Demand Response Verification (DRV)

Outline

• DRV Use Case Overview• Non-Intrusive Load Monitoring

(NILM)• Compression Algorithm

load shed requestload shed request

AMIheadend

smartmeter

HVAC poolpump

customerweb portal

DR gateway

pluginhybrid

fridge solarpanels

Internet

customernetwork

verificationverification

AMIheadend

smartmeter

verificationverification

Link from meter to AMI headend

• Frequency: twice year ~ once per 15 min

• Data: Total wattage

Limited resources

• Bandwidth

• Processing power of meters

• Memory/Storage of meters

AMIheadend

smartmeter

verificationverification

Assumption

Power company has sufficient processing power to

analyze large amount of load data

Tradeoff: Accuracy vs Transmission Speed

• Accuracy - Amount of useful info received at AMI headend

• Transmission Speed (local preprocessing)

• Extracting info from data

• Compressing data

AMIheadend

smartmeter

Proposed Method Amount of info at AMI headend

Transmission Speed

Local Processing Time

Raw data Complete Very slow Fast

Compression (loss rate) Large Slow Medium

Transmission Frequency = Func(load)

Medium Fast Medium

Fit load data with standard distribution function

Small Fast Medium

Make local decision, transmit “yes/no”

Small Very fast Slow

1. Smart meters are not that trusted yet - lack proper authentication

2. Power company has well-established ways to analyze large amount of load data

Non-Intrusive Load Monitoring

• Developed in 1982 at MIT by George W. Hart

• While looking at load data for a photovoltaic study, the research team noticed that on/off events for major appliances in the home could be read visually

The Idea

• Individual On/Off events of high power appliances are easy to detect

Improvements

• Wanted the system to be able to recognize individual loads based on the aggregate data

• Determined that real power alone would not give enough information about which appliance was turning on/off

• But we are looking at AC power….

Real vs Reactive Power

• AC Power is made up of AC current and AC voltage

• Each is a sinusoidal wave that oscillates at some frequency

• Recall that power is calculated by the simple equation P = I*V

Real Power• In phase I and V yields real power:

Reactive Power• Out of phase I and V yield reactive power:

Improvements• Using the real and reactive power gives

a two dimensional plane to identify appliances

The Algorithm

• This observation led to a simple algorithm to ascertain that load in the system with the following steps:

1. Edge Detection2. Cluster Analysis3. Cluster Matching4. Anomaly Resolution5. Appliance Recognition

Edge Detection

• Analyze the incoming data for transitions

Cluster Analysis/Matching

• Group like transitions together• Match the on/off transitions that appear similar

Anomaly Resolution

• For cases that do not match known patterns, analyze the waveform for the possibilities of multiple on/off transitions for the net change

Results

• Simple algorithm has a high probability of identifying major appliances in residential settings

Results

• Pros:• Measurement data was simple• Small amount of data

• Cons:• Algorithm had difficulty identifying low power

appliances uniquely • Algorithm could not identify clustered systems• Systems that needed to turn on slowly sometimes

passed edge detection• The power signatures of appliances needed to be

known ahead of time for the appliance recognition

Recent Work: Improving Granularity

• In order to increase the ability to discern between individual appliances, more detailed appliance fingerprints are required

• The strategy in more detailed system is to look at harmonic properties of load data

• This gives more granularity at the expense of needing more detailed measurement and more processing power

Recent Work: Clustered Systems

• Clustered systems presents a problem• Examples

• Light bulb in a refrigerator• Automatically Defrosting Refrigerator• Multistage Light bulb

• Deal with this by introducing state based recognition using finite state machine models

• Modern methods can find clustered loads, but introduces even more computational complexity

Recent Work: Self-Learning

• All these methods depend on pre-determined list of appliances and load patterns

• These have been initialized ahead of time at installation of the system

• Would be nice if this was self-learning• Solutions are being researched that use neural learning

algorithms to create the appliance load data

• This introduces even more computational complexity

Current Status

• There is a lot of research in making NILM extremely accurate

• Papers report results accurate down to individual 10 watt light bulbs

• These algorithms are able to deduce power drawn from clustered sources and systems that are slow to ramp up to power

• The algorithms are also self learning so no initial setup is required

What we really need….

• If the load data is sent to a processing station, computational constraints are not as severe to complete the NILM

• The big difference is that we have the following limitations due to meter constraints:

• We may only have real power• There is a limitation on the amount of data we

can take due to BW issues

This is Ideal• The fact that we may* only have real power lowers

granularity• We are interested in turning off large appliances• There is an inverse relationship between the size of

the appliance and the difficulty of detecting an on/off transition

• Thus, the loss in granularity due to not having reactive power may not be a problem

• Additionally, we may not care as much if we cannot distinguish between two different 1000 Watt appliances

* There is an initiative to include reactive power measurements in smart meters

If Only…..

• Classic NILM can be used to analyze major appliance use

• In order to do this every major change in power level would need to be reported

• This could be problematic over low BW links

So much data!

40 steps40 steps

Let’s Floor Some Values (20)

12 steps12 steps

Let’s Try Again (40)

8 steps8 steps

Time To Compare

Instead of Flooring…

• Rounding• Create a point based on input and

change values only if above some threshold

• Take a rolling average (has to be used in conjunction with other ideas)

Threshold (±20) Vs Full

10 steps10 steps

• Deflate (e.g., gzip, zip, png)– LZ77

• Blah blah blah blah blah! => Blah b[D=5,L=5]lah blah blah! => Blah b[D=5,L=18]!

– Huffman (Prefix) EncodingA 16 B 32C 32 D 8 E 8

Time To Compress

(source: gzip.com)

Other Compression Algorithms?

• Lossless– LZMA/LZO (hash chains, binary trees

and Patricia tries)– Bzip2 (effective, but slow because it

has 9 steps)

• Lossy– Discrete cosine transform (audio/video)– Vector quantization (finds centroids)

(source: wikipedia.com)

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