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Computing Advances for the Smart Grid Vision - CEC...
Transcript of Computing Advances for the Smart Grid Vision - CEC...
Computing Advances for the Smart Grid Vision
Steve WidergrenIEEE Computer SocietySmart Grid Vision Project
IEEE STC 20143 April 2014
Forces Shaping the Future of Energy
Worldwide, demand for energy -- and costs -- are rising
Climate change policiesset environment as a parameter in the cost equation
Technology is enabling new ways to get things done
Customers want morevalue for their energy dollar
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The Challenge Ahead is Complex
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Electrify transportation sector to reduce dependence on imported oil
Deliver significant amounts of renewable generation quickly
Maximize benefits of end-use efficiency and storage
Meet future carbon and emissions constraints
Affordable, Power
Reliable Power
Secure Power
Historical Expectations
Emerging Expectations
What will it take to get us there?
Policy
Science &Technology
Knowledge andTransparency
Capital
• government legislation
• R&D Labs• Universities• Private Sector
• Venture capital• Institutional investments• New business models
A New Relationship with Electricity
Understand how we use electricityConsumption by appliance and activityMajor contributors of electricity useEfficiency and waste
Appreciate its time-varying valueLike freeways, system loads vary over timeExpense and limits to capacity - congestion
Realize we can make a differenceAlternative operating behaviorWhere investments in efficiency can pay-off
Important enablersInformation on usage and costsAutomation to represent our desires
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Electricity Ecosystem Transformation
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Smart grid opportunity• Paradigm shift in
electricity• Fresh business
models• Innovative
government and institutional roles
Drives change, restructure• Cylinders of
excellence crumbling
• Rapid evolution
Creates work and jobs• Affects utility,
industrial, commercial, residential sectors
• Technology deployment
• New services
Necessitates education• Greater
educated workforce needed
• Curriculum reorganization
• Holistic, systemic methods
Contents
Purpose and ObjectivesProject TeamProject ApproachVision Summary– Architectural Concepts (Tier 1)– Functional Concepts (Tier 2)– Technological Concepts (Tier 3)
Future Work
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Smart Grid Vision Project (SGVP)Objective– Develop a “Smart Grid Vision” Report
Role of Computing, 2030 and beyond Incorporate futuristic concepts
Purpose– Stimulate research and development, education, standards
Project Groundrules and Assumptions– There are no wrong visions for the future– Not bounded by current understanding of technology– Not constrained by today’s policies and practices– Not driving toward a common end vision – this was not an
engineering exercise– Visions may be complimentary or contradictory
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Smart Grid Vision Project TeamManagement Team– Dan McCaugherty (Athena Sciences Corp)– Steve Widergren (US Dept of Energy, Pacific Northwest National Lab)– Dr. Joe Chow (Rensselaer Polytechnic Institute)– Bill Ash, Sponsor IEEE SA– Dr. Roger Fujii, IEEE CS Executive, Division VIII
Topic Area Leadership– Cyber Security – Dr. William Sanders (Univ of Illinois – Urbana)– Software Systems Engineering – Dr. Andreas Tolk (Old Dominion Univ)– Operations/Monitoring/Control – Dr. Dave Cartes (Florida State Univ)– Planning/Analysis/Simulation – Dr. Joe Chow (RPI)– Communications Networks – Dan McCaugherty (Athena Sciences Corp)– Computing – Steve Widergren (DOE PNNL)– Visualization/Data Management – Dr. Joe Chow (RPI)
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Project ApproachElaborate Visions in three Concept Tiers – Architectural (AC), Functional (FC), and Technological (TC)Visions along lower Tiers often stimulated higher Tiers
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Capabilities
Architectural Concepts (11 Total)
Supply Side (4)– Renewable resources– Energy storage & balancing– Integrated islands– Isolated islands
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Demand Side (4)– Utility demand response– Aggregated local energy– Self-owned base energy– Electric transportation
System Concepts (3)– Coherent system operations– Complex autonomous adaptive systems– System and cyber security
Complex Autonomous Adaptive Systems
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Level 1: Self-awareness, Context-awareness
Level 2: Self-configuring, optimizing,
healing, protecting
Level 3: Self-adaptive
Sensors and intelligence to monitor states and behaviorAwareness of location and ops environment
Defend against attacks & mitigate effectsRespond to changesManage performance & resources to satisfy different users
Self-adaptation services & information flow between distributed grid services - balancing global properties and grid health managementEmergent behavior emblematic
Hierarchical aspects of autonomy [1]
[1] Salehie, M., Tahvildari, L. 2009. "Self-adaptive software: Landscape and research challenges." ACM Transactions on Autonomous and Adaptive Systems (TAAS) 4, no. 2.
