Information Systems and Science for Energyblizzard.cs.uwaterloo.ca/iss4e/wp-content/uploads/2013/12/iss4e... · Information Systems and Science for Energy ... Storage: the holy grail

Information Systems and Science for Energy

School of Computer Science Colloquium S. Keshav

October 17, 2013

IPCC Report: 27 Sep 2013

! Warming of the climate system is unequivocal

Gasoline combustion

1 liter of gasoline = 2.27kg of CO2

Electricity generation > Transportation!

IPCC 2007

Today’s Electrical Grid

! !"#"$%&'#(

! )$%#*+,**,'#(

! -,*.$,/0&'#(((

!"

3 components

Howstuffworks.com

Grid characteristics

((

#"

1. Overprovisioned by design

capacity

2. Inefficient

5% better efficiency of US grid

= zero emission from 53 million cars

http://www.oe.energy.gov/

3. Aging

Post-war infrastructure is reaching EOL

4. Uneven

TWh generated

! China 4,938

! US 4,256

Daily W/capita (2012 est.)

395

1402

Wikipedia

5. Poorly measured

6. Poorly controlled

!  Electrons are not addressible

7. Ridiculously cheap

Wikipedia

Technology is changing the grid

1. Wind

“Revolution Now,” US DOE Sept. 17. 2013

WIND POWER CENTS/KILOWATT-HOUR

INSTALLED CAPACITY (GW)

2. Solar

“Revolution Now,” US DOE Sept. 17. 2013

Module cost $/W Deployment MW

The future is here now!

! Wind and solar produced 60% of Germany’s electricity on October 3

Image: BCCONSULT

60%

3. Storage: the holy grail

! Global investment in energy storage technologies to reach $122 Billion by 2021: Pike Research

! Many new technologies !  Batteries

!  Flywheels

!  Supercapacitors

!  Compressed air storage

…

4. Electric vehicles

!  Spur research on lower-cost storage

!  Huge consumers of electricity

Nissan Leaf chassis

5. Pervasive sensing

Michigan Micro Mote

6. Pervasive computation

Freescale KL02 microcontroller 1.9 mm x 2.0 mm

7. Pervasive communication

Trilliant Corp.

8. Pervasive control

Control 4’s EMS-100 in-home display

Current grid

!  Decentralized

!  Storage rich

!  Renewables/low carbon

!  Sensing rich

!  Control rich

!  Flexible

!  Energy frugal

Centralized

Little to no storage

High carbon

Poorly measured

Poorly controlled

Ossified

Inefficient

“Smart” grid

OK, we’re done, right?

Maybe not…

1. Matching demand and supply

Barnhart et al, Proc. Energy and Environment, 6:2804, 2013

2. Storage is expensive

! Buying 1 KWh = 10c ! Storing 1 KWh = ~$450!

3. Controlling distributed generators

US EIA

4. Control over many time scales

Jeff Taft, Cisco

5. Communication is complex

Wid

e A

rea

Mon

itorin

g an

d C

ontro

l Sys

tem

(WA

MC

S) N

etw

orks

Conventional Wind Solar Storage

Internet

Distributed Energy Resources (DER)

(large scale)

Converged Voice, Data, and Video

Area ControlError (ACE)

Control

Virtual Power Plant (VPP)/Demand Response (DR)

Application

Reference Model

Trans-Regional Balancing Org.

Interchange Authority/Reliability Coordinator Grid Direct Current (DC) Inter-Ties Other Regions

Other Energy Traders

Utility EnergyTrading

UtilityNOC

• Customers• Suppliers• Retail Energy Providers (REP)• Third Parties

Third PartyStabilization

Services

Other TransmissionSystem Operators (TSO)/

Distribution System Operators (DSO) and Verticals

IndependentPower

Producers(IPP)

