smart grid: status and challenges
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Smart Grid: Status and Challenges. S. Keshav School of Computer science University of Waterloo CCSES Workshop April 28, 2014. Outline. The grid has real problems t hat smart grids can solve These problems are intrinsic and difficult s o progress has been slow - PowerPoint PPT PresentationTRANSCRIPT
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Smart Grid: Status and Challenges
S. KeshavSchool of Computer scienceUniversity of Waterloo
CCSES WorkshopApril 28, 2014
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Outline
The grid has real problems that smart grids can solve
These problems are intrinsic and difficult so progress has been slow
Three areas where changes are imminent are solar, storage, and sensing I’ll give some examples of my work in these areas
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The grid has some real problems
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4
1. Overprovisioned
capacity
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2. Inefficient
5% better efficiency of US grid = zero emission from 53 million cars
http://www.oe.energy.gov/
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3. Aging
Post-war infrastructure is reaching EOL
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4. Uneven
TWh generatedChina 4,938US 4,256
Daily W/capita (2012 est.)
3951402
Wikipedia
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5. Poorly measured
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6. Poorly controlled
Electrons are not addressible
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7. Huge carbon footprint
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Smart grid vision
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Current grid
Renewables/low carbon Storage rich Sensing rich Control rich Flexible Energy frugal Decentralized generation
High carbon footprint
Little to no storage
Poorly measured
Poorly controlled
Ossified
Inefficient
Centralized generation
“Smart” grid
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Source: European technology platform: Smart Grids
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Intrinsic challenges
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1. Matching demand and supply
Barnhart et al, Proc. Energy and Environment, 6:2804, 2013
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2. Controlling distributed generators
US EIA
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3. Control over many time scales
Jeff Taft, Cisco
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4. Complex control architecture
Cisco
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5. Consumers lack incentives
Energy savings of 10% $10/month
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6. Utilities also lack incentives!
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7. Huge legacy infrastructure
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8. Storage is complex and expensive
No clear winner in terms of technologyNeed to balance energy and power
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Status
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Depressing…
A. Demand response – only time of use pricingB. Storage - tiny C. Smart buildings and homes – demo stageD. Microgrids – rareE. Electric vehicles – early stageF. Security and privacy – mostly missing
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Three inflection pointsSolarStorageSensing
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McKinsey, 2014
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Storage
Global investment to reach $122 Billion by 2021
Pike Research
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Storage alternatives
“Bytes”
“Bits/s”
Declining to $125/KWh
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Process storage
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Thermal storage
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Sensing
Michigan Micro Mote
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So what?
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GridSolar
ElectronsStorage
Transmission lineTransmission networkDistribution network
Demand response
Internet = Variable bit-rate source= Bits= Buffer= Communication link= Tier 1 ISP= Tier 2/3 ISP= Congestion control
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Equivalence theorem
Every trajectory on the LHS has an equivalent on the RHS
• can use teletraffic theory to study transformer sizing
O. Ardakanian, S. Keshav, and C. Rosenberg. On the Use of Teletraffic Theory in Power Distribution Systems, e-Energy ’12.
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Guidelines for transformer sizing
HydroOne’s guidelines
Prediction from teletraffic theory
O. Ardakanian, S. Keshav, and C. Rosenberg. On the Use of Teletraffic Theory in Power Distribution Systems, e-Energy ’12.
Can also quantify impact of storage
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O. Ardakanian, C. Rosenberg and, S. Keshav, “Distributed Control of Electric Vehicle Charging”. e-Energy ’13
“TCP” for EV charging1 EV = 5 homes
Creates hotspots
Real-time AIMD control of EV charging rate
Solution is both fair and efficient
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Stochastic network calculus
Joint work with Y. Ghiassi-Farrokhfal and C. Rosenberg
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“Rainbarrel” model
uncontrolledstochastic input
uncontrolledstochastic output
range What barrel size to avoidoverflow and underflow“with high probability”?
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Envelope idea
lower envelope ≤ Σinput ≤ upper envelope
lower envelope ≤ Σ output ≤ upper envelope
Envelopes are computed from a dataset of trajectories
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Stochastic envelopes
P((Σinput - lower envelope) > x) = ae-x
P((upper envelope –Σinput) > x) = be-x
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Stochastic network calculus
Equivalence
Wang, Kai, et al. "A stochastic power network calculus for integrating renewable energy sources into the power grid.” Selected Areas in Communications, IEEE Journal on 30.6 (2012): 1037-1048.
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Analytic results
Minimizing storage size to smooth solar/wind sources
Optimal participation of a solar farm in day-ahead energy markets
Modeling of imperfect storage devicesOptimal operation of diesel generators to deal with
power cuts in developing countries
Joint work with Y. Ghiassi-Farrokhfal, S. Singla, and C. Rosenberg
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Load reduction using extreme sensing
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Smart Personal Thermal Comfort
Fine-grained thermal control of individual offices
P.X. Gao and S. Keshav, Optimal Personal Comfort Management Using SPOT+, Proc. BuildSys Workshop, November 2013.P. X. Gao and S. Keshav, SPOT: A Smart Personalized Office Thermal Control System, Proc. ACM e-Energy 2013, May 2013.
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Our insight
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In a nutshell
Mathematical comfort modelWhen occupied, reduce comfort to the minimum acceptable
levelWhen vacant, turn heating offPre-heat Optimal model-predictive
control
6 7 8 9 10111213141516171820212223242526
Occupancy
Time of a day
Tem
pera
ture
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Extreme sensing
Microsoft Kinect
5° infrared sensor Servos
90° infrared sensor
Weatherduck sensorMicrocontroller
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Comparision of schemes
Discomfort
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Reflections on the research area
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Energy research
Pros Rapidly growing area Many open problems Industry interest and
support Motivated students Potential for impact
Cons Requires learning new
concepts and ideas Entrenched interests Difficult to obtain data Field trials nearly
impossible
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Many open research problems
Renewables integration Multi-level control Non-cash incentive design for consumers Energy efficiency policies for utilities Storage size minimization in energy systems Incentives for EV adoption Data mining of energy data sets Peak load reduction HCI for energy applications Data center energy minimization Microgrid/nanogrid design Building energy use monitoring and reduction
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Many publication venues
Conferences/workshops
ACM eEnergy IEEE SmartGridComm ACM Buildsys ACM GreenMetrics IEEE CCSES ACM Sustainability AAAI AAMAS
Journals
IEEE Trans. Smart Grid IEEE PES magazine Energy and Buildings J. Solar Power J. Power Sources Transportation
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ACM e-Energy 2014
Premier conference at the intersection of Internet technologies and energy systems
Sponsored by ACM SIGCOMM June 11-13, 2014 in Cambridge, UKEarly registration deadline is Wednesday!
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Conclusions
The grid has some real problemsSmart grid offers solutions, but it is still
early daysThree areas to watch out for
Solar Storage Sensing
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Acknowledgements
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ISS4E
Co-Director
Affiliated faculty
http://iss4e.ca
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ISS4E students
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