Functional Concepts (27 Total)
Systemic (8)Cyber Security– Information security– Control security– Privacy– Supply chain resilience– Intrusion tolerance
Software/Systems Engineering– Unsupervised autonomy– Social nodes– Autonomous validation
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Enabling (7)Comm. & Networks– Intelligent devices/nodes– Converged communications– Hardware/Software refresh
Visualization and Data Mgt– State awareness– Failure awareness, restoration
Markets and Economics– Wholesale power market– Dynamic demand side markets
More Functional Concepts – Performance (12)
Operations, monitoring, and control subtopic (8)– Bulk system transmission dynamic operations– Operations congestion detection– Power flow forecasting in distribution networks– Direct load control events– Island-to-island stable power flow control– Automated grid load flow coordination– Process coordination of industrial manufacturing– Commercial and industrial building coordination
Planning, analysis and simulation subtopic (4)– Bulk system transmission planning– Asset management and maintenance– Resilient systems– Command, control, and automated functions
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Automated Intrusion Tolerance ConceptHighly integrated cyber-physical components– Detect attacks automatically– Diagnose root cause accurately– Real-time, adaptive response to malicious adversaries
Regular manual intervention unnecessaryEach time instant accurately determines security stateMonitor and detect exploitation of known vulnerabilitiesIntrusion tolerance strategies’ algorithms compare criticality of assetsMathematical decision making framework selects optimal tolerance strategy
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Distributed System Architecture (4)– Self-integrating systems and standards – Distributed multi-agent architecture – Virtual computing architecture– Messaging-oriented middleware
Computer Applications (7)– Market-Inspired (transactive) control– Monitoring and control/modeling and simulation tools– Signal processing for control, protection and performance
qualification/performance monitoring– State estimation analysis algorithms– Contingency, preventive, and corrective control analysis– Stochastic analysis for system operations, planning, forecasting – Prognostics and asset management
Technological Concepts (21 Total)
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Technological Concepts
Information Science (4)– Visualization– Artificial Intelligence, data analytics, fast mathematics and high-
performance computing– Internet and real-time systems– Software verification and validation
Cyber Security (6)– Trusted component validation– Portable identity – bidirectional authentication support– Hierarchical sense making (HSM) and collaborative HSM agent
networks – Massive parallel pattern detection– Patterns for agile self-organizing security– Information security technology
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Distributed Multi-Agent Architecture
Bottoms-Up organic control- Individual needs negotiated- Regional imbalances
smoothed- Power flow checked against
stability constraints
Local optimization coordinated step-wise up, forming ever-larger networks
Monitoring & Control/Modeling & Simulation (M&S) Tools
Problem:Proprietary control system M&S tools do not interactDifficult to leverage integrated M&S component capability for dynamic SCADA system simulation
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Future Vision:Standard data exchange formatsScalable fidelity (size, performance, level of detail)Accommodate third party M&S tools (e.g., state estimation)Grid device vendors supply compatible modelsUses – grid design, planning, operations
Software Verification and Validation
Future Smart Grid is beyond the scale of existing high integrity systems
Expresses proper models of system element behaviors subject to formal methods
– Use of domain specific modeling languages
– Machine learning needed to construct the formal specifications
– Computational intelligence needed to guide the verification mechanism
Continuous run-time verification needed for adaptive elements
– High performance simulations constantly evaluating emergent behaviors
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Technological Concepts
1: S
elf-
inte
grat
ing
syst
ems a
nd st
anda
rds
2: D
istr
ibut
ed m
ulti-
agen
t arc
hite
ctur
e
3: V
irtua
l com
putin
g ar
chite
ctur
e
4: M
essa
ging
-orie
nted
mid
dlew
are
5: M
arke
t-In
spire
d (t
rans
activ
e) co
ntro
l
6: M
onito
ring
and
cont
