Outline - SIGMETRICS12-06-13 7 The(Smart(Grid(Is((About(• Upgrading!the!Distribu5on! Network! –...

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12-06-13 1 RealTime Distributed Conges5on Control for Electrical Vehicle Charging Catherine Rosenberg (University of Waterloo) Joint work with Omid Ardakanian and Srinivasan Keshav Outline The electric grid as it is The smart grid ISS4E: Internet research and the smart grid RealTime Distributed Conges5on Control for Electrical Vehicle Charging 2

Transcript of Outline - SIGMETRICS12-06-13 7 The(Smart(Grid(Is((About(• Upgrading!the!Distribu5on! Network! –...

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 Real-­‐Time  Distributed  Conges5on  Control  

for  Electrical  Vehicle  Charging  

Catherine  Rosenberg  (University  of  Waterloo)  

Joint  work  with  Omid  Ardakanian  and  Srinivasan  Keshav  

Outline  

ü The  electric  grid  as  it  is  ü The  smart  grid  ü ISS4E:  Internet  research  and  the  smart  grid  ü Real-­‐Time  Distributed  Conges5on  Control  for  Electrical  Vehicle  Charging  

 

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The  Electric  Grid  Major  components  of  the  electric  grid:    •  Genera5on  

–  Electricity  produc5on  –  Natural  gas,  coal,  hydro,  nuclear,  renewables,  etc    

•  Transmission  –  Network  of  high  voltage  power  lines  –  Analogous  to  fiber  links  in  the  internet  core  

•  Distribu5on  –  Lower  voltage  power  lines  carrying  electricity  from  transmission  lines  to  consumers  

–  Analogous  to  access  networks  in  the  Internet  

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Today’s  Transmission  Grid    §  Transmission  Capacity...  ü  Designed  to  meet  annual  15  mn  peak  –  with  redundant  

capacity..  ü  Planning/Implementa5on  requires  several  years  –  many  

projects  are  commi\ed  for  construc5on  well  before  they  are  needed...  

   The  transmission  system,  for  the  most  part,  is  sophis1cated,  reliable,  

reasonably   secure   ...  BUT   ...It  operates  at  peak  capacity   for   short  1mes  each  year.  

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The  Distribu;on  Network  §  Essen5ally  a  radial  network  –  the  loss  of  a  major  feeder  line  results  

in  customer  outages...  §  Technology  is  mixed  (Some  is  REALLY  OLD!!)  •  Some  equipment  installed  more  than  75  years  ago  remains  in  

opera5on.  •  System  is  generally  designed  for  “one  way”  flow  –  to  the  users...  •  Monitoring  of  customer  service  has  been  limited...    •  The  u5lity  has  limited  means  of  iden5fying  local  overloads  –  or  

thed.  

The  System  Operator  has  Real  Challenges  •  Electricity  is  consumed  the  instant  that  it  is  created  –there  is  

prac5cally  no  storage  on  the  network  for  electricity...  The  u5lity  has  the  task  of  ensuring  that  the  genera5on  meets  the  demand–  on  a  second  by  second  basis..  

•  Large  generators  –  in  par5cular  the  newer  ones,  do  not  change  load  easily  or  quickly...  Yet...  

ü When  someone  turns  on  a  stove,  or  even  a  light...  A  generator  somewhere  is  adjusted  to  meet  the  demand...  

•  Adding  capacity  at  peak  ;mes  is  expensive...  At  off-­‐peak  5mes  it  is  very  cheap.  

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Issues  with  the  Grid  •  Grid  is  over-­‐provisioned  (sized  for  the  peak),  no  

storage  -­‐  always  need  to  match  demand  with  supply.  

   

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“15% of the generating capacity in Massachusetts is needed fewer than 88 hours per year”

Philip Giudice, Commissioner, Massachusetts Department of Energy, Nov. 30, 2009

Issues  with  the  Grid  •  Reliability  

– Outdated  switches,  lack  of  sensors  results  in  poor  visibility  of  the  grid  

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In  Summary  

•  Power  systems  operators  can  control...  ü  Genera5on  ü  Transmission  ü  Distribu5on  

•  Loads,  for  the  most  part  are  uncontrolled...  

