design and implementation of a high-fidelity ac metering network
TRANSCRIPT
Wireless Building Energy Monitoring
andLoCal: an Intelligent Power
Network
Computer Science DepartmentUniversity of California - Berkeley
Microsoft Research Asia
Xiaofan Jiang (姜小凡 )
In collaboration with David Culler, Randy Katz, Scott ShenkerStephen Dawson-Haggerty, Prabal Dutta, Minh Van Ly, Jay Taneja, Mike He,
Evan Reutzel
2
My Utility Statement
Current level of visibility Delayed Aggregated over
time Aggregated over
space Inaccessible
Want Real-time Per-appliance
[Stern92], [Raaii83]
3
Aggregate is Not Enough
What percent is plug-load
What percent is wasted by idle PCs at night?
What’s the effect of server load on energy?
What’s the effect of turning off A?
What caused the spike at 7:00AM?
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This would be nice…
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Architecture
ACme application Standard networking tools Python driver + DB + web
ACme network IPv6 wireless mesh Transparent connectivity
between nodes and applications
ACme node Plug-through Small form factor High fidelity energy
metering Control Simple API
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ACme Node
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Two Designs
ACme-A ACme-B
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ACme-A vs ACme-B
Resistor + direct rectification + energy metering chip
Real, reactive, apparent power (power factor)
Idle power 1W Low CPU utilization
Hall-Effect + step-down transformer + software
Apparent power Idle power 0.1W Medium CPU
utilization
ACme-A ACme-B
A tradeoff between fidelity and efficiency
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ACme Node API
ASCII shell component running on UDP port provides direct access to individual ACme node: Adjust sampling parameter Debug network connection Over-the-air reprogramming
Separate binary UDP port for data Periodic report to ip_addr at frequency rate
Node API function Purpose
read() -> (energy, power) Read current measurements
report(ip_addr, rate) -> Null Begin sending data
switch(state) -> Null Control the SSR
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ACme Network
IPv6 mesh routing Each ACme is an IP router Header compression
using 6loWPAN/IPv6 (open implementation -blip)
Modded Meraki/OpenMesh as “edge router”
Diagnostics using ping6/tracert6
ACme send per-minute digest / no in-network aggregation
internet
backhaul links edge routers Acme nodes
data repository app 1
app 2
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Network Performance
49 nodes in 5 floors
Single edge router
6 month to-date 802.11
interference (on channel 19)
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ACme Application
N-tier web application ACme is just like
any data feed Python daemon
listening on UDP port and feed to MySQL database
Web application queries DB and visualize
UDP Packets
Python Daemon
MySQL DB
ApacheACme Driver
6loWPAN
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Visualization http://acme.cs.berkeley.edu/
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Building Energy Monitoring
1. Understanding the load tree
2. Disaggregation Measurements Estimations
3. Re-aggregation Functional Spatial Individual
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Understanding the Load Tree
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Deployment
Edge router obtaining IPv6 address
Ad-hoc deployment Un-planned
Online “registration” using ID and KEY Meta data collection Security
Online for 6 month and counting
10 million rows
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Deployment
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Raw Data
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Additivity using Time Correlated Data
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Multi-Resolution
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Appliance Signature
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Functional Re-aggregation
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Correlate with Meta-data
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Spatial Re-aggregation
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Individual Re-aggregation
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Improvements in Energy Usage
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Reducing Desktop Idle Power
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ACme Discussion
Measurement fidelity vs coverage Non-intrusive Load Monitoring (NILM) IP node level API vs application layer
gateway Easy of deployment is key DB design Multiple input channel / power strip
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What if the Energy Infrastructure were Designed like the Internet?
Energy: the limited resource of the 21st Century
Needed: Information Age approach to the Machine Age infrastructure
Match load & supply through continuous observation and adjustment
Enhanced reliability and resilience through intelligence at the edges Dumb grid, smart loads and supplies
Packetized Energy: discrete units of energy locally generated, stored, and forwarded to where it is needed; enabling a market for energy exchange
* Several slides borrowed from Randy Katz
Towards an Information Age Energy Infrastructure
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Baseline + Dispatchable Tiers
DistributionTransmissionGeneration Demand
Nearly Oblivious Loads
Non-Dispatchable Sources
Interactive Dispatchable Loads???
Energy Network Architecture Information exchanged whenever energy
is transferred Loads are “Aware” and sculptable
Forecast demand, adjust according to availability / price, self-moderate
Supplies negotiate with loads Storage, local generation, demand
response are intrinsic
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Information Overlay to the Energy Grid
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Conventional Electric Grid
Generation
Transmission
Distribution
Load
Intelligent Energy Network
Load IPS
Source IPS
energy subnet
Intelligent Power Switch
Conventional Internet
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Intelligent Power Switch
(IPS)
Energy Network
PowerComm Interface
EnergyStorage
PowerGeneration
Host Load
energy flows
information flows
Intelligent Power Switch
PowerComm Interface: Network + Power connector Scale Down, Scale Out
MultiScale Project Plan35
IPScomm
power
now
Load profile
w$
now
Price profile
w
now
Actual load
w
Data centerIPS
Bldg Energy
Network
IPS
IPS
IPSInternet
Grid
IPS
IPS
Power proportional kernel
Power proportional service manager
Quality-Adaptive Service
M/R Energy
Net
IPS
IPS
IPS
AHU
Chill
CT
LoCal System Architecture
Transmission
Distribution Market
Supply IPS
Supply IPS
Load IPS
Load IPS
Load IPSSupply
IPS
Load / DG
Generation
Physical Layer Information Layer
LoCal Simulator
LoCal Simulator37
Market
Supply IPS
Load IPS
Supply IPS
Supply IPS
Load IPS
Load IPS
Load IPS
Load IPS
Load IPS Generated using measured data from the ACme sensor deployment in Soda Hall
ACme data provides 6 months of continuous load data for individual appliances with 1 minute resolution
A Load IPS consists of a mixture of appliance types that might be found in a typical home (actual appliance chosen at random for each type)
Load IPS Responsibilities
Predict Next Hour Energy Needs
Last
Hou
r D
ata
Pre
vio
us
Day
Data
Load IPS Responsibilities
Determine Power Package to Purchase Incremental Cost of Base Power vs. Variable
Power
Set and solve for
Finally, we obtain the probabilistically optimal Base Power purchase amount
cVBVBBuuBu CPCCPttCPtC )()(
0
BP
C )())(Pr( BBu PcdfPtLt
t
Supplier IPS Responsibilities
Determine Power Package to Offer Cost of providing Base Power Cost of providing Variable Power Expected Capacity Factor for Variable Power Price of each power product
Market determined in competitive markets
))()(max( VVVVVVBBB PpPCFCpPCpC
Trends determined
by plant type, individual per plant
as well
LoCal Simulator
LoCal Simulator
LoCal Simulator Results
Highly Variable Load Large DC Component
LoCal Simulator Results
Low Variability Load (no coffeemaker)
Variable Power Contracts Exhausted
LoCal Simulator Results
Aggregate Market Contract Visualization
LoCal Simulator
Si
mul
ation
Ti
me
M
ark
et
Supply IPS
Load IPS createOffer
addPowerSource getCDF getPowerList
allocateLoad powerDema
nd getContracts
runSim
accountPower updateContr
act getContracts getContracts
accountMoney
accountMoney
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Thank You
ACme web site: http://acme.cs.berkeley.edu LoCal web site: http://local.cs.berkeley.edu Contact: [email protected] /