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Sustainability?

Sustainable development should meet theneeds of the present without compromisingthe ability of future generations to meet theirown needs

Our Common Future, World Commission on Environmentand Development, United Nations, 1987

2 11/7/2012

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My Goal today

Suggest ways SenSys community is and canbe more engaged in achieving sustainableenergy networks

11/7/20123

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Quantifying Sustainability California Law

AB 32 Reduce GHG emissions to 1990 levels by 2020

Governors executive order S-3-05 (2005) 80% reduction below 1990 levels by 2050

Renewable Portfolio Standard 33% renewables by 2020, 20% biopower procurement

480 => 80 mmT CO2e in 40 years Population: 37 => 55 million Economic growth

11/7/20124

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CA2050: GHG 90% below 1990

5 11/7/2012

But,

0

200

400

600

800

1,000

1990 2005 2020 2050

Historical BAU

G H G E m i s s i o n s ( M t C O

2 e / y r )

Energy

emissionsNon-energyemissions2020 Target

2050 Target

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CA2050 4 Part GHG Reduction Plan

Efficiency

6 11/7/2012

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Efficiency Electrify

7 11/7/2012


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Efficiency Electrify Decarbonize

the electricity Decarbonize

the fuel

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Efficiency Electrify Decarbonize


the fuel

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All required for even 60% reduction

but still fall short of 80%11/7/201210

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Scenarios

11 11/7/2012

New Nuclear plantevery 14 monthsfor 40 years

New CCS facilityevery 9 mo.Exceeds saline

aquifer

Resources exist- 1.4 % of CA land- 43% agriculture- 3.4% urban

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The Problem: Supply-Demand Match

12

Baseline + Dispatchable Tiers Oblivious Loads

TransmissionGeneration DemandDistribution

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An Engineering Marvel NA: 3 synchd regions Lots of wires

170k miles of >200 kvtransmission

6m miles of distribution

3k miles of 500 kv DC 3,200 retail

distributors 147 M customers

125M res, 17.6 M com,0.78 M ind.

10 ISO/RTO cover 2/3 Little communication

11/7/201213http://www.nerc.com/docs/oc/rs/BA_BubbleDiagram_2011-10-03.jpg

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Towards an Aware Energy Network

14

Baseline + Dispatchable Tiers Oblivious Loads

Communication

Non-DispatchableSources

Communication

Aware InteractiveLoads

TransmissionGeneration DemandDistribution

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Classic al view of the Energy Challenge

Supply Transport Load

Generators Lines

TransformersMeters

VFDEfficiency

Control

Baseline

Intermittent

Peaker

Nuclear,Coal

HydroGeothermal

Combined cyclenat. gas

Single cyclenat. gas

T r a n s m

i s s i o n

D i s t r i b u t i o n

Imports

Motors

Lighting

HVAC

Electronics

Appliance

Power Supplies

Markets

Circuits

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Modern Energy Network Challenges


Generators Lines

TransformersMeters

VFDEfficiency

Control

Baseline

Intermittent

Peaker

FluctuatingRenewable

Nuclear ,Coal

HydroGeothermal

Combined cyclenat. gas / bio

Single cyclenat. gas /

bio

PhotovoltaicWind

T r a n s m

i s s i o n


Imports

Motors

Lighting

HVAC

Electronics

Appliance

Power Supplies

Storage

Consumption

Usage

Schedule

Pricing

Facilities

Personal

Industrial

Transportation

Pumped

Information Plane

Physical Plane


Storage Storage

Circui ts

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Sensys? Creating the info plane

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Smart Meters the WSN Killer App?