ü  Demand  Response  –  Some  control  of  industrial  loads  to  reduce  peak  loads    

ü  Exis5ng  metering  systems  do  not  provide  customers  with  informa5on  needed  to  monitor  and  avoid  peak  periods  

U;li;es  control  the  supply  of  energy  –  but  have  very  limited  control  over  the  demand...  The  system  is  sized  for  the  peak  

Facts  •  If  the  grid  were  just  5%  more  efficient  

–  equivalent  to  permanently  elimina5ng  the  fuel  and  greenhouse  gas  emissions  from  53  million  cars.      

•  If  every  American  household  replaced  just  one  incandescent  bulb  with  CFL  –  would  conserve  enough  energy  to  light  3  million  homes  

 

è  Terrific  opportuni5es  for  improvement.  

http://www.oe.energy.gov/ 10  

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Outline  

•  The  electric  grid  as  it  is  •  The  smart  grid  •  ISS4E:  Internet  research  and  the  smart  grid  •  Real-­‐Time  Distributed  Conges5on  Control  for  Electrical  Vehicle  Charging  

 

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The  Smart  Grid  

Smart  Grid  

Bi-­‐direc5onal  energy  flows  

Renewables  and  DG    -­‐  millions    -­‐  non-­‐tradi5onal    -­‐  intermi\ent  

Consumer  in  the  loop  (incen5viza5on)  

New  (elas5c)  loads:  EVs  +  smart  appliances  

Storage  

Reliable  &  fast  communica5on  +  sensors  

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The  Smart  Grid  Is    About  

•  Upgrading  the  Distribu5on  Network  –  Customers  -­‐  near  real  5me  data  –  U5li5es  

•  Be\er  monitoring  of  loads  and  devices  

•  Be\er  distribu5on  protec5on  –  allow  remote  genera5on  (Distributed  Genera5on  &  Micro  grids)  

•  Reduc5on  of  thed  •  Controlling  demand  

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Objec5ves:  •  Improve  efficiency  •  Reduce  total  GHG  Emissions  •  Increase  u5liza5on  (defer  

capital  expenses)  •  Maintain  or  improve  

reliability  and  security  

 

   

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A  rela5vely  sta5c,  predictable,  stable  system  with  inelas5c  loads  and  a  few  points  of  control  

A  highly  dynamic  system  with  elas5c  loads  and  millions  of  points  of  control  

A paradigm shift

Rolling  out  the  smart  grid  will  require  massive  change  

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Outline  

•  The  electric  grid  as  it  is  •  The  smart  grid  •  ISS4E:  Internet  research  and  the  smart  grid  •  Real-­‐Time  Distributed  Conges5on  Control  for  Electrical  Vehicle  Charging  

 

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Our  Research    

   

Use  Internet  concepts  to  smarten  the  grid  

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Can  Internet  Research  Help?  

•  The  Internet  resembles  the  smart  grid  – Cri5cal  infrastructure    – Large-­‐scale  – Heterogeneous  – Hierarchical  – Matches  geographically  distributed  demands  with  distributed  genera5on  

– Distributed  highly  variable  sources  – Balances    centraliza5on  and  decentraliza5on  – Simple  API  

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Differences  

•  Primarily  one-­‐way  vs.  primarily  two-­‐way  flows  •  Grid  has  prac5cally  no  storage  •  Consumers  are  used  to  see  their  electrical  bill  reflect  what  they  really  use  

•  Policy  makers  are  very  proac5ve    •  Many  loads  at  home  are  determinis5c,  most  loads  are  predictable  

•  Packets  are  “addressed”,  electrons  are  not  

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ISS4E  Vision  

To   apply   our   exper,se   in   Informa1on   Systems   and  Sciences  to  find  innova1ve  solu1ons  to  problems  in  energy  systems.    We  work  within  Waterloo  Ins1tute  for  Sustainable  Energy  (WISE)  in  collabora,on  with  

