Demand Response Verification (DRV)

Outline DRV Use Case Overview Non-Intrusive Load Monitoring (NILM) Compression Algorithm

load shed request AMI headend smart meter HVAC pool pump customer web portal DR gateway plugin hybrid fridge solar panels Internet customer network verification

AMI headend smart meter verification 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

AMI headend smart meter verification 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

AMI headend smart meter Proposed MethodAmount of info at AMI headend Transmission Speed Local Processing Time Raw dataCompleteVery slowFast Compression (loss rate)LargeSlowMedium Transmission Frequency = Func(load) MediumFastMedium Fit load data with standard distribution function SmallFastMedium Make local decision, transmit “yes/no” SmallVery fastSlow 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 Detection 2.Cluster Analysis 3.Cluster Matching 4.Anomaly Resolution 5.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 steps

Let’s Floor Some Values (20) 12 steps

Let’s Try Again (40) 8 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 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) Encoding A 16 B 32 C 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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