Generators Lines

TransformersMeters

VFDEfficiency

Control

Baseline

Intermittent

Peaker

Nuclear,Coal

HydroGeothermal

Combined cyclenat. gas

Single cyclenat. gas

T r a n s m

i s s i o n


Imports

Motors

Lighting

HVAC

Electronics

Appliance

Power Supplies

Markets

Circuits

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Smart meter rollouts

11/7/201219http://www.edisonfoundation.net/iee/Documents/IEE_SmartMeterRollouts_0512.pdf

Proprietary / Zigbee

Open IPv6,

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Much more than meters and billing

11/7/201220

http://www.openthegrid.com/docs/ipv6_in_smart_grid_field_area.pdf

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The Mote/TinyOS revolution

SmartDustWeC Rene

Intelrene

9998 00 01 0302 04 0605 07

S E N S I T

E x p e d

i t i o n

N E S T

N E T S /

N O S S

C E N S

S T C

N S F

C y b e r -

P h y s i c a

l

Mica

Inte l/UCBdot

InteliMOTE

XBOWcc-dot

XBOWmica2

XBOWrene2

Intelcf-mica

Boschcc-mica

Dust Incblue cc-TI

digital sunrain-mica

XBOWmica

zeevo BT

TelosXBOWmicaZ

Intel

MOTE2EyesBTNod

etrio

8 kBrom kBram

48 kB rom10 kB ram802.15.4

L o W

P A N / I P v 6

Epic

11

I E T F R P L

10

11/7/2012

Mote inside uP => Arm Cortex Radio => 802.15.4gnarrow=bandfrequency hopper TinyOS too SOC from here

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23

Idle Listening: 3 Basic Solutions Scheduled Listening

Arrange a schedule of communication Time Slots Maintain coordinated clocks and schedule

Listen during specic slots Many variants:

Aloha, Token-Ring, TDMA, Beacons, Bluetooth piconets, S-MAC, T-MAC, PEDAMACS, TSMP, FPS,

Sampled Listening Listen for very short intervals to detect eminent

transmissions On detection, listen actively to receive DARPA packet radio, LPL, BMAC, XMAC, Maintain always on illusion, Robust

Listen after send (with powered infrastructure) After transmit to a receptive device, listen for a short time Many variants: 802.11 AMAT, Key fobs, remote modems,

Many hybrids possible

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24

802.5Token Ring

Internet WSN assimilated

802.3Ethernet

802.11WiFi802.3a

Ethernet10b2802.3i

Ethernet10bT802.3y

Ethernet100bT802.3ab Ethernet1000bT

802.3an Ethernet

1G bT

802.11aWiFi802.11b

WiFi802.11g

WiFi802.11n

WiFi

X3T9.5FDDI

SerialModem

GPRS

ISDNDSL

Sonet

Transport (UDP/IP, TCP/IP)

Application (Telnet, FTP, SMTP, SNMP, HTTP)

Diverse Object and Data Models (HTML, XML, , BacNet, )

802.15.4LoWPAN

Network (IP)Link2: link

3: net

4: xport

7: app

1: phy

11/7/2012

802.15.4e

802.15.4gP1901.2

6LoWPAN RFC6282

RPL RFC6550

COAP

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Or in industry speak

11/7/201225

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At incredible scale

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ROLL & Sensys Role RPL retained fundamental routing diversity

Multiple DODAGs with selection Incorporated trickle density awareness

Many questions for which the research was simply

not there Piece-wise source routing, routing stretch versus protocol

complexity & state, routing metrics, Many issues for which the analysis is seriously

incomplete Local / global repair, loop formation, routing staleness,

scaling, state management, trickle timers IETF, mobile, and industry rediscovering Where is the research community?

11/7/201227

k

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Creating an Energy Network ???

Supply Transport ConsumptionLoad

Generators Lines

TransformersMeters

VFDEfficiency

Usage

ScheduleControl

Pricing

Baseline

Intermittent

Peaker


Nuclear ,Coal

HydroGeothermal



bio

PhotovoltaicWind

T r a n s m

i s s i o n


Imports

Motors

Lighting

HVAC

Electronics

ApplianceFacilities

Personal

Industrial

Transportation

Power Supplies


Storage Storage Storage Pumped

Information Plane

Physical PlaneCircuits

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Grid Exists

30

Conventional Electric Grid

GenerationTransmission

DistributionLoad

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Internet Exists

31

Conventional Electric Grid


DistributionLoad

Conventional Internet

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Wh S ?