ü   researchers  in  related  disciplines  ü   partners  in  industry  

 

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l  The  Waterloo  Ins5tute  for  Sustainable  Energy  (WISE)  was  established  at  the  University  of  Waterloo  in  2008.    §  Focal  point  at  UW  for  research  in  energy  studies  

l  More  than  70  faculty  members  with  graduate  students  and  postdoctoral  fellows  working  as  mul5-­‐disciplinary  research  teams  across  Engineering,  Science  and  Environment.    

l  Research  areas:  §  Renewable  Energy  §  Storage  &  Transport  §  Conversion  Technologies  §  Emission  Management  §  Power  System  Op5miza5on  §  Sustainable  Energy  Policy  

ISS4E  and  WISE!

§  Conserva5on,  Demand  Mgmt,  Energy  Efficiency  

§  Green  Auto  Powertrain    §  ISS4E  

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l  Sensors for building monitoring"l  Smart power strips for home monitoring and control"l  ENVI systems for home energy data collection"l  Custom-built wireless sensors for solar panel

monitoring""ISS4E is committed to system building and data collection and analysis"

Lab  facili;es!

Ongoing Projects

•  Architecture •  DR •  EV integration •  Pricing •  Data analysis •  Application/tool design •  Storage

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Outline  •  The electric grid as it is •  The smart grid •  ISS4E: Internet research and the smart grid •  Real-­‐Time  Distributed  Conges;on  Control  for  Electrical  Vehicle  Charging

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Challenges  posed  by  EV  integra5on  

ü  The  large-­‐scale  introduc5on  of  electric  vehicles  (EVs)  will  greatly  affect  the  electrical  grid's  distribu5on  system.    

ü  Each  EV  can  impose  a  significant  load  on  the  distribu5on  network:  especially  with  L2  charging.    

ü  Using  lower-­‐level  (i.e.,  L1)  charging  does  reduce  the  impact  on  the  grid  but  greatly  increases  the  dura5on  of  the  charging  process.    

ü  There  is  an  inherent  trade-­‐off  between  charging  level,  charging  dura5on,  and  impact  on  the  grid.  

ü  EV  mobility  has  the  addi5onal  impact  that  EV  load  may  appear  at  different  parts  of  the  distribu5on  network  at  different  5mes.  

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Need  smart  charging  schemes  to  protect  the  grid  while  allowing  fast  charging  when  possible  

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How  to  control  EV  charging?  

ü Exis5ng  approaches  to  control  EV  charging:  –  use  a  central  controller  to  compute  a  charging  schedule  (using  power  flow  analysis)  that  does  not  congest  any  part  of  the  distribu5on  network.  It  requires  an  accurate  model  of  the  distribu5on  network  (typically  not  available  or  not  up-­‐to-­‐date).  

–  cast  the  control  algorithm  in  the  form  of  a  distributed  op5miza5on.  

ü Both  approaches  need  to  predict  the  future  demand  from  non-­‐EV  loads,  the  number  of  charging  EVs,  and  their  ini5al  SoC  so  as  to  compute  a  schedule  ahead  of  ;me.    

ü The  safety  margin  built  in  to  hedge  against  predic5on  errors  makes  both  approaches  overly  conserva5ve.  

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Our  Proposal  

ü The  future  smart  grid  is  likely  to  have  a  large  number  of  measurement  and  control  devices  that  are  interconnected  by  a  ubiquitous  communica5on  network.  

ü We  propose  to  use  fast-­‐;mescale  measurements  and  communica5on  to  control  EV  charging  in  real  ;me,  mo5vated  by  techniques  for  conges5on  control  in  the  Internet.  

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More  specifically  

ü  The  propaga5on  delay  between  any  EV  charger  and  its  connected  substa5on  is  less  than  1ms.    

ü  Hence,  it  is  feasible  to  design  and  implement  a  control  algorithm  that  changes  the  EV  charging  rate  in  response  to  the  conges5on  state  of  the  distribu5on  system  (a  func5on  of    the  uncontrollable  loads)  every  few  milliseconds  (same  order  of  magnitude  as  one  cycle  of  AC  power  (16.6ms)).    