Other Region Phasor Measurement Unit

(PMU) Data

Other TSO PMU Data

Web Portals

Balancing Authority

VPP/DRApplication

ACEControl

Other Balancing Authorities

Trans-Regional Energy Markets

Wholesale Energy Markets

Trans-Regional/Trans-National TierTrans-Regional Networks

System Control TierInter-Substation Networks

Interchange TierInterchange Networks

Balancing TierControl Area Networks

Synchronous Grid Inter-Tie Control

WAMCS Network Operations Center (NOC)/

Data Center

Inter-Control Center NetworksEnterprise Networks Utility Tier

Intra-Control/Data Center Tier

AncillaryServices

VPP/DR Data/Signals

Control CenterNetwork

FieldDispatch

CallCenter

Control Center Control CenterControl CenterApplicationsand Users

EnterpriseApplicationsand Users

Control CenterNetwork

Data CenterData Center

Network

Transmission PMU Data

Substation Tier

D Level 1 Tier

D Level 2 Tier

Transmission Substation

Intra-Substation Networks

Sensor Networks

PhasorMeasurement

Unit (PMU)

Distribution Substation

Intra-Substation Networks

Sub-Transmission Substation

Intra-Substation Networks

Traction Substation

Intra-Substation Networks

Urban FANs Rural FANs

Sensor Networks

Electric Vehicle (EV)Sub-networks

Prosumer Tier

Third Party Aggregator of DR, Distributed

Generation (DG), etc.

FeederD-PMU

Nets

Sensors

Controls

Home Energy Controller

Controls

Displays

ChargerMeter

Inverter Control

Protection and Control

Inverter Control

Distribution PMU

Feeder

DistributeResourc

(large

Streetlight Systems

Line Sensor

Volt/ VAR Regulation

Fault Isolation and Service Restoration Nets

Sensor Subsystems

Inverter Control

DA Devices

DER Protection

Distribution Automation (DA) Sub-networks

Gateway

Transmission LevelStabilization

© 2011 Cisco and the Cisco Logo are trademarks of Cisco and/or its a!liates in the U.S. and other countries. A listing of Cisco’s trademarks can be found at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The use of the word partner does not imply a partnership relationship between Cisco and any other company. (1009R) C82-676734 6/11

Distribution LevelStabilization

(DSTATCOM)

Neighborhood Area Networks (NAN) (Residential, Commercial and Industrial)

Residential Networks Building Networks Private Microgrid Networks In-Vehicle Networks

In

D

Cisco

6. Consumers have no incentive to save

!  Energy savings of 10% !  $10/month

7. Utilities have no incentive to save!

ISS4E

Mission

To use information systems and science to !  increase the efficiency !  reduce the carbon footprint

of energy systems

3 Approaches

1.  Exploiting equivalency of grid and Internet*

2.  Problem-oriented research

3.  Data-driven research*

* novel

1. Grid = Internet

S. Keshav and C. Rosenberg, How Internet Concepts and Technologies Can Help Green and Smarten the Electrical Grid, CCR, January 2011.

Grid

Electrons

Load

Communication link

Battery/energy store

Demand response

Transmission network

Distribution network

Stochastic generator

Internet

= Bits

= Source

= Transmission line

= Buffer

= Congestion control

= Tier 1 ISP

= Tier 2/3 ISP

= Variable bit rate source

Equivalence theorem

Server(C)

L1

Ln

Buffer (B)

Storage(B)

L1

Ln

.

.

.

Transformer (C)

PCS

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.

A. 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

!"#$%&'("%)'*+,'-.+"/%0123"*+'12%012+,1$%41,%5$"6+,'6#$%7"8'6$"%08#,3'23%

9(#,+%:,'/%;,68'+"6+.,"%%

<%

O. Ardakanian, C. Rosenberg and, S. Keshav, “Distributed Control of Electric Vehicle Charging”. e-Energy ’13

B. TCP for EV charging

!  1 EV = 5 homes !  Creates hotspots

!  Real-time AIMD control of EV charging rate

!  Solution is both fair and efficient

C. Matching supply and demand

“Rainbarrel” model

uncontrolled stochastic input

uncontrolled stochastic output

range What barrel size to avoid overflow and underflow “with high probability”?

