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Real-Time Optimization and Simulation for Integrated Power Systems
 

Real-Time Optimization and Simulation for Integrated Power Systems

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    Real-Time Optimization and Simulation for Integrated Power Systems Real-Time Optimization and Simulation for Integrated Power Systems Presentation Transcript

    • Real-time Optimization and Simulation for Integrated Power Systems Jing Sun Naval Arch. and Marine Eng. University of Michigan
    • Presentation outline  Introduction: RACE‐Lab at University of  Michigan  Integrated Power Systems   IPS Power Management, Real‐time  Optimization and Simulation  A Case Study: IPS for All‐Electric Ships  Conclusions
    • Real-time Adaptive Control Engineering Lab Simulation and Validation Model Development Control design Optimization Data Hardware info Requirements Constraints Transient profiles Hardware/configuration recommendations Subsystem specifications Sensitivity analysis results Control strategy Control-oriented “grey-box” model 3
    • Our Applications Real‐time Simulation  Power management of  Integrated Power Systems  Engine and powertrain control  Combined fuel cell and gas  turbine cycle systems and  CHP  Vessel control and dynamic  positioning 4
    • Active Projects  Energy Management and Configuration  Optimization for All‐Electric Ships  (ONR/NEEC)  Integrated Fuel Cell and Fuel Reforming  Systems Dynamic Analysis and Control  Design (TARDEC/ARC, NSF)  Vehicle Electrification (DoE/CERC)  Control and Optimization of Advanced  Automotive Powertrains  (Ford, Toyota) 5
    • Lab Facilities • An 8‐node Opal‐RT real‐time simulator • A DC hybrid power system • Combined cycle SOFC/GT hardware  simulation bench (in progress) • A fully instrumented model ship 6
    • Presentation outline  Introduction: RACE‐Lab at University of  Michigan  Integrated Power Systems   IPS Power Management, Real‐time  Optimization and Simulation  A Case Study: IPS for All‐Electric Ships  Conclusions
    • Integrated Power Systems  Power systems that combine multiple  power sources/loads through synergetic  integration  Examples of integrated power systems – Hybrid vehicles – All‐electric ships – SOFC/GT system 8 www.defenseindustrydaily.com www.techjournal.org www.americanhistory.si.edu
    • Characteristics of IPS  Multiple and heterogeneous power/heat plants involved  High efficiency and (intended for) self‐sustaining  Close thermal, chemical, mechanical and electrical  couplings  More complex and challenging tasks for control,  optimization and integration – High efficiency  system often operates on or close to the  boundary of admissible state and input sets – Mobile requirements  require fast load following capability  and sufficient power reserve and safety margin 9
    • Presentation outline  Introduction: RACE‐Lab at University of  Michigan  Integrated Power Systems   IPS Power Management, Real‐time  Optimization and Simulation  A Case Study: IPS for All‐Electric Ships  Conclusions
    • Power Management for IPS  Coordinate multiple, heterogeneous power plants  (including energy storage devices)  Manage transient operations   Assure safe operation in case of component and  subsystem failure  Facilitate effective system reconfiguration  Achieve optimal performance in terms of power quality  and system operation efficiency 11
    • Optimization in Power Management of IPS  Optimization: A natural formalism for power  management of IPS  – Assure optimal performance during normal operation – Guarantee effective reconfiguration in case of failures – Enforce hard and soft constraints  Challenges: – Computationally intensive (nonlinear dynamics, long  time horizon, mixed form of models) – Real‐time performance requirements 12
    • Our Approach  Algorithm development – Integrated perturbation analysis and sequential  quadratic programming (IPA‐SQP) to speed up  optimization – Sensitivity function approach to explore multiple  time‐scales in IPS – Incremental reference governor to reduce problem  complexity  Algorithm evaluation and validation  – RT‐Lab for algorithm  development and evaluation 13