ü With  our  proposed  approach,  if  an  EV  is  charging  at  a  rate  that  affects  the  reliability  of  the  grid  (overhea5ng  a  transformer  or  overloading  a  feeder)  its  rate  can  be  decreased  in  a  few  cycles,  aver5ng  damage  and  the  invoca5on  of  grid  self-­‐protec5on.    

ü  This  fundamental  insight  changes  the  approach  to  EV  charging  from  a  slow  centralized  or  decentralized  op5miza5on  approach  to  a  fast  dynamic  approach.  

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The  5me  scales  

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Contribu5ons  

ü We  show  that  the  conges5on  control  problem  in  the  context  of  a  distribu5on  system  is  similar  in  many  aspects  to  the  conges5on  control  problem  in  the  Internet.  

ü We  propose  a  measurement  and  signaling  architecture  to  provide  real-­‐5me  explicit  feedback  to  EV  chargers.  

ü We  present  three  real-­‐5me  distributed  conges5on  control  mechanisms  for  charging  EVs.  

 Our  focus  is  on  establishing  a  vision  and  proposing  a  high-­‐level  architecture,  rather  than  valida5on  and    analysis,  which  we  defer  to  further  study.  

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The  System  

30 Today Tomorrow

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Conges5on  Control:  Internet  vs.  Grid  

ü  Defini;on  of  conges;on:  a  user  sees  conges5on  if  –  In  CN:  buffer  overflow  on  a  path  –  In  DN:  the  current  passing  through  at  least  one  feeder  on  the  path  

persistently  exceeds  its  current  limit  or  the  winding  hot  spot  temperature  of  at  least  one  transformer  exceeds  a  threshold  

ü  Topology:    –  The  Internet  is  a  general  mesh  network  consis5ng  of  many  sources  and  

des5na5ons  that  are  connected  by  communica5on  links  and  routers.  If  a  source  congests  a  link  by  sending  a  burst  of  data,  all  other  sources  that  send  data  through  this  link  see  its  impact  on  their  QoS  even  if  their  packets  are  going  to  different  des5na5ons.    

–  A  distribu5on  system  has  a  tree  topology  in  which  every  node  has  a  parent  which  supplies  its  demand  and  the  root  of  the  tree  supplies  the  demand  of  all  loads  in  this  tree.  If  a  few  loads  congest  a  feeder  by  consuming  high  power,  only  downstream  loads  that  are  supplied  by  this  feeder  are  affected.  Other  loads  located  in  this  tree  will  not  be  affected.   31

Conges5on  Control:  Internet  vs.  Grid  

ü  Infrastructure  for  sending  measurement  and  control  signals:  –   In  the  Internet,  data  packets  carry  control  informa5on  and  therefore  the  same  infrastructure  is  used  for  transmivng  both  data  and  control  signals  

–   Power  lines  deliver  electricity  to  customers  and  conges5on  signalling  is  done  separately  (using  an  auxiliary  comm.  network).  

ü   Conges;on  no;fica;on:    –  There  are  two  types  of  conges5on  feedback  in  the  Internet:  explicit  and  implicit.  Intermediate  routers  can  explicitly  report  conges5on  to  end-­‐nodes.  End-­‐nodes  can  also  infer  conges5on  by  measuring  packet  loss  or  es5ma5ng  the  round-­‐trip  delay;  this  is  known  as  implicit  conges5on  no5fica5on.    

–  In  the  grid  it  is  difficult  to  infer  conges5on  implicitly.    

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Conges5on  Control:  Internet  vs.  Grid  

ü Self-­‐protec;on:  Both  systems  protect  themselves  against  conges5on.    –  Internet  routers  are  configured  to  drop  packets  to  avoid  conges5on.    

–  In  a  distribu5on  network,  the  protec5on  system  consists  of  re-­‐  lays  and  circuit  breakers  that  trip  and  disconnect  the  load  in  case  of  conges5on.    

