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David E. Culler University of California, Berkeley August 2, 2011.
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Working together to put CPS Technology on the Sustainability Table
David E. Culler University of California, BerkeleyAugust 2, 2011
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What can we put on the table?
28/2/2011CPS-PI-11
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Sustainability?
“Sustainable development should meet the needs of the present without compromising the ability of future generations to meet their own needs”
Our Common Future, World Commission on Environment and Development, United Nations, 1987
38/2/2011CPS-PI-11
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Quantifying it - California Law AB 32
Reduce GHG emissions to 1990 levels by 2020
Governor’s 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 expected to grow from 37 => 55 million Economic growth
48/2/2011CPS-PI-11
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58/2/2011CPS-PI-11
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What it amounts to …
6
1990 2005 2020 2050Historical BAU
0
100
200
300
400
500
600
700
800
900
1,000
Historical and BAU Emissions
En-ergy emissionsNon-en-ergy emissions
GH
G E
mis
sio
ns
(MtC
O2
e/y
r)
CA
1990
CA
2005
CA
2020 T
a..
.
CA
2050 T
a..
.
US 2
003
0
5
10
15
20
25
14.62
12.93
9.88
1.56
23.4
Per Capita CO2 Emissions
Metr
ic t
on
s C
O2 p
er
cap
ita p
er
year
8/2/2011CPS-PI-11
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Can we do this?
78/2/2011CPS-PI-11
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But
88/2/2011CPS-PI-11
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My Take …
We can’t build our way to sustainability The discussion is focused on the Physical
New Windows, Materials, Motors, Biofuels, … Need to make the best of what we have,
or will have, and how we use it… This takes observation, intelligence and
control Demonstrable CPS technology on the
table Efficiency & Supply-Following Loads 10 years to innovate, 30 years to scale
98/2/2011CPS-PI-11
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What’s on the Table? – buildings
108/2/2011CPS-PI-11
Deployed
Demonstrated
Development
Research
Concept
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4 Part GHG Reduction Plan
Efficiency
118/2/2011CPS-PI-11
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4 Part GHG Reduction Plan
Efficiency Electrify
128/2/2011CPS-PI-11
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4 Part GHG Reduction Plan
Efficiency Electrify Decarboniz
e the electricity
Decarbonize the fuel
138/2/2011CPS-PI-11
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4 Part GHG Reduction Plan
Efficiency Electrify Decarboniz
e the electricity
Decarbonize the fuel
148/2/2011CPS-PI-11
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All required for even 60% reduction
but still fall short of 80%8/2/2011 15CPS-PI-11
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Efficiency
168/2/2011CPS-PI-11
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Electrification
178/2/2011CPS-PI-11
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Low Carbon Electricity Options
188/2/2011CPS-PI-11
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Build Rate
198/2/2011CPS-PI-11
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The Renewables are there
208/2/2011CPS-PI-11 43% agriculture, 3.4% urbanized
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The Problem: Supply-Demand Match
21
Baseline + Dispatchable Tiers Oblivious Loads
TransmissionGeneration DemandDistribution
8/2/2011CPS-PI-11
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Load-following Supply (?)
8/2/2011CPS-PI-11 22Source: EEX spot prices.