Envelope idea

lower envelope " Σinput " upper envelope

lower envelope " Σ output " upper envelope

Envelopes are computed from a dataset of trajectories

Stochastic envelopes

P((Σinput - lower envelope) > x) = ae-x

P((upper envelope –Σinput) > x) = be-x

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.

Analytic results

! Minimizing storage size to smooth solar/wind sources

! Optimal participation of a solar or wind farm in day-ahead energy markets*

! Modeling of imperfect storage devices*

! Optimal operation of diesel generators to deal with power cuts in developing countries*

Joint work with Y. Ghiassi-Farrokhfal, S. Singla, and C. Rosenberg

2. Problem-oriented research

Problem formulation

Modeling

Analysis

Design

Implementation

Evaluation !  Data

Increase efficiency, reduce carbon footprint

!  Optimization !  Queueing theory !  Network calculus !  Control theory !  Network flow !  Algorithms

!  New technologies !  Storage !  EVs !  Pervasive computation,

sensing !  Renewable generation

Smart Personal Thermal Comfort

Reduce building energy use with fine-grained thermal control of individual offices

P.X. Gao and S. Keshav, Optimal Personal Comfort Management Using SPOT+, Proc. BuildSys Workshop, 2013. P. X. Gao and S. Keshav, SPOT: A Smart Personalized Office Thermal Control System, Proc. ACM e-Energy 2013, May 2013.

Our insight

In a nutshell

!  Mathematical comfort model

! When occupied, reduce comfort to the minimum acceptable level

! When vacant, turn heating off

!  Pre-heat

! Optimal model-predictive control

20 21 22 23 24 25 26

6 7 8 9 10 11 12 13 14 15 16 17 18

Tem

pe

ratu

re

Time of a day

Occupancy

New technology: sensing

Microsoft Kinect

51(,#2$%$"3(*"#*'$ Servos

901(,#2$%$"3(*"#*'$

Weatherduck *"#*'$ Microcontroller

Evaluation: clothing level estimation

!  Root mean square error (RMSE) = 0.0918

!  Linear correlation = 0.92

New technology: data mining for occupancy prediction

!  Predict occupancy using historical data

Match previous similar history

Predict using matched records

0 .3 1 1 1 .3

Comparision of schemes

!

"

#!

#"

$!

$"

!%& !%" !%' !%( !%) !%*

!"#$%&#'(%

)*+',-#

$&+'./012'

!"#$%&#'!345*67#'889'

+,-./012./34./5.617,8.9:.;<5.617,8.9==>?<7,;64

Discomfort

3. Data-driven research

Obtain dataset

Problem formulation

Analysis

Insights

Difficult!

!  Data mining !  Machine learning !  Big data analytics !  What-if analysis !  Simulation

!  Effect of new technologies !  Storage !  EVs !  Pervasive computation !  Renewable generation

Problem

peak

average

What we’d like

peak

average

New technology

!  ~5 million ‘smart meters’ installed in Ontario

Ontario Time-Of-Use (TOU) Scheme

peak mid-peak off-peak

Questions

!  Is 2 the right number of seasons?

!  Should they be 6 months long?

! Do they start at the right times?

! Do peak times correspond to load peaks?

! Does the scheme reduce peak loads?

A. Adepetu, E. Rezaei, D. Lizotte, and S. Keshav!Critiquing Time-Of-Use Pricing in Ontario, IEEE SmartGridComm Symposium 2013, Vancouver, October 2013.

Dataset

! Aggregate hourly energy use in Ontario for the last 10 years

Aggregate Ontario load for the first two weeks of May over 4 years

Answers

!  Is 2 the right number of seasons? No

!  Should they be 6 months long? No

! Do they start at the right times? No

! Do peak times correspond to load peaks? No

! Does the scheme reduce peak loads? No!

A. Adepetu, E. Rezaei, D. Lizotte, and S. Keshav!Critiquing Time-Of-Use Pricing in Ontario, IEEE SmartGridComm Symposium 2013, Vancouver, October 2013.