    • Presentation outline  Introduction: RACE‐Lab at University of  Michigan  Integrated Power Systems   IPS Power Management, Real‐time  Optimization and Simulation  A Case Study: IPS for All‐Electric Ships  Conclusions
    • Case Study: IPS for All-Electric Ships Main Subsystems: 1. PGM: Power Generation Module • PGM1:Gas turbine • PGM2:Fuel cell 2. EPM: Electric Propulsion Module 3. ESM: Energy Storage Module 4. PCM: Power Conversion Module • PCM1: DC/DC • PCM2: DC/AC • PCM3: DC/DC • PCM4: AC/DC & DC/DC • PCM5: DC/AC • PCM6: DC/AC Main features of IPS: 1. Redundant power sources 2. Reconfigurable power flow path 15
    • Case Study: IPS for All-Electric Ships System representation of a shipboard        integrated power  system DC Hybrid Power System (DHPS) 1. Multiple power sources 2. Multiple power converters 3. Energy storage bank 16 16
    • Test-bed Setup • Power converter 1. Unidirectional DC/DC (1, 2) 2. Bidirectional DC/DC (3) • RT‐LAB with 4 targets • Power supply (1, 2) • Electronics load (1, 2) • Energy storage bank 17
    • Explore Time-scale Separation Main Subsystems: PGM: Power Generation Module PGM1:Gas turbine PGM2:Fuel cell (~seconds-minutes) EPM: Electric Propulsion Module (~ms - seconds) ZEDS: Power Conversion Module (s – ms) Vital loads Non-vital loads DC STBD bus DC Port Bus Zone1 PCM1 NV load PCM3 Vital load PCM2 NV load PCM6 Vital load PCM5 MEPM MEPM Zone2 PCM1 PCM1 NV load PCM3 Vital load PCM2 NV load PCM6 Vital load PCM5 PGM2PCM-4 PCM1 PCM-4 AC 4160V/60HZ DC 600V PORT 1100VDC STBD 900VDC PORT 900VDC PGM: power generation module PCM: power conversion module EPM: electric propulsion module PGM1 STBD 1100VDC 18
    • Use RT-Lab for Algorithm Development  Explore the trade‐off between  optimality and computational  efficiency  Level 1: static optimization (all  dynamics are considered  infinitely fast)  Level 2:  ignore fast dynamics  Level 3: consider both slow and  fast time dynamics – Calculate the corrections to the  Level 2 solution CostJ Time required to solve for u L1 L2 L3 Opt Opt Performance loss due to non real-time 19
    • Simulation and Validation From_PM From_Gen From_FC From_Loads To_Con To_Loads To_FC To_Gen To_PM SS_ZEDS From_PM From_Gen To_Con To_PM To_Gen SS_Propulsion From_PM From_ZEDS To_Con To_ZEDS To_PM SS_Loads From_G/T From_ZEDS From_Prop To_Con To_PM To_ZEDS To_Prop To_GT SS_Generator From_PM From_Gen To_Con To_Gen SS_G/T From_PM From_ZEDS To_PM To_ZEDS To_Con SS_Fuelcell From_Con From_Gen From_FC From_Prop From_Loads From_ZEDS To_Con To_Loads To_ZEDS To_GT To_FC To_Prop SM_PowerManagement From_PM From_ZEDS From_Loads From_Prop From_FC From_G/T From_Gen To_PM SC_Console DC STBD bus DC Port Bus Zone1 PCM1 NV load PCM3 Vital load PCM2 NV load PCM6 Vital load PCM5 MEPM MEPM Zone2 PCM1 PCM1 NV load PCM3 Vital load PCM2 NV load PCM6 Vital load PCM5 PGM2PCM-4 PCM1 PCM-4 AC 4160V/60HZ DC 600V PORT 1100VDC STBD 900VDC PORT 900VDC PGM: power generation module PCM: power conversion module EPM: electric propulsion module PGM1 STBD 1100VDC 20
    • Simulation and Validation  With the time‐scale separation – Better tracking – The timeliness of the optimal  solution proved to be critical Power demand Solution with Time scale separation Solution without Time scale separation 21
    • Presentation outline  Introduction: RACE‐Lab at University of  Michigan  Integrated Power Systems   IPS Power Management, Real‐time  Optimization and Simulation  A Case Study: IPS for All‐Electric Ships  Conclusions
    • Conclusions  Real‐time performance is an essential  requirement IPS system performance  Computational Efficiency is critical for  optimization‐based power management for IPS  Case study illustrates the utility of efficient  algorithm and computational tools in developing  effective IPS with desired real‐time performance  RACELab has been using RT‐Lab in algorithm  development and methodology research 23