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Where to Start?

Buildings 72% of electrical consumption (US), 40-50% of total consumption, 42% of GHG footprint US commercial building consumption

doubled 1980-2000, 1.5x more by 2025[NREL]

Where Coal is used

Prime target of opportunity forrenewable supplies

33

Renewable energy consumption

Electricity source

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Building Power Consumption

11%

1MW 883 kW

11/7/201234

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P

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11/7/201237

Power-Proportional

Buildings ? 1.45 MW 2.02 MW

Min = 72% of Max

Stanley Hall:Office + BioScience

- 13 NMRs

P

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11/7/201238

Power-Proportional

Buildings ? 756 KW 1030 KW

Min = 69% of Max

Koshland Hall:Office + ???

P

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11/7/201239

Power-Proportional

Buildings ?62 KW

202 KW

Min = 31% of Max

LeConte Hall: Office

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Power Proportionality

40

Productivity

C o n s u m p

t i o n

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Power Proportionality

41

Productivity

C o n s u m p

t i o n

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Doing Nothing Well ???

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Power Proportional Buildings?

43

50 Ton Chiller 200 Ton Chiller

10 months 2 months

Scott McNally Bldg Manager 11/7/2012

The Building Challenge

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The Building Challenge

44

CT: mains

powermonitoringPanel 1 Panel 2

A

B

A

B

Panel 1 Panel 2

A

B

A

B

1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

A1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

A2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

B2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

B

1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

A1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

1

5

9

13

17

21

25

29

33

37

41

3

7

11

15

19

23

27

31

35

39

A2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

B2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

2

6

10

14

18

22

26

30

34

38

42

4

8

12

16

20

24

28

32

36

40

B

panel level powermonitoring

ACme: plug loadenergy monitor and

controller

Temperature

Humidity

Vibra;on

Pressure

E le c t r ic L o ad T r e e C lim a t e P la n t

Operations and Environment

11/7/2012

CEC B2G T tb d A E g

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CEC B2G Testbed - An EnergyTransparent Building

11/7/201245

Whole BldgMCL equip

MCL infra

MCL vac

servers

DOP HVAC

Central vent

office HVAC

inst Lab 199HVAC

Plug loads

Lighting

Parking Lot

The Other Energy Usage

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The Other Energy Usage

11/7/201246

Bldg 90

Wireless plug meters

on 611 of 1200 loads

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sMAP simple Monitoring and Action Protocol Uniform Access to Diverse Physical Information

11/7/201247

Electrical

Weather

GeographicalWater

EnvironmentalStructuralActuator

Occupancy

sMAP

Modeling

Visualization

ContinuousCommissioning

Control

PersonalFeedback

DebuggingStorageLocation

Authentication

Actuation

A p p

l i c a t

i o n s

P h y s

i c a l

I n f o r m a t

i o n

REST API

HTTP/TCP

JSON Objects

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Internet

Cell phonesMAP Gateway

sMAP

Modbus RS-485

sMAP

sMAP Gateway

EBHTTP / IPv6 / 6LowPANWireless Mesh Network

sMAP

sMAP

sMAP

sMAP

Edge Router

Temperature/PAR/TSR

Vibra;on / Humidity

AC plug meter

Light switch

D e n t c i r c u i t m e t e r

P r o x y S e r v e r

E B H T T P

T r a n s l a ; o n

California ISO sMAP Gateway

sMAP Resources Applica;ons

Google PowerMeter

Weather

sMAP

Every Building

Database

sMAP Virtual Energy-Lab Ecosystem

11/7/201248

Factoring is critical

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Factoring is critical

Archiver

RDBMS TSDB

sM

AP

sMAP

sMAP

c on

t r ol

w e b

6lowpannetworks

RS-458 bus

BacNET/IP

m o d el s

m g

m t

Public interfaces

Application-specificfunctionality built onexposed interfaces

Provide access toarchived data

Manage views, datacleaning

Represent, transmit dataand metadata

Abstract underlyingheterogeneity into simpledata model

11/7/201249

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sMAP is Universal information representation for physical

data Self-describing, compact JSON schema, transportable

over UDP/TCP Integrated metadata

Software Architecture for physical data processingand actuation Real-time and archival data, time-series DB Adapters/Drivers for legacy and direct streams Subscription, syndication, distillates Query processing, visualization interface

