Cloud  Compu)ng  and  the  Smart  Grid:  Threat,  Menace  or  Salva)on?  

Chair:  David  Bakken,  Washington  State  University    

ISGT  2014  Panel  February  21,  2014  

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Outline  of  this  Panel  

•  Overview  of  cloud  compu)ng  (me:5+  min)  •  Opening  statements  by  panelists  (5min  each)  

– Killer  apps  for  grid  cloud  compu)ng  – Achilles  Heel(s)  for  grid  cloud  compu)ng  – Anything  else  to  toss  out  for  considera)on  

•  Ques)ons  to  panel  (60min)  – Lets  make  this  interac)ve  with  the  audience!  

•  Wrapup  &  Announcements  (5min)  

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The  Cloud  is  Coming!  

•  Cloud  compu)ng  permits  “consolida)on”  – 10x  or  be\er  reduc)ons  in  cost  of  opera)on    – Far  be\er  equipment  u)liza)on  and  management  – New  styles  of  elas)c  compu)ng,  poten)al  to  compute  directly  on  massive  data  collec)ons  

– Adds  up  to  a  new  way  of  compu)ng  that  forces  us  to  undertake  new  kinds  of  thinking  

•  But  is  it  suitable  for  the  smart  grid?  – Different  requirements  (opera)ons,  regula)ons,  )  

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Cloud  Compu)ng  for  the  Smart  Grid  

•  Real-­‐)me  collec)on  of  data  from  widely  deployed  PMU  and  other  SCADA  data  sources  – Data  rates  within  large  regions  high  – Robust  real-­‐)me  tracking  enables  shared,  consistent  situa)onal  awareness  and  coordina)on  

– By  reusing  today’s  scalable  cloud  infrastructure,  we  achieve  a  low-­‐cost  solu)on  based  on  proven,  universally  accessible  technologies  and  hosted  on  geographically  and  technically  diverse  data  centers  

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Defining  Terms  

•  Computer  Networking:  gets  bytes  of  data  from  Point  A  to  Point  B  with  some  proper)es  

•  Distributed  Compu4ng:  a  discipline  above  the  network  layers  that  ask  how  we  can  best  use  the  network  to  help  applica)ons  – Coordinate  &  synchronize  – Replicate  – Higher-­‐level  building  blocks  for  programmers  (middleware)  

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Defining  Terms  (cont.)  

•  Distributed  compu)ng  is  where  we  get  – Cloud  compu)ng  – Middleware  

•  Middleware:  a  layer  above  the  opera)ng  system  but  below  the  applica)on  that  provides  a  common  programming  abstrac)on  across  a  network  – Heterogeneity:  CPU,  net.  Tech.,  language,  vendor  – Shield  programmers  from  complexity  of  DSs  

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Defining  Some  Cloud  Terms  

•  So9ware  as  a  Service  (Saas):  online  applica)ons  delivered  as  web  services  

•  Pla?orm  as  a  Service  (PaaS):  customers  can  develop  and  deploy  applica)ons  on  large  scale  compu)ng  infrastructure  &  meet  changing  demands  

•  Infrastructure  as  a  Service  (IaaS):  cluster  of  virtualized  computer  resources  provisioned  – Supports  rapid  elas)city  of  recources  

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Defining  Some  Cloud  Terms  (cont)  

•  Public  cloud:  a  third-­‐party  provides  mul)-­‐tenant  cloud  infrastructure  and/or  services  

•  Private  cloud:  cloud  runs  within  a  company’s  own  data  center  for  internal  users  and  partners  – RTE  France  has  one!  

