Intelligent Energy Management System Simulator for PHEVs at a by lsz36768

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									NC STATE UNIVERSITY




            Intelligent Energy Management System
          Simulator for PHEVs at a Municipal Parking
              Deck in a Smart Grid Environment

               Preetika Kulshrestha, Student Member, IEEE, Lei Wang, Student Member, IEEE,
                       Mo-Yuen Chow, Fellow, IEEE and Srdjan Lukic, Member, IEEE
                                        North Carolina State University
                                                 Raleigh, NC
NC STATE UNIVERSITY
                                                              Outline


          Introduction
          System architecture
          Component description
          System simulator
          Sample system simulation
          Future work




       Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment   2
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                                 Emergence of the “Smart Grid”

       Optimization of power delivery - Capacity to deliver efficiently,
       reliably and intelligently
       Features
         Decentralization of control
         Services customized to user’s needs
         Use of energy efficient systems
         Rapid reconfiguration




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                                          Introduction to PHEVs

        HEV with larger battery pack                                                                                            Load leveling during
                                                                                                                                extreme load events


        Can be charged from standard wall outlet




                                                                                       MW Load
        40 mile all-electric range (Chevy Volt)                                                     Off peak
                                                                                                    charging


       Benefits                                                                                  Source: Prometheus Institute
                                                                                                                                  Time of Day

        Reduction in GHG emissions        • Load leveling
        Reduction on oil dependence       • Lower cost




                                            Potential Synergistic
                                                 Relation


                                    A cluster of vehicles is
                                a controllable load for the grid
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                                        Opportunities and Challenges

       Opportunities
       • US fleet’s 176 million light vehicles = power capacity of 19.5TW= 24 x power
          capacity of the electric generation system.
       • PHEV penetration by 2050 – 62% of the US fleet (EPRI prediction)

       Challenges
       • Potential load of 1000 cars => 4 MW load
       • Potential dangers => Voltage instability and blackouts
       • Infrastructure
       • Need of an underlying framework to enable PHEV integration

       A Solution

                   Intelligent Energy Management at a Municipal Parking Deck




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                                                     Related Work

             J. Tomic and W. Kempton, “Using fleets of electric-drive vehicles for grid
             support”, J. Power Sources, vol. 168, issue 2, 2007
              M. Duvall and E. Knipping, “Environmental assessment of Plug-in Hybrid
             Electric vehicles”, EPRI, July 2007
             Hutson, G. K. Venayagamoorthy, K. A. Corzine, “Intelligent Scheduling of
             Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit
             Maximization in Grid Power Transactions”, in proc. IEEE Energy2030,
             Atlanta, GA, 2008
             S. B. Pollack et al, patent title “User interface and user control in a power
             aggregation system for distributed electric resources”, IPC8 Class:
             AG01R2106FI, USPC Class: 702 62
             GridPoint: http://www.gridpoint.com/




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                                                 System Architecture




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                                                      Information Flow

                                                      User Profile Entry
      Utility

                    Pricing
                    Available power


                                                                               •    Power Allocated
      iEMS
     Optimization                                                              •    Rate of charge
                                                       Data Acquisition                                      Vehicle 1
                                                                               •    Other control
                                                       (DSP, FPGA etc.)             messages

                                                                                                             Vehicle 2


                                                                                                             Vehicle 3
                                                                                                             .
 •     Time of availability
 •     Type of charge
                                                                                                             .
 •     Pricing preferences                                                                                   .
 •     Current state of charge                           Communication Medium:                               .
                                                         Wi-Fi, Bluetooth, Satellite
 •     Power consumed                                    ZigBee etc.                                         Vehicle n
 •     …..




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                                    System - Component Description




 States
    Idle: There is no activity, the load may be waiting for a
    control action or it may have completed charging the
    battery.
    Data Acquisition: The data is acquired from the battery
    and the user.
    Communication: Data is communicated to controller.
    Charging: Battery charging is in progress.
    Error: There is an error in the system and system
    operation is halted until error is resolved.                                                             •A - Idle
                                                                                                             •B - Data Acquisition
                                                                                                             •C - Communication
                                                                                                             •D - Charging
                                                                                           State transitions •E - Stop


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                                        System - Component Description
                                      Initialize




                                      T=Tsample or
                                   request received
                                  for update or new
                                  load has plugged
                                         in?



     Data from Loads                     Yes
         Time of Availability                            Information
         User Preferences                                from Utility
         Initial State of              Sample                Total Power               A
         Charge                 instantaneous power          Available
                                                             Pricing
                                  and SOC of load
                                                                                                       •A - Communicate
                                                                                Plug- in/ Update
       Data Acquisition                                                            Request,            •B- Optimize
                                 Optimize on power
                                subject to constraints                             Tsample



                                   Allocate Power                                      B
                                       to loads

                                iEMS flow chart                                 State transitions
        States
        • Communication: Inform the loads of power allocated/listen for signals
        • Optimize: Calculation of power allocation when:
              • There is a change in utility power.
              • A load has plugged-in/out.
              • Periodically, after sampling the instantaneous power consumed by loads.
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                                    System - Component Description