Resource-oriented web-service framework forembedded applications

50 11/7/2012

http://code.google.com/p/smap-data

All ph sic l info

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Name Sensor Type Access Method Channels

ISO Data CAISO, NYISO, PJM, MISO, ERCOT, BPA Web scrape 1211ACme devices Plug-load electric meter Wireless 6lowpan mesh 344

EECS submetering project Dent Instruments PowerScout 18 electric meters Modbus 4644

EECS steam and condensate Cadillac condensate; Central Station steam meter Modbus/TCP 13

UC Berkeley submetering feeds ION 6200, Obvius Aquisuite; PSL pQube,VerisIndustries E30

Mosbus/Ethernet, HTTP 4269

Sutardja Dai, Brower Hall BMS SiemensApogee BMS, Legrand WattStopper,Johnson Control BMS

BACnet/IP 4064

UC Davis submetering feeds Misc., Schneider Electric ION OPC-DA 34 (+)

Weather feeds Vaisala WXT520 rooftop weather station;Wunderground

SDI-12, LabJack/Modbus, webscrape

33

CBE PMP toolkit Dust motes; NewYork Times BMS CSV import; serial 874

NOA Weather Forecast Meteorological (window, solar, cloud, etc) Web 77740

SDH Air Quality CO2, Temp, TSR, PAR, Hum Wireless 6lowpan mesh 50

total: 93,242

All physical info

11/7/201251

www.openbms.org

Li i g L b A h I t i

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Living Lab Approach: Innovate in aVirtual Private Grid


DistributionLoad

VPG

11/7/201252

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sMAP Energy Markets, Ops, $,

11/7/201253

http://www.isorto.org

BPA

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sMAP Solar, Wind, Meteorology

1450 Met feeds covering Cal Solar Initiative: 130,275 proj. 1370 MW

Cal Utility Solar: 60 facilities 695 MW Cal Utility Wind: 134 facilities 4295 MW

11/7/201254

MAP i

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sMAP generation CA generation plant locations, type, and

rated power (> 0.1 MW) [CEC] Hourly output from each type of CA

generation source for > 1 year [CAISO]

[CEC] http://energyalmanac.ca.gov/powerplants/Power_Plants.xls

[CAISO] http://www.caiso.com/green/renewableswatch.html11/7/201255


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Generators Lines

TransformersMeters

VFDEfficiency

Usage

ScheduleControl

Pricing

Baseline

Intermittent

Peaker


Nuclear ,Coal

HydroGeothermal



bio

PhotovoltaicWind

T r a n s m

i s s i o n


Imports

Motors

Lighting

HVAC

Electronics

ApplianceFacilities

Personal

Industrial

Transportation

Power Supplies



Information Plane


11/7/201256

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What would the CA Grid be like @

60% renewables?