•  Hybrid  cloud:  using  both  private  and  public  clouds  for  a  given  applica)on/service  – Ojen  just  for  spare  capacity  via  public  cloud  

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Meet  the  Panelists  

•  Mike  Kurland:  Execu)ve  Architect,  Energies  &  U)li)es  @  IBM  – Led  smart  grid  efforts  with  many  u)li)es  

•  Dr.  Eugene  Litvinov:  CTO  @  ISO  New  England  – Degrees  &  Experience  in  both  power  &  IT  

•  Dr.  Tim  Heidel:  Program  Manager  @  ARPA-­‐E  – Research  Director:  2011  MIT  future  grid  study  

•  Prof.  Ken  Birman:  Comp.  Sci.  Prof.  @  Cornell  – SW  has  run  in  French  ATC,  NYSE,  Aegis  cruisers,  …  

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Mike  Kurland  IBM  Corpora)on  Execu)ve  Architect,  Energy  and  U)li)es  

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A  killer  applica)on  for  cloud  compu)ng  and  the  power  grid?    Deep  Analy)cs!!  –  mining  inferences  from  deep  and  rich  pools  of  data  –  genera)ng  granular  and  high  confidence  predic)ons  to  improve  opera)ons  

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Correlation Optimization Allocation

OPERATIONS ACTION

ReliabilityPerformanceCAPEXOPEX

InspectionPreventive maintenanceComponent replacementFlow redirectionEmergency action

Weather/season

Demand

Environment

Maintenance

Construction actions

Crew skills and experience

Measurements

Control actions

Equipment failures

Maintenance resources

Equipment age

Economic factors

Equipment rating

BUSINESS OUTCOMES

Training actions

EXOGENOUS CONDITIONS

OPERATIONS

ASSET ATTRIBUTES

Equipment supplier

Equipment topology

ANALYTICS ENGINE

An  Achilles  Heel?  •  Actually,  there  are  several  concerns  

– Cyber  Security  and  Data  Privacy    -­‐-­‐  How  to  assure  u)lity  stakeholders  they  are  safeguarding  customer  data  and  opera)onal  systems?  

– Financial  Model,  CAPEX  vs  OPEX  –  rate  recovery  formula  encourages  capital  investment  because  it  provides  a  rate  of  return  on  the  rate  base.  The  more  a  u)lity  invests,  the  more  money  it  earns.    

R = O + (V – D)*r R = utility's total revenue requirement or rate level. This is the total amount of money a regulator allows a utility to earn. O = utility's operating expenses. V = gross value of the utility's tangible and intangible property. D = utility's accrued depreciation. Combined (V - D) constitute the utility's rate base, also known as its capital investment. r = rate of return a utility is allowed to earn on its capital investment or on its rate base.

One  More  Thing…  

•  Everyone  loves  the  killer  app,  but  there  are  many  simple  opportuni)es  to  get  started  

•  The  challenges  are  NOT  roadblocks  –  we  expect  Cloud  Compu)ng  to  transform  how  business  processes  are  delivered  

Eugene  Litvinov  

Chief  Technologist,  ISO  New  England  ISGT,  February,  Washington,  DC,  2014  

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Future  Grid  Architecture  and  Control  Paradigm  

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Transmission   Transmission   Transmission  

•  Decentralized,  loosely  coupled  system  is  more  resilient  

•  Coopera)on  vs.  Coordina)on  among  subsystems  

•  Methods  and  algorithms  to  support  spontaneous  ad-­‐hoc  coopera)on  between  subsystems  

Wide-­‐Area  Monitoring  System  16  

ISO 1

Regional PMU Repository

Joint Network Model

ISO 2

State    Es4mator  

Cloud-­‐Based  WAMS  Pilot  Project  

•  The  proof-­‐of-­‐concept  project  brings  together  synchrophasor  technology  and  cloud  compu)ng  to  explore  the  poten)al  combined  benefits  

•  The  envisioned  cloud-­‐based  data  repository  will  facilitate  streaming  and  exchanging  synchrophasor  data  from  different  en))es  by  taking  advantage  of  the  reliability,  resilience,  and  flexibility  offered  by  cloud  technology  

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Main  Challenges  

•  Cost  benefit  while  sa)sfying  addi)onal  requirements  that  are  not  ini)ally  built  into  cloud  compu)ng  model  

•  Security  and  privacy  •  Latency  •  Data  consistency  across  wide  area  •  Power  industry  conserva)sm  

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Ken  Birman  

Rao  Professor  of  Computer  Science  Cornell  University  

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Killer  Applica)ons?  