Growing proportion of renewables leads to higher price volatility. October 2008 to March 2010:>90 hours with negative prices; highest price reached: +€500/MWh, lowest -€500/MWh
-500.02
Daily minimum price (indicated in red when negative)Daily maximum price
494.26
€/M
Wh
-150
-120
-90
-60
-30
0
30
60
90
120
150
180
210
240
270
300
330
01.1
0.2
008
01.1
1.2
008
01.1
2.2
008
01.0
1.2
009
01.0
2.2
009
01.0
3.2
009
01.0
4.2
009
01.0
5.2
009
01.0
6.2
009
01.0
7.2
009
01.0
8.2
009
01.0
9.2
009
01.1
0.2
009
01.1
1.2
009
01.1
2.2
009
01.0
1.2
010
01.0
2.2
010
01.0
3.2
010
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… and in CA
8/2/2011CPS-PI-11 23
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Zero Emissions Load Balancing (ZELB)
Just the emissions from the natural gas to firm the 33% renewables exceeds GHG target
Even with 50% with natural gas & 50% with some yet-to-exist storage
248/2/2011CPS-PI-11
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We Understand Supply
258/2/2011CPS-PI-11
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Not demand
268/2/2011CPS-PI-11
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Limits to Renewable Penetration
Variability, Intermittency of Supply
Visibility into Availability of Supply Ability of Loads to Adapt Algorithms and Techniques for
Reactive Load Adaptation
Capability of the Infrastructure to maintain the match
8/2/2011CPS-PI-11 27
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CPS and the 4 Part GHG Reduction Plan
Efficiency
Electrify
Decarbonize the electricity
Decarbonize the fuel
28
Monitoring, Analysis, Modeling, Waste
Elimination, Power Proportionality, Optimal
ControlIntelligence,
Communication, adaptation in Everything
ZELB. Supply-Following Loads, Energy SLA, Cooperative Grid
8/2/2011CPS-PI-11
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CPS Technology …
National-scale Physical Information Service Data collection, streaming, archiving,
querying Modeling, Analysis, Control Representation, metadata, life-cycle Use others’, contribute yours
298/2/2011CPS-PI-11
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Some starts
308/2/2011CPS-PI-11
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sMAP: Uniform Access to Diverse Physical Information
8/2/2011CPS-PI-11 31
Electrical
Weather
GeographicalWater
EnvironmentalStructural
Actuator
Occupancy
sMAP
Modeling
Visualization
ContinuousCommissioni
ngControl
PersonalFeedback
DebuggingStorageLocation
Authentication
Actuation
Ap
plic
atio
ns
Ph
ysic
al
Info
rmat
ion
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
Vibration / Humidity
AC plug meter
Light switch
Den
t circ
uit m
eter
Prox
y Se
rver
EBH
TTP
Tran
slati
on
California ISO sMAP Gateway
sMAP Resources Applications
Google PowerMeter
Weather
sMAP
Every Building
Database
sMAP ecosystem
8/2/2011CPS-PI-11 32
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Cory CEC B2G Testbed
8/2/2011CPS-PI-11 33
Whole BldgMCL equip
MCL infra
MCL vac
servers
DOP HVAC
Central vent
office HVAC
inst Lab 199 HVAC
Plug loads
Lighting
Parking Lot
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DOE MELS => Appliance Energy
8/2/2011CPS-PI-11 34
LBNL Bldg 90611 of 1200 loads
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DOE/UCB/Siemens Auto Demand Response
www.openbms.org8/2/2011 35
Sutardja Dai Hall
CPS-PI-11
0
50
100
150
200
250
300
350
400
Lights & PlugsServers & CoolingNano FabHVACOtherP
ow
er
(kW
)
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BACNet => sMAP
Sensors,Actuators Controllers
Modem
Siemens P2over RS-485
ApogeeServer
RemoteLogin
BACnet/IPGateway
sMAP
Internet
Sutardja Dai Hall
8/2/2011 36CPS-PI-11
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Data from 1 Modern Building 1358 control settings
Set points, Relays (lights, pumps, etc), Schedules 2291 meters/sensors
Power (building, floor, lights, chiller, pumps, etc) Current, voltage, apparent, real, reactive, peak
Temp (rooms, chilled water, hot water) Air volume Alarms, Errors
2165 control outputs Dampers, valves, min/max flow, fan speed, PID
parameters 72 other
4+ million Commercial, 110+ million Residential
8/2/2011 37CPS-PI-11
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Current sMAP demography
Name Sensor Type Physical Layer Sense Points
Channels
Cory Hall Submetering Dent Three-Phase Modbus + Ethernet 79 3318
Cory Hall Metering ION and pQube Meters HTTP/Ethernet 3 150
Cory Lab Temperature TelosB 802.15.4 + Ethernet 4 8
Cory Lab Machines ACme 804.15.4 + Ethernet 8 16
Cory Chilled Water HeatX Modbus + Ethernet 1 11
Cory Weather Vaisala WXT520 SDI-12 + Ethernet 1 11
Soda Hall Sun Blackbox Fan speed; environmental
Http/Ethernet 10 84
Soda Lab Machines ACme 802.15.4 + Ethernet 40 80
Soda Lab Panel Veris E30 Modbus + Ethernet 1 42
Soda SCADA Data Barrington controls RS-485/various “1” 1670
LBNL Building 90 Acme 802.15.4 + Ethernet 550 1650
Campus Power Data Obvius Aquisuite; various
XML/HTTP/Ethernet 4 100
Citris SDH BACnet Siemens Apogee BACnet/IP + RS-485 “1” 600+
Brower Buildinbg Johnsons Control BACnet “1” 1500
8/2/2011 38CPS-PI-11
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insert
resample
aggregatequery
stre
amin
g p
ipel
ine
Page Cache
Lock Manager
Key-Value Store
Storage Alloc.