11/7/201257

Bloomberg New Energy Finance, Liebreich April 5, 2011

G-20Investment

($B)

Whos Winning the Clean Energy Race? 2010 Pew Charitable Trust www.PewEnvironment.org/CleanEnergy

CA g id t d S li

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CA grid today - SuppliesSource Rated

(GW)

Capacity

Factor1

Total Energy

(TWh)

% of Total

EnergyGeothermal 2.600 38.7% 8.68 3.8%

Biomass/Biogas 1.145 43.5% 4.30

1.9%

Small Hydro 1.380 31.7% 3.77 1.7%

Wind 2.812 29.1% 7.06 3.1%

Solar 3 0.403 28.7% 1.00 0.4%

Nuclear 4.456 85.9% 33.00 14.6%

Hydro 12.574 27.7% 30.05 13.3%

Imports 11.055 2 66.6% 63.43 28.0%Thermal 44.339 19.7% 75.43 33.3%

Total 80.764 32.6% 226.71 100.0%1 Mean delivered power divided by rated power (excl. import)2 For imports, rating is the maximum observed power 3 Residential net factored into demand11/7/201258

CA g id t d S l Ch ll g

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CA grid today Supply Challenge

11/7/201259

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CA grid today Supplies

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CA grid today - SuppliesSource Rated

(GW)

Capacity

Factor1

Total Energy

(TWh)

% of Total

EnergyGeothermal 2.600 38.7% 8.68 3.8%

Biomass/Biogas 1.145 43.5% 4.30

1.9%

Small Hydro 1.380 31.7% 3.77 1.7%

Wind 2.812 29.1% 7.06 3.1%

Solar 3 0.403 28.7% 1.00 0.4%

Nuclear 4.456 85.9% 33.00 14.6%

Hydro 12.574 27.7% 30.05 13.3%

Imports 11.0552

66.6% 63.43 28.0%Thermal 44.339 19.7% 75.43 33.3%

Total 80.764 32.6% 226.71 100.0%1 Mean delivered power divided by rated power (excl. import)2 For imports, rating is the maximum observed power 3 Residential net factored into demand11/7/201261

A i h d id

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A year in the todays grid

11/7/201262

Seasonal, Weekly, DailyvariationsMany underlying factors

Peak: 47.1 GWMin: 18.8 GWMean: 26.3 GW

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A mid summers week

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A mid-summer s week

11/7/201264Sep Nov Jan Mar May Jul

0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

IMPORTS: 28.0%THERMAL: 33.3%WIND: 3.1%SOLAR: 0.4%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

07/08 07/09 07/10 07/11 07/12 07/130

5

10

15

20

25

30

35

40

45

T o t a

l C A P o w e r

( G W )

IMPORTS: 29.9%THERMAL: 26.2%

WIND: 5.0%SOLAR: 0.6%

BIOMASS/BIOGAS: 1.8%GEOTHERMAL: 3.2%

HYDRO: 15.9%SMALL HYDRO: 1.9%NUCLEAR: 15.4%CA GRID DEMAND

A winter weeks tale

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12/19 12/20 12/21 12/22 12/23 12/24

0

5

10

15

20

25

30

35

40

45

T o

t a l C A P o w e r

( G W )

IMPORTS: 24.5%

THERMAL: 38.5%

WIND: 1.8%

SOLAR: 0.1%

BIOMASS/BIOGAS: 1.9%

GEOTHERMAL: 4.0%HYDRO: 14.8%

SMALL HYDRO: 1.4%

NUCLEAR: 13.1%

CA GRID DEMAND

A winter week s tale

11/7/201265Sep Nov Jan Mar May Jul

0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

IMPORTS: 28.0%THERMAL: 33.3%WIND: 3.1%SOLAR: 0.4%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

Th D d D ti C

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The Demand Duration Curve

11/7/201266

A Si l h t if

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A Simple what if Take current demand, current activity, current

technology, current deployment At a crude top-level scale (by category)

Represented by the time series Scale up the renewable portions

Preserve the seasonal, weekly, daily, hourly effectsof mother nature *

Scale back the fossil fuel based supplies With current demand as a reference

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E ample: Solar

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Example: Solar

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Example: Solar Scaled

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Example: Solar Scaled

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Example: Wind

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Example: Wind

11/7/201270

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A Year in CA grid @ 60%

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A Year in CA grid @ 60%

11/7/201273

Sep Nov Jan Mar May Jul0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

EXCESSIMPORTS: 2.6%

THERMAL: 9.7%WIND: 34.4%SOLAR: 18.2%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