•  Over  the  horizon  “grid  radar”  helps  operators  understand  wide-­‐area    grid  stress,  disturbances  

•  Tools  (“apps  for  the  smart  grid”)  help  operators  cooperate  to  solve  problems,  search  knowledge                                                  base  for  past  situa)ons  with  similar                          fingerprint,  explore  what-­‐if  scenarios  

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Achilles  Heel?  •  Today’s  cloud  is  op)mized  for  applica)ons  with  weak  security  needs.    It  offers  scalable  snappy  response,  but  lacks  robust  guarantees.    Lacks:  – Hardened  network  protocols  aimed  at  consistent  but  )ghtly  controlled  sharing  for  collabora)on  

– A  new  distributed  security  model  suppor)ng  total  control  by  regional  operator,  controlled  data  flows  

•  To  leverage  the  cloud,  we  need  a  new  smart-­‐grid  technology  built  within  today’s  cloud  technology!  

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Tim  Heidel    

Program  Director    

Advanced  Research  Projects  Agency  –  Energy  (ARPA-­‐E)  

U.S.  Department  of  Energy  

   

February  2014  

Electric  Grid  Cloud  Applica)ons  •  What  makes  a  good  grid  cloud  applica3on?  

–  Geographic  distribu)on  of  data  producers/consumers  –  Benefit  from  cloud  ELASTICITY  and  “RENT”  opera)on  model    

•  Executed  occasionally  or  with  low  frequency    •  Ini)ated  to  handle  large  computa)on  requirements  •  Require  highly  variable  compu)ng  resource  levels  

–  Benefit  from  cloud  RESILIENCY  •  Redundancy  and  physical  distribu)on  of  compu)ng  resources  •  Efficient  infrastructure  and  data  maintenance  (e.g.  backup)  

•  What  is  a  possible  killer  app?  –  Massively  parallel  “real-­‐)me”  con)ngency  screening  

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Achilles  Heel  for  Grid  Cloud  Compu)ng    •  Resistance  to  cloud  compu3ng?  

–  Unavailability  during  major  failures    –  Inability  to    precisely  specify  the  geographic  loca)on  of  compu)ng  resources  (public  clouds)  

–  System/Data  security  and  privacy  concerns  

•  Indifference  to  cloud  compu3ng?  –  U)li)es/ISOs  do  not  have  the  "hair  on  fire"  need  for  cloud  plavorms  

•  “Conven)onal”  high  performance  compu)ng  s)ll  catching  on  and  advancing  rapidly.  

–  Lack  of  suitable  “current”  (legacy)  applica)ons  •  Applica)ons  associated  with  “smart  grid”  are  s)ll  in  the  future  

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ARPA-­‐E  Investments  in  Cloud  Compu)ng  

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•  Cornell’s ISIS2 virtual synchrony toolkit for managing cloud resources;

•  WSU’s GridStat middleware for efficient data communication;

•  Demo: Hierarchical Linear State Estimator

GridCloud Demand Response Optimization and Management System for Real-Time (DROMS-RT) •  Developing scalable SaaS platform for

implementation and management of DR Programs.

•  Standard comm. protocols for real-time DR signaling.

•  Utilize machine learning to forecast and characterize customer response to DR signals.

Issues  Raised  Killer  Apps  

•  Deep  Analy)cs  •  Decentralized  &  loosely-­‐coupled:  coopera)on  &  coordina)on  among  subsystems  

•  Over-­‐the-­‐horizon  “grid  radar”  

•  Tools  for  operators  to  coopera)vely  solve  probs  

•  Parallel  RT  con)ng.  screen.  