rol/
mod
elin
g an
d si
mul
atio
n to
ols
7: In
form
atio
n pr
oces
sing
for c
ontr
ol,
prot
ectio
n an
d pe
rfor
man
ce
qual
ifica
tion/
perf
orm
ance
mon
itorin
g
8:St
ate
estim
atio
n an
alys
is a
lgor
ithm
s
9: C
ontin
genc
y, p
reve
ntiv
e an
d co
rrec
tive
cont
rol a
naly
sis
10: S
toch
astic
ana
lysi
s for
syst
em o
pera
tions
, pl
anni
ng, f
orec
astin
g
11: P
rogn
ostic
s and
ass
et m
anag
emen
t
12: V
isua
lizat
ion
13: A
rtifi
cial
Inte
llige
nce,
dat
a an
alyt
ics,
fast
m
athe
mat
ics a
nd h
igh-
perf
orm
ance
co
mpu
ting
14: I
nter
net a
nd re
al-t
ime
syst
ems
15: S
oftw
are
verif
icat
ion
and
valid
atio
n
16: T
rust
ed co
mpo
nent
val
idat
ion
17: P
orta
ble
iden
tity
– bi
dire
ctio
nal
auth
entic
atio
n su
ppor
t
18: H
iera
rchi
cal s
ense
mak
ing
19: M
assi
ve p
aral
lel p
atte
rn d
etec
tion
20: P
atte
rns f
or im
plem
entin
g ag
ile se
lf-or
gani
zing
secu
rity
21: I
nfor
mat
ion
secu
rity
tech
nolo
gy
Functional Concepts1: Information security x x x x x x x2: Control security x x x x x x3: Privacy x x x4: Supply chain cyber resilience in software and hardware x x x x x x x x x x5: Automated intrusion tolerance x x x x x x6: More dependence on unsupervised autonomy x x x x x x x x x7: Social nodes x x x x x x x x x x8: Smart Grid autonomous validation x x x x x x x9: Proliferation of intelligent devices and nodes x x x x x x x x x x x x x x10: Secure converged communications x x x x x x x x11: Smart Grid hardware and software refresh x x12: State awareness x x x x x x x x x x x13: System failure awareness, emergency response and system restoration x x x x x x x x x x x x x x14: Wholesale electric power market policy, operation and design x x x x15: Emergent dynamic demand side markets x x x x x16: Bulk system transmission dynamic operations x x x x x x x x x17: Operations congestion detection x x x x x x x x18: Power flow forecasting in distribution networks x x x x x19: Direct load control events x x x x x20: Island-to-island stable power flow control x x x x x x x x21: Automated grid load flow coordination x x x x x x x x22: Advanced process coordination of industrial manufacturing x x x x x x x x x23: Commercial and industrial building coordination x x x x x x x x x24: Bulk system transmission planning x x x25: Asset management and maintenance x x x x x x26: Resilient systems x x x x x x x x x x x x27: Advanced command, control, and automated functions x x x x x x x x x
Future Work
Pursue opportunities for StandardsPromote education and training for relevant computer science disciplinesRe-address the visions in 2 to 5 years– Impact of emerging sciences– New smart grid concepts
Business/economic value propositions Lessons learned Promising technologies
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Authors Acknowledgement
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Al Valdes, University of IllinoisAndreas Tolk, Old Dominion UniversityAndrew Wright, N-Dimension SolutionsAnnabelle Lee, Electrical Power Research InstituteAranya Chakrabortty, North Carolina State UniversityDan McCaugherty, Athena Sciences CorporationDave Cartes, Florida State UniversityDavid P. Chassin, Pacific Northwest National LabDave Hardin, EnerNOCDiane Hooie, National Energy Technology LabEdmond Rogers, University of IllinoisEugene Litvinov, ISO New EnglandGlen Chason, Electrical Power Research InstituteJennifer Bayuk, Stevens Institute of TechnologyJianhui Wang, Argonne National LaboratoryJoe H. Chow, Rensselaer Polytechnic InstituteKeith Thal, Sypris SolutionsKlara Nardstedt, University of IllinoisKumar Venayagamoorthy, University of MissouriLeigh Tesfatsion, Iowa State UniversityMark Blackburn, Stevens Institute of Technology
Mark Smith, Sandia National LabMathias Uslar, OFFISNikos Hatziargyriou, National Tech University of AthensPaulo Ribeiro, Eindhoven University of TechnologyPrabir Barooah, University of FloridaRakesh Bobba, University of IllinoisRick Dove, Paradigm Shift InternationalSaman Zonouz, University of MiamiSean Meyn, University of FloridaSebastian Lehnhoff, OFFISSteve Ray, Carnegie Mellon UniversitySteve Widergren, Pacific Northwest National LabSteven Low, California Institute of TechnologySvetlana Pevnitskaya, Florida State UniversityTim Yardley, University of IllinoisTodd Montgomery, InformaticaWenxin Liu, New Mexico State UniversityWilliam Sanders, University of IllinoisZbigniew Kalbarczyk, University of IllinoisZhi Zhou, Argonne National Lab
Smart Grid Vision Report for Computing Link
http://www.techstreet.com/ieee/products/1857774
Contacts:Dan [email protected] [email protected]