow Wind turbines Electric Vehicles Solar generators Distribution network Loads Transformers Different uncertainties in the distributed generation system Distribution Network Operator Aleatoryuncertainties Epistemicuncertainties Aleatoryuncertainties

  30. 1 Possibility α 0.5 3 1.5 4 1 2 0 3.5 x Uncertainty representation and propagation Aleatory uncertainties Epistemic uncertainties Probability distribution Possibility distribution Evidence Theory Able to hybridize two different uncertainties

  31. Different types of uncertainties in the distribution generation system

  32. Results: Probabilistic-possibilisticv.s. pure probabilistic

  33. Research lines • Energy network systems Works: • 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

  34. Environmental power unit commitment problem (EUCP) formulation EUCP is non-linear, and mixed combinatorial and continuous multi-objective optimization problem Objective functions Costs: Emission: Constraints Power balance: System spinning reserve requirements: Unit minimum up/down times: Unit generationlimits:

  35. 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

  36. Multi-Objective Memetic Algorithm (MOMA)

  37. Convergence and comparisons

  38. Pareto-fronts of different multi-objective optimization algorithms

  39. Research team

  40. Merci Cпасибо Grazie Gracias Teşekkürler Thanks 谢谢

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