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Achilles  Heels  •  Security  &  Privacy  •  Financial  Model  •  Cost-­‐benefit  for  cloud++  •  Latency  •  Data  consistency  •  Robustness  •  Un-­‐hardened  net  protocols  •  Can’t  spec/know  loca)ons  •  Indifference  

Announcements  •  NIST  efforts  on  cloud  compu)ng  for  smart  grid  

– DETAILS  TO  ADD  (?Flyer)  •  IEEE  PES  General  Mee)ng  Tutorial  

– Overview  of  Middleware,  Cloud  Compu)ng,  and  Distributed  Compu)ng  

•  Workshop  on  Trustworthiness  of  Smart  Grids  (slide)  •  CRC  Press  book  (slide)  •  Any  else?  •  Grid  cloud  compu)ng  altar  call  J  

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ToSG  Workshop  •  1st  Intl.  Workshop  on  Trustworthiness                              of  Smart  Grids  (tosg-­‐workshop.org)  –  June  23  in  Atlanta  –  In  conjunc)on  with  DSN  2014,  the  #1  intl.  conf.  on  dependable  compu)ng  (<<20%  acceptance  rate)  

– “Trustworthiness”  very  broad:  fault  tolerance,  performance,  security,  privacy,  resilience,  …  

– Bakken,  Birman,  ….  &  power:  Sakis  Meliopoulos,  Geert  Deconinck,  Lars  Nordstrm,  Ma\hias  S)jer  

•  Flyer  in  back:  submit  a  paper  or  simply  a\end  

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Related  Book  (flyer  in  back)  

Smart GridsClouds, Communications, Open

Source, and Automation

“… broad coverage of THE hot topics in present-day smart grid R&D. The specialvalue lies in the timely presentation of the latest research from sources from allover the world, giving an interesting global picture of the current state of the art.… Especially noteworthy are the different contributions merging smart grid developments with cloud computing … written by a mixture of top industrial experts and key academics … The diverse chapters are written in a down-to-earthlanguage, making the book practical enough to understand how smart grid technologies work and clearly pointing out which problems the R&D communityis currently facing.”

—Dr. Lars T. Berger, BreezeSolve

The electric power grid is in the early stage of a sea change, and hype aboutsmart grid is at a high point. Investment money is pouring in searching for a"killer app" and profits. The smart grid revolution will require utilities and theirsuppliers to develop new business models, strategies, and processes. Truthfully,nobody knows which business models will survive the Darwinian contest, butcompanies heeding the lessons here can increase their chances of success.

Smart Grids: Clouds, Communications, Open Source, and Automation detailsa comprehensive outline of how smart grids will provide better management ofthe power system: utilities will be able to automate meter reading and billingprocesses; consumers will be more aware of their energy usage and its associatedcosts. It provides test cases of real-life implementation, describes smart grid sim-

ulation software, and discusses implementation trade-offs.

Developed by an expert editorial team, the book includes contributionsfrom specialists in academia and industry across the globe, including

The US, China, Canada, France, Belgium, Greece, Cyprus, Italy,Mexico, and Thailand. These experts provide a state-of-the-art

description of the smart grid that demonstrates how it canreduce utilities’ costs (losses), and improve ROI (return

on investment), and increase service and benefits forconsumers.

• Offers a state-of-the-art descriptionof the smart grid, focusing on howit is being used

• Describes smart grid simulationsoftware

• Contains test cases of real-life implementation

• Includes case studies and industrialexamples

Features:

Edited by

David BakkenWashington State University, School of Electrical Engineering and Computer Science, Pullman, USA

Krzysztof IniewskiCMOS Emerging Technologies Research Inc., Vancouver, British Columbia, Canada

• • • • •

Catalog no. K21493May 2014c. 470 pp.

ISBN: 978-1-4822-0611-1$99.95 / £63.99

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