The   protec5on   mechanisms   differ   in   that   the   packet  dropping   schemes   do   not   interrupt   service   to   clients  (though   it   may   impact   the   QoS);   however,   when   a  protec5ve   relay   trips   all   downstream   loads   are  disconnected,  leading  to  a  service  disrup5on.  

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Conges5on  Control:  Internet  vs.  Grid  

ü Uncontrolled  loads:  Both  systems  are  designed  to  deal  with  uncontrollable  demands.    –  Specifically,   UDP   traffic   is   uncontrolled   in   the   Internet   and  conges5on   control   mechanisms   do   not   deal   with   this   type   of  traffic   (although   UDP   packets   can   easily   be   filtered   and  discarded  if  necessary).    

–  Similarly,  there  are  uncontrollable  loads  in  the  grid  which  do  not  respond   to   control   signals.   The   main   difference   is   that   the  current   infrastructure   does   not   permit   the   segrega5on   of   the  uncontrolled  loads  from  the  controlled  ones.  

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Our  Vision  ü  Our  vision  requires  a  joint  measurement  and  signaling  infrastructure  to  

detect  the  outset  of  conges5on  very  quickly  and  to  inform  the  chargers  that  are  in  the  congested  region  (others  should  not  need  to  decrease  their  rates).  

ü  When  an  EV  needs  charging,  it  starts  charging  at  a  low  rate  and  then  increases  it  slowly  up  to  L2  as  long  as  it  does  not  receive  a  signal  from  the  grid  that  indicates  (pre)-­‐conges5on.    

ü  This  (pre)-­‐conges5on  might  be  due  to  the  chargers  themselves  or  to  an  increase  in  the  uncontrollable  loads.    

ü  Thanks  to  the  efficient  communica5on  and  control  infrastructure,  the  charger  can  react  nearly  immediately  to  conges5on  signals,  aver5ng  the  use  of  grid  protec5on  ac5ons  from  circuit  breakers.    

ü  Note  that  no  predic5on  of  the  EV  SoC  or  their  mobility  is  required:  charging  happens  for  the  EVs  present  in  the  system  at  any  point  in  5me,  and  their  charging  rates  are  controlled  every  few  milliseconds.  

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Design  Goals  

ü Maintaining  grid  reliability:  a  crucial  goal  in  the  design  of  a  control  mechanism  for  charging  EVs  is  to  maintain  the  same  level  of  reliability  and  to  ensure  that  no  addi5onal  power  outage  is  introduced  due  to  their  charging.  

ü   High  u;liza;on:  without  overshoo5ng  via  a  margin  (λ)  used  to  hedge  against  the  risk  of  system  over-­‐loading  due  to  transient  system  behaviour.  

ü   Minimize  oscilla;ons:  Oscilla5ons  are  usually  inefficient  and  could  affect  the  life5me  of  the  ba\eries.  

ü   Fairness:  Alloca5on  of  charging  rates  to  EV  chargers  must  be  done  according  to  a  fairness  criteria.  

ü   Robustness:  a  fail-­‐safe  mechanism  is  needed  in  case  of  failure  of  the  comm.  infrastructure  

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Measurement  

ü  The  distribu5on  network  is  equipped  with  measurement  devices  con5nuously  measuring  the  current  in  the  feeder  and  the  winding  hot  spot  temperature  of  the  transformer.    

ü  They    compute  an  average  of  the  current  and  the  temperature  every  tM  ms.    

ü  Hence,  we  can  compute  the  difference  between  the  current  limit  of  the  feeder  (with  a  margin)  and  the  current  passing  through  it  and  the  difference  between  the  maximum  winding  temperature  (with  a  margin)  and  the  measured  temperature.    

ü  If  these  differences  are  posi5ve  it  means  that  the  feeder/transformer  is  not  over-­‐loaded.  Otherwise,  it  is  nearly  overloaded  (depending  on  the  value  of  the  margins)  and  the  protec5ve  relay  will,  most  probably,  trip  if  this  condi5on  persists  or  worsens.   37

The  Logical  Infrastructure  

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Decision  Making  (Rate  Alloca5on)  

ü The  space  of  possible  rate  alloca5on  algorithms  is  large.  