Time-series Interface
Bucketing RPCCompressi
on
read
ing
db
SQL
Storage mapper
MySQL
A Stream EngineD
e nt
cir
cui t m et er
sM
AP
sM
AP
~300k pts/sec
8/2/2011 39CPS-PI-11
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Scaling Out in the Cloud
8/2/2011CPS-PI-11 40
Stream Management System
Data Processing
Data Source
Data Sink
Real Time Monitoring
HBase Hive
HDFS
Map
-Red
uc
eHadoop
Interactive Workloads
Analytics Workloads
Columnar Data Storage
Query TranslatorSQL MR
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15,000 pts and growing…
8/2/2011CPS-PI-11 41
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CPS Technology National-Scale Physical Info Service Software foundations, platforms and
solutions for Energy Efficiency and Agility
428/2/2011CPS-PI-11
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Stages of Energy Effectiveness
Waste Not Do Nothing Well !!!
Power Proportionality Peak Performance : Power => Safety Optimize Partial Load - from nothing to
peakl Sculpting
Identify the energy slack and utilize it Negotiated Grid / Load / Human
Interaction Plan, Forecast, Negotiate, Manage
8/2/2011CPS-PI-11 43
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8/2/201144
Lighting
Servers
PDUs, CRACs
Our Buildings
HVACIT and Plug Load
CPS-PI-11
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8/2/2011 45
Power-Proportional Buildings ?
950 KW 1150 KW
Min = 82% of Max
Cory Hall: Office + Semiconductor + IT
CPS-PI-11
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8/2/2011 46
Power-Proportional Buildings ?
1.45 MW 2.02 MW
Min = 72% of Max
Stanley Hall: Office + BioScience - 13 NMRs
CPS-PI-11
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8/2/2011 47
Power-Proportional Buildings ?
62 KW
202 KW
Min = 31% of Max
LeConte Hall: Office
CPS-PI-11
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Re-Flash the HVAC …
48
Whole Bldg
inst Lab 199 HVAC
8/2/2011CPS-PI-11
LoCal + ActionWebs
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Learning-based Model Predictive Control
Mathematical model from Newton’s law of cooling
Model identified using semi-parametric regression
dT/dt = -krT - kcu(t) + kww(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
)Temperature: Experimental (blue) Simulated (red)
11AM 12PM 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM
0.0285
0.029
(°C
/s)
Time
Heating from occupantsand equipment
8/2/2011 49(Aswani, Master, Taneja, Culler, Tomlin, Proc. of IEEE, 2011)
CPS-PI-11
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50
Living Lab… LBMPC saved
30% energy when hotter outside 70% energy when cooler outside
Standard MPC was inconsistent Two-position control was inefficient
Method Measured Energy
Estimated Energy
Tracking Error
Temperature Variation
Average External Load
Two-Position Control Experiment
LBMPC 23.6kWh
0.75°C
0.13°C 11.0°C
Linear MPC 30.5kWh
0.62°C
0.30°C 11.0°C
Two-Position 32.6kWh
35.1kWh
0.61°C
0.20°C 11.0 °C
LBMPC Experiment
LBMPC 11.8kWh
13.3kWh
0.86°C
0.17°C 7.2°C
Linear MPC 8.6kWh 0.93°C
0.21°C 7.2°C
Two-Position 34.5kWh
0.55°C
0.19°C 7.2°C
8/2/2011CPS-PI-11
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51
LBMPC to Minimize Energy… Estimates heating load using model
Best control that considers estimated occupancy
(Aswani, Master, Taneja, Culler, Tomlin, 2011, submitted)
Cost
AC Constraints
Temperature Constraints
Temperature Dynamics
8/2/2011CPS-PI-11
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… or optimize to follow the supply
52
Generation
Transmission
Distribution
Load
VPG
8/2/2011CPS-PI-11
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53
at Building-Scale, and beyond
TemperatureBlue – Measured; Red - Simulated
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
1
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
2
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