A Summer Week @ 60%

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A Summer Week @ 60%

11/7/201274 Se Nov Jan Mar Ma Jul0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

EXCESSIMPORTS: 2.6%THERMAL: 9.7%WIND: 34.4%SOLAR: 18.2%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

07/08 07/09 07/10 07/11 07/120

10

20

30

40

50

60

70

80

T o t a

l C A P o w e r

( G W )

EXCESS

IMPORTS: 0.0%

THERMAL: 0.0%

WIND: 41.6%

SOLAR: 20.1%


GEOTHERMAL: 3.2%

HYDRO: 15.9%

SMALL HYDRO: 1.9%

NUCLEAR: 15.4%

CA GRID DEMAND

A Winter Week @ 60%

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A Winter Week @ 60%

11/7/201275 Se Nov Jan Mar Ma Jul0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

EXCESSIMPORTS: 2.6%THERMAL: 9.7%WIND: 34.4%SOLAR: 18.2%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

12/19 12/20 12/21 12/22 12/230

10

20

30

40

50

60

70

80

T o

t a l C A

P o w e r

( G W )

EXCESS

IMPORTS: 1.8%

THERMAL: 22.9%

WIND: 33.4%

SOLAR: 6.8%BIOMASS/BIOGAS: 1.9%

GEOTHERMAL: 4.0%

HYDRO: 14.8%

SMALL HYDRO: 1.4%

NUCLEAR: 13.1%

CA GRID DEMAND

CA Grid @ 60%

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CA Grid @ 60%

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What Can we do to Make it Work?

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What Can we do to Make it Work? Design for deep penetration

Optimize for the whole, not peak production Use your off-grid intuition

Efficiency for shaping Poor power proportionality of buildings and other loads,

especially at night Storage

Move energy in time Load scheduling (continuous DR)

Precooling, preheating, guardband adjustment

Deferral, acceleration Integrated Portfolio Management Utilize resources in concert with non-dispatcables

Curtailment

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Load shifting to follow supply

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Load shifting to follow supply

11/7/201278

Sep Nov Jan Mar May Jul0

10

20

30

40

50

60

70

T o

t a l C A P o w e r

( G W )

EXCESSIMPORTS: 2.6%

THERMAL: 9.7%WIND: 34.4%SOLAR: 18.2%BIOMASS/BIOGAS: 1.9%GEOTHERMAL: 3.8%HYDRO: 13.3%SMALL HYDRO: 1.7%NUCLEAR: 14.5%CA GRID DEMAND

Windrush?Sunrush?- energy agile

industry?

A Day

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A Day

00:00 06:00 12:00 18:000

5

10

15

20

25

30

35

T o

t a l C A P o w e r

( G W )

EXCESS

IMPORTS: 1.0%

THERMAL: 21.2%

WIND: 33.9%

SOLAR: 15.8%


GEOTHERMAL: 4.2%

HYDRO: 10.8%

SMALL HYDRO: 1.3%

NUCLEAR: 9.8%

CA GRID DEMAND

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The Day with +/- 3 hours of shift

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The Day with +/- 3 hours of shift

00:00 06:00 12:00 18:000

5

10

15

20

25

30

35

T o

t a l C A P o w e r

( G W )

EXCESS

IMPORTS: 0.6%THERMAL: 16.4%

WIND: 35.7%

SOLAR: 19.1%


GEOTHERMAL: 4.2%

HYDRO: 10.8%

SMALL HYDRO: 1.3%

NUCLEAR: 9.8%

CA GRID DEMAND

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How to match demand to supply?

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How to match demand to supply?