A better scheme

Summer

Fall

Winter

Spring

4 seasons

A. Adepetu, E. Rezaei, D. Lizotte, and S. Keshav!Critiquing Time-Of-Use Pricing in Ontario, IEEE SmartGridComm Symposium 2013, Vancouver, October 2013.

Other datasets

!  Hourly electrical load from 26,000 homes in Ontario

!  Hourly electrical load from 5,000 homes in Ireland

!  Hourly water usage from 27,000 homes in Abbotsford

!  Per-appliance energy use for ~500 appliances

!  50 commercial buildings’ energy use over 2-4 years (Pulse)

!  500 taxi driving records for 1 month (Cabspotting)

!  7 years of car rental records (Grand River Carshare)

!  Hourly electricity prices and use for 10 years (IESO)

!  Solar and wind production over 10 years (NREL)

!  Weather records (Ontario)

Reflections on the research area

Energy research

Pros !  Rapidly growing research

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

Many open research problems

!  Renewable 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

Join us!

!  ISS4E mailing list

!  ISS4E seminar series starting October 2013 !  next speaker: Pascal van Hentenryck, 4pm on

Oct. 24th

! Open weekly group meetings, 10:30-11:30 on Wednesdays in DC 1331

Conclusions

!  Technology is changing the grid

! Computer Science has a role to play

! Opportunity for interesting, impactful research

Acknowledgements

ISS4E Faculty

Co-Director

Affiliated faculty

Co-authors

ISS4E students

WISE

!  Prof. Jatin Nathwani (Executive Director)

!  Tracey Forrest (Director)

!  Prof. Claudio Canizares (ECE)

!  Prof. Kankar Bhattacharya (ECE)

Funding agencies

Corporate sponsors

Our next project

Backup slides

What kills birds?

! According to Environment Canada !  Wind turbines: 16,700

!  High-rise buildings: 64,000

!  Single family homes and low-rises: 22,000,000

!  Cats: 196,000,000

http://metronews.ca/news/canada/811474/cats-blamed-for-70-of-bird-deaths-in-canada-study/

Impacts

!  Physical !  Extreme weather

!  Ocean acidification

!  Sea level rise

!  Ocean currents

!  Social !  Food supply

!  Water availability

!  Health

!  Ecological !  Species loss

!  Migration

Wikipedia

Carbon emitters

Wikipedia

Top emitters

Wikipedia

Per capita carbon emissions

WorldBank

Renewable energy

Pervasive communication

Trilliant Corp.

Storage

“Bytes”

“Bits/s”

Solar SSPE

!  Model stochastic sources (solar, wind) and stochastic loads using Markov models or stochastic sample path envelopes

1 2 3 4 5 6 7 80

1000

2000

3000

4000

5000

6000

7000

8000

time (hour)

I g(t) (W

h/m

2 )

sample pathProbabsilitic envelopeMean rate envelopeBound on samples

Pervasive communication

Carbon from gasoline combustion

!  2C8H18 + 25O2 -> 16CO2 + 18H20 C9H20 + 14O2 -> 9CO2 + 10H20 2C10H22 + 31O2 -> 20CO2 + 22H20 C11H24 + 17O2 -> 11CO2 + 12H20 2C6H6 + 15O2 -> 12CO2 + 6H20 C7H8 + 9O2 -> 7CO2 + 4H20 4C8H7 + 39O2 -> 32CO2 + 7H20 2C8H10 + 21O2 -> 16CO2 + 10H2O

!  1 liter of gasoline (~800g) produces 2.27kg of CO2

Ontario Time-Of-Use (TOU) Scheme 94

Publication venues Conferences and

workshops !  eEnergy

!  SmartGridComm

!  Buildsys

!  GreenMetrics

!  ACM Sustainability

!  AAAI: Sustainability track

!  AAMAS

Journals

!  IEEE Trans. Smart Grid

!  IEEE PES magazine

!  Energy and Buildings

!  J. Solar Power

!  J. Power Sources

!  Transportation