ü We  outline  three  distributed  conges5on  control  schemes  that  illustrate  different  points  in  the  design  space:  –  Intelligent  Endpoint  Approach  –  Local  Scheduling  Approach  –  Distributed  Explicit  Rate  Control  

ü Our  chosen  algorithms  differ  in  the  en55es  that  makes  decisions  about  charging  rates  of  EVs  and  the  degree  of  communica5on  overhead.  

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Intelligent  Endpoint  Approach  

ü  In  this  approach,  each  EV  charger  independently  decides  on  its  charging  rate,  much  like  a  TCP  endpoint.    

ü  Decision  making  is  distributed  because  every  EV  charger  sets  its  rate  without  direct  knowledge  about  the  rates  of  other  chargers.  

ü MCC  node  ac;ons:  Every    MCC  node  con5nuously  measures  and  checks  if  its  corresponding  feeder/transformer  is  congested.  Every  tM  ms,  the  root  MCC  node  broadcasts  a  packet  that  contains  a  conges5on  flag.  This  packet  is  routed  hop-­‐by-­‐hop  by  intermediate  MCC  nodes  un5l  it  reaches  the  EV  chargers.  Each  congested  intermediate  MCC  node  can  modify  the  packet  that  it  receives  from  its  parent  by  sevng  the  conges5on  flag.    

ü  EV  charger  ac;ons:  Every  EV  charger  examines  the  conges5on  flag  upon  receiving  a  packet  from  its  parent  and  uses  an  AIMD  algorithm  to  set  its  rate.   40

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Local  Scheduling  Approach  

ü The  EV  charger  is  slaved  to  its  parent  MCC,  which  makes  local  scheduling  decisions  on  behalf  of  the  EVs  a\ached  to  it.    

ü Decision  making  is  distributed  in  this  strategy  similar  to  the  previous  scheme;  however,  it  is  done  by  leaf  MCC  nodes  instead  of  EV  chargers.  This  permits  to  discriminate  amongst  and  schedule  their  downstream  EV  chargers.  

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Distributed  Explicit  Rate  Control  ü  In  this  approach,  all  MCCs  coordinate  to  select  a  charging  rate  for  their  

subtree,  in  an  a\empt  to  minimize  oscilla5ons  (loosely  draws  on  XCP).  ü  EV  charger  ac;ons:  Every  EV  charger  sends  a  packet  toward  the  root  every  tC  

ms  to  nego5ate  its  charging  rate  for  this  control  interval.  This  packet  contains  the  current  charging  rate  along  with  the  requested  next  charging  rate.  When  this  packet  returns  to  the  EV  charger,  it  adjusts  its  charging  rate  to  the  charging  rate  encapsulated  in  the  packet.  

ü  MCC  node  ac;ons:  When  an  MCC  node  receives  a  rate  requested  packet  of  an  EV  charger,  it  may  reduce  the  request  rate  if  its  corresponding  feeder  is  congested  and  this  rate  is  higher  than  the  fair  share  of  this  EV  charger.  Then,  it  forwards  all  packets  that  it  has  received  to  its  parent.  

ü  When  a  rate  request  packet  arrives  at  the  root  MCC  node,  the  root  sends  it  back  to  the  EV  charger  along  the  same  path.  

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Comparison  

ü Who  has  control?  :  In  the  first  scheme,  control  is  distributed  among  EV  chargers.  However,  the  second  and  the  third  schemes  cede  control  to  the  u5lity    

ü Oscilla;ons:  The  third  scheme  tries  to  minimize  oscilla5ons  by  accurate  and  con5nuous  computa5on  of  the  remaining  capacity  and  doing  rate  alloca5on  on  this  basis.    

ü Communica;on  overhead:  The  third  scheme  has  a  higher  overhead  because  control  packets  travel  bi-­‐direc5onally  rather  than  unidirec5onally,  as  they  do  in  the  first  two  schemes.  

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Conclusions  

ü A  paradigm  shid!  ü Need  to  transform  this  vision  into  prac5cal  solu5ons  that  are  efficient  and  robust!  

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