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Modern EE & CS Energy Challenges

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gy g


Generators Lines

TransformersMeters

VFDEfficiency

Usage

ScheduleControl

Pricing

Baseline

Intermittent

Peaker


Nuclear ,Coal

HydroGeothermal



bio

PhotovoltaicWind

T r a n s m

i s s i o n


Imports

Motors

Lighting

HVAC

Electronics

ApplianceFacilities

Personal

Industrial

Transportation

Power Supplies



Information Plane


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Interesting Sensor Networks

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g151 Temperature Sensors

50 Electrical Sub-meters

12 Variable Speed Fans

138 Air Dampers

312 Light Relays

6 Variable Speed Pumps

121 Controllable Valves

> 6 , 0 0 0 S e n s e a n d

C o n t r o l P o i n t s

Sutardja Dai HallBuilt in 2009

140k sq. ft.11/7/201283

Controls are Widely Available

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Controls are Widely Available

Bancroft Library: Built in 1949 100k sq. ft.

5,000 points

>70% of large buildings have digital controls

U.S. Energy Information Administration, 2009 11/7/201284

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Measure => Model => Mitigate

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Mathematical model from Newtons law of cooling

Model identied using semi-parametric regression

dT/dt = -k r T - k cu(t) + k ww(t) ) + q(t)change in temperature

over time

time constantof room

AC cooling

weather heating from occupantsand equipment

11AM 12PM 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM

21

22

23

Time

( C )

11AM 12PM 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM

0.0285

0.029

( C / s )

Time

Temperature:Experimental (blue)Simulated (red)

Heating from occupantsand equipment

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Learning-Based Model Predictive Control

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1PM 3PM 5PM 7PM 9PM 11PM 1AM 3AM 5AM 7AM 9AM 11AM

22

23

Time

( C )


0

1

A C

S t a t e

Time


22

23

( C )

Time


0

1

A C

S t a t e

Time

Experimental LBMPC: 12.6kWh Consumed

Temperature

Control Action

Simulated Hysteresis Control: 29.7kWh Consumed (estimated)

Temperature

Control Action

(Aswani, Master, Taneja, Culler, Tomlin, 2011)

g

LBMPC adjusts for internal dynamics, avoids over-cooling, trades off duty cycle and switching frequency

0 2 4 6 80

1

2

3

4

5

6

Minutes

k W

Transient PowerSteady State Power

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Supply-Following Computational

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Supply Following ComputationalLoads

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IPSRequests

Power

Background Processing (shiftable)

Controllable Storage

QoS (fidelity & latency)

Availability

Forecasts

Energy-Availability Driven Scheduling

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Non-dispatchable,variable supply

Power proportional,grid-aware loads

NREL Western Wind and Solar Integration Study Datasethttp://wind.nrel.gov/Web_nrel/

0 200 400 600 8000

2

4

6

8

10

12

14 x 104

Time (hrs)

P o w e r ( W )

Pacheco wind farm

0 200 400 600 8000

1

2

3

4

5

x 104

Time (hrs)

P o w

e r ( W )

Scientific computingcluster

200 250 3000

1

2

3

4

5

x 104 greedy pacheco 2.0x

Time (hrs)

P o w

e r ( W )

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Energy Slack

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gy

90

ThermostaticallyControlled Load

Set Point

IPSGuard band

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From Auto Demand Response

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Sutardja Dai Hall

To Personalized Automated Lighting

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Control

Three controllableballasts per xture

~5 zones per oor

BACnetsMAP

HTTP

Gateway

MySQLPythonDjango

PythonControlProcess

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Real Energy Savings

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11/7/201294

0 0

58%

68% 69%63%

53%

65%68%

75%71%

61%

0%

10%

20%

30%

40%

50%

60%

70%

80%

0

0.5

1

1.5

2

2.5

3

3.5

4

K W

SDH 4th Floor Lighting Energy Usage

Colab Savings

Floor kW

Collab kW

A Macroscope ???