3
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
4
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
5
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
6
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
7
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
8
Tu W Th F Sa Su M Tu W Th F Sa Su20
22
24
°C
9
Tu W Th F Sa Su M Tu W Th F Sa Su
0.7
0.75
0.8
0.85
0.9
0.95
°C
1
Tu W Th F Sa Su M Tu W Th F Sa Su
1.6
1.65
1.7
1.75
1.8
1.85
1.9
°C
2
Tu W Th F Sa Su M Tu W Th F Sa Su
1.2
1.25
1.3
1.35
1.4
1.45
°C
3
Tu W Th F Sa Su M Tu W Th F Sa Su
1.6
1.65
1.7
1.75
1.8
°C
4
Tu W Th F Sa Su M Tu W Th F Sa Su
1.4
1.45
1.5
1.55
1.6
1.65
°C
5
Tu W Th F Sa Su M Tu W Th F Sa Su
1.35
1.4
1.45
1.5
1.55
1.6
°C
6
Tu W Th F Sa Su M Tu W Th F Sa Su1.4
1.45
1.5
1.55
1.6
1.65
°C
7
Tu W Th F Sa Su M Tu W Th F Sa Su
1.5
1.55
1.6
1.65
1.7
1.75
°C
8
Tu W Th F Sa Su M Tu W Th F Sa Su
1.3
1.35
1.4
1.45
1.5
1.55
°C
9
Heating from occupants, equipment, etc.
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Making Sense out of Sensors
54
°°°
°°°
°°°
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Supply Air Fan VFD
55
2 132 262 392 522 652 782 912 1042117213021432156216921822195250
55
60
65
70
75
80
85
90
Input
Output
OAT
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What’s really going on?
Bring comfortable air from outside Cool it down till its really cold Push it out everywhere thru VAVs that are
at minimum opening Reheat it to set point So the empty rooms will be comfortable
This is going on everywhere! And we supply perfect, precious energy to
do it!
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230
15
248
87
190
13
190
13
200
14
161
19
287
48
0
50
100
150
200
250
300
350
Wat
ts
Pow
erE
dge
1850
Del
l Pow
erE
dge
1950
Sun
Fire
V60
x
Sun
Fire
x21
00 -
Cyb
er S
witc
hing
Sun
Fire
X22
00
Com
paq
DL3
60
HP
Int
egrit
y rx
2600
Server Power Consumption
Active
Idle
And IT is no better …
Core i7
Atom 333 Westmere
4/12/11 57CPS 2011
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Supply-Following Computational Loads
8/2/2011CPS-PI-11 58
IPSRequests
Power
Background Processing (shiftable)
Controllable Storage
QoS (fidelity & latency)
Availability
Forecasts
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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/
Pacheco wind farm
Scientific computing cluster
Energy-Availability Driven Scheduling
8/2/2011CPS-PI-11 59
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Scheduling & Energy Storage
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CPS Technology National Physical Info Service Software foundations for Energy
Efficiency and Agility Infrastructure for Energy Innovation
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Personalized Automated Lighting Control
Three controllable ballasts per fixture
~5 zones per floor
BACnetsMAP
MySQLPythonDjango
PythonControl Process
HTTP
Gateway
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Real Energy Savings
8/2/2011CPS-PI-11 63
Befo
re
13-M
ay
20-M
ay
27-M
ay
3-Ju
n
10-Ju
n
17-Ju
n
24-Ju
n1-
Jul
8-Ju
l
15-Ju
l0
0.5
1
1.5
2
2.5
3
3.5
4
0%
10%
20%
30%
40%
50%
60%
70%
80%
0 0
0.58
0.680.69
0.63
0.53
0.650.68
0.750.71
0.61
SDH 4th Floor Lighting Energy Usage
Colab Sav-ingsFloor kWCollab kW
KW
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From the agony of load-following …
648/2/2011CPS-PI-11
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to Co-operative Energy Mgmt in a Cyber-Physical Grid
65
Load IPS
Source IPS
energy subnet
Intelligent Power Switch
• Monitor, Model, Mitigate• Deep instrumentation• Power proportional Design• Energy-Agile Control
• Shifting, Scheduling, Adaptation
• Forecasting• Tracking• Market
• Availability• Pricing• Planning
8/2/2011CPS-PI-11
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Thanks
8/2/2011CPS-PI-11 66