design and implementation of a high-fidelity ac metering network

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?

4

This would be nice…

5

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

6

ACme Node

7

Two Designs

ACme-A ACme-B

8

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

9

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

10

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

11

Network Performance

49 nodes in 5 floors

Single edge router

6 month to-date 802.11

interference (on channel 19)

12

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

13

Visualization http://acme.cs.berkeley.edu/

14

Building Energy Monitoring

1. Understanding the load tree

2. Disaggregation Measurements Estimations

3. Re-aggregation Functional Spatial Individual

15

Understanding the Load Tree

16

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

17

Deployment

18

Raw Data

19

Additivity using Time Correlated Data

20

Multi-Resolution

21

Appliance Signature

22

Functional Re-aggregation

23

Correlate with Meta-data

24

Spatial Re-aggregation

25

Individual Re-aggregation

26

Improvements in Energy Usage

27

Reducing Desktop Idle Power

28

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

29

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

30

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

31

Information Overlay to the Energy Grid

32

Conventional Electric Grid

Generation

Transmission

Distribution

Load

Intelligent Energy Network

Load IPS

Source IPS

energy subnet

Intelligent Power Switch

Conventional Internet

33

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

48

Thank You

ACme web site: http://acme.cs.berkeley.edu LoCal web site: http://local.cs.berkeley.edu Contact: fxjiang@cs.berkeley.edu /

fxjiang@gmail.com