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10/11 00:00 10/11 12:00 10/12 00:00 10/12 12:00 10/13 00:00 10/13 12:00100

80

60

40

20

0

20

40

60

80

100

100% Cooling

100% Heating

Dead band

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Network of

Cooling Tower Loop

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Dampers(VAVs)

Heating Coils &Valves

70 F setpoint

Condenser Water Pump (CWP)Always on max

Control

Loops

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Outside Air Intake

Supply Fan(Air Handler Unit AHU)

336 Pa setpoint

Exhaust Air Return Fan

Cooling Coil & Valve55 F setpoint

Chilled Water Pump (CHP)Always on max

Chiller 45 F setpoint

Cold Water Loop

H o t W

a t e r

L o o p

V A V L o o p

V A V L o o p

V A V L o o p

Air Handling

Loop

E c o n o m

i z e r

L o o p

Script the building

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11/7/201297

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11/7/201298

Theres a bldg app for that

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g pp

11/7/201299

Holistic HVAC optimization

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11/7/2012100

12/10 12/11 12/12 12/13 12/14 12/15 12/16 12/17 12/18 12/19120

140

160

180

200

220

240

260

280

RMSE = 5.6%

Savings: 16.52%25.4 kW

P o w e r ( k W )

MeasuredBaseline

Empirical models, includingexternal factors, and

monitoring, provide rapid,focused feedback

Dynamically set economizer, supply air temp, minairow, reheat

0 8/10 0 8/10 0 8/11 0 8/11 0 8/12 0 8/12 0 8/13 0 8/13 0 8/14 0 8/140

2

4

6

8

10x 10

4

A i r F l o w

( C F M )

Fresh air

Return Air

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CyberPhysical Building Systems

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BMS

Cyber Physical Building

L i g h

t

T r a n s p o r tProcess

Loads

Occupan tDemand

LegacyInstrumentation &Control Interfaces

PervasiveSensing

Activity/Usage

Streams

Local Controllers

Planning Visualiza;on

OccupantSa;sfac;on

Mul;-Objec;veModel-Driven

Control

Building IntegratedOperating System

H V A C

E l e c t r i c a

l

S e c u r i

t y , F

a u l t

, A n o m a l y

D e t e c t

& M a n a g e m e n

t

Control andScheduleExternal

Physical

Models

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BIM

EmpiricalModels

drvrs drvrs drvrs

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Synchrophasors

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Synchronized Phasor Measurement Units (PMUs)

distributed across the transmission system 30 Hz sampling of voltage, current, frequency & phase Synchronized (

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Power Systems Applications Enhanced state estimation, Operator visualization Black Start visibility, Line impedance derivation Oscillatory mode detection & damping Post-disturbance analysis, Islanding Power network model validation

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Phase Difference beforethe Blackout

Aug 14, 2003

Currently ~500 PMUs in US grid

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y g

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The issues to solve?

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Sensor placement Huge complex grid, few expensive PMUs

Time synchronization Higher delity requires tighter synchronization Especially as we move to the distribution tier

Latency Data rates (!), storage, query Continuous, unattended operation

Especially during crises Analytics, Prognostics,

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DIY Synchrophasors

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What happens when ?

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Push observability throughout distribution tier? Into consumption tier ? Electrify more and more of the transportation ? The loads become efficient, power

proportional, and grid responsive?

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From a Grid to an Energy Network

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110

Load IPS

SourceIPS

energysubnet

IntelligentPower Switch

Monitor, Model, Mitigate

Deep instrumentation Waste elimination Efficient Operation

Shifting, Scheduling, Adaptation

Forecasting Tracking Market

Availability Pricing Planning

11/7/2012

IT and the 4 Part GHG

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Reduction Plan

Efficiency

Electrify Decarbonize


the fuel

111

Monitoring, Analysis, Modeling,Waste Elimination, Power

Proportionality, Optimal Control

Intelligence, Communication,adaptation in Everything

ZELB. Supply-Following Loads,Energy SLA, Cooperative Grid

11/7/2012

What made me think it was time for

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WSN reseatch?

11/7/2012112

Why the next tier of internet is here?

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Thanks

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Keynote Sensys 11-7-2012

Documents

Transcript of Keynote Sensys 11-7-2012