       Functions
         Periodically inform the iEMS about the power available and pricing information




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                                                                                                                         iEMS - System Simulator
                                                                                                   Notify Plug in

                                                                                           Vehicle Information

           Discrete,                              time         Input Rate and Allocated Power        Completed
           Ts = 5 s.          Clock
           powergui                          Simulation Time                                                   VA

                                                                                                         UpdateI

                                                                                  Station I
                                                                                                    Notify Plug in

                                                                                              Vehicle Information

                                                               Input Rate and Allocated Power          Completed

                                                                                                                 VB

                                                                                                          UpdateII

                                                                                  Station II


                        Input from car I            Car I                                            Notify Plug in

                        Input from car II           Car II                                     Vehicle Information
                        Input from car III
                                                   Car III     Input Rate and Allocated Power           Completed
                        Input from car IV
 Utility                                           Car IV                                                           VC
                        Input from car V
   Power                Power                       Car V                                                 UpdateIII

                                                                                   Station III
                                       iEMS


                                                                                                     Notify Plug in

                                                                                               Vehicle Information




                                                                                                                                       Hybrid System
                                                               Input Rate and Allocated Power           Completed

                                                                                                                    VD

                                                                                                          UpdateIV

                                                                                  Station IV

                                                                                                     Notify Plug in

                                                                                               Vehicle Information

                                                               Input Rate and Allocated Power           Completed

                                                                                                                    VE

                                                                                                           UpdateV

                                                                                   Station V




                                                                                                                            Discrete




                                                                                                                                                  Continuous Dynamics


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                                                 Sample System Simulation

                             D                                        Tplug out = 10:40 am                  Tplug out = 11:36 am
  A
                         SoCin = 40%
  SoCin = 70%

  B          C                   SoCin = 50%
8:00 am            9:00 am                     10:00 am                         11:00 am                 12:00 am                                                1:00 pm
                                 E                                                  Tplug out = 11:22 am                                              Tplug out = 1:04 pm
          SoCin = 0%                                   Tplug out = 10:17 am

SoCin = 10%

                                                                                                                        Plot of power consumption of five cars
                                                                                                12000


 Objective function:                 max J ( k ) = ∑∑ w i (k )SoCi ( k + j )                    10000
                                                                                                                                                            PowerA
                                       p
                                                   j     i                                                                                                  PowerB
                                                                                                 8000                                                       PowerC
                                                                                                                                                            PowerD
 wi(k): the priority assigned for to vehicle i at time step k                                                                                               PowerE




                                                                                    Power (W)
                                                                                                 6000
                                                                                                                                                            Total Power
 Priorities are assigned based on capacity required                                              4000
                                                                                                                                                            Available Power


 and time remaining                                                                              2000


                                                                                                   0


                                                                                                -2000
                                                                                                        0         0.5          1         1.5          2          2.5          3
                                                                                                                                      Time (s)                                4
                                                                                                                                                                       x 10




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                                                  Monte Carlo Simulation

                                                                                                            State of Charge at Plug out - Optimal Allocation for SoC Maximization
 Simulation Parameters                                                                             0.35


 State of Charge at plug-in: Uniform random                                                         0.3
 number between 10% and 75%
                                                                                                   0.25




                                                                      Percentage of Vehicles (%)
 Time of Availability: Uniform random number
 between 0.5 and 2 hours                                                                            0.2


 Time of Plug-in: Uniform random number                                                            0.15
 between 0 and 2 hours
                                                                                                    0.1
 Simulation Run Time: 4 hours
                                                                                                   0.05
 Battery Capacity: Uniformly distributed between
 6 Ah and 15 Ah                                                                                       0
                                                                                                     -0.2          0         0.2       0.4       0.6         0.8        1           1.2
 Number of times the simulation was run for each                                                                                      SOC at Plug out

 algorithm: 100
                                           Optimal Allocation   Dynamic Priority                                                         Equal Priority
                                           for SoC Maximization Allocation                                                               Allocation
    Percentage of vehicles                 81.8%                                                   69.6%                                 67.6%
    leaving with SoC 55% or                (409)                                                   (348)                                 (339)
    higher
    Number of vehicles leaving             2                                                       32                                    40
    with SoC 35% or lower                  0.4%                                                    6.4%                                  8.0%


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                                                         Conclusions

          This paper proposes an iEMS for managing power at a parking deck
          System components, functions and behavior are outlined
          A simulator (test-bed) is developed to simulate the real world scenario
          The simulator will contribute towards evaluation of varied scenarios and
          iEMS algorithms
          Optimization on a chosen objective is formulated and simulation results
          presented




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                                                        Future Work

    • Exploration of different objectives for optimization
    • Extension of the problem to multi-objective optimization and incorporation
      of additional constraints
    • Network in the Loop iEMS – performance evaluation with communication
      delay, packet drop, and signal strength
    • Decision on optimal sampling time
    • Extension of the concept to distributed control
    • Real world implementation and demonstration of the iEMS




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                                                   Acknowledgement




                      This work was partially supported by the National Science
                       Foundation (NSF) under Award Number EEC-08212121




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                                               THANK YOU!




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