Profiles for the Economic Actors in Distributed Energy

This post is part of the continuing Paths to Transactive Energy series. You can find them all listed by clicking on the matching metatag at the bottom of each post.

As this series continues its survey of Transactive Energy, we get, at last to what I see are the essential agent personalities. The Agent Personalities are a mid-level abstraction that makes it easier for the appliance supplier and the EMS/BMS maker to know what is being attached. Every appliance at the local store could be a pluripotent transactive agent, but this does not aid the brain-developer in understanding what you just bought. A wine cellar may not be on the list of known appliances, but it is useful to know that it is similar to the refrigerator and to an air conditioner in how it approaches the in-home energy market.

http://www.theenergymashuplab.org/blog/8agents 

These agent types interact based on the principals of transactive energy. The non-power services provided and mechanisms used by each system are not known to the energy market. The precise mechanism of each system is not known to the market. Each system uses the market to achieve its own goals.

The creator of a system can identify which economic best suits the system. Some systems may be most easily represented by aggregate roles, wherein each role remain simple.

For example, an air conditioning system and a refrigerator may each act as intermittent consumers. When in the same market, each system can optimize its own costs by buying when the other does not. The air conditioner produces an equilibrium of comfort, the refrigerator produces an equilibrium of the conditions to store food safely, and the market achieves a punctuated equilibrium of power use with lower peaks. An ice maker may act as a pre-consumer, buying power when it is cheap to have a supply of ice at the target time. A pre-consumer buys when others do not, so long as its delivery time and product (ice) can be met. These two agent types may coexist in a single interface just as the two roles coexist in the same refrigerator.

These agent profiles indicate patterns for market interaction. But the market doesn’t care what kind of agent you are. User interfaces, which is to say human interfaces, that want to augment information beyond market summaries, will need to look for another means to discover that information.

The ASHRAE Facility Smart Grid Info Model (FSGIM) allows for communication of expected forward load curves, I think. A controller needs to know more than a partner’s present state. The partners trading position is Inflexible until when? Shiftable until when, then available for how long? How adjustable (shed levels)? Etc. These are all things that higher-level controllers need to get from lower-level controllers. A higher level controller could pass DR-related signals to lower level controllers: it may choose to alter them for its own purposes.

Thinking about Snowden and Smart Grids

Privacy activists have long warned about the massive data collection enabled by smart grids. Utility representatives have long defended the smart grid by asserting that they have no interest in analyzing the lives of their customers. The recent revelations of government activity in the US make that defense irrelevant, as company after company confesses to have shared operational data with the government agencies. The lesson of current headlines is that it does not matter who collects big data, or what their motives are. Big data is a honeypot that will attract surveillance by someone.

One of the oldest stories of smart grids is of early researchers attempting to analyze the activity in a house from the meter. Early going was slow, as this was before modern tools were available. Still, after a couple weeks, analysts had figured out most of the electrical activity in the house: the big load of the air conditioning, the periodic spikes of the refrigerator, etc. Still, one activity seemed to fit no pattern.

Each week day, between fifteen and forty-five minutes after everyone left the house, there was a change in electrical activity. The researchers were stumped for some time. Eventually, they realized that when the family dog decided that “they” were finally gone for the day and not coming back, it would get up onto the nice warm waterbed. This changed the heating pattern for the bed.

After the researchers tracked this pattern for longer, they realized they could tell whether the dog was sleeping peacefully on the water bed and when the dog was restless.

In recent news, we have seen the US government asserting that there is no privacy right to transactional metadata. Times, durations, and phone numbers of all calls are shared freely. Businesses are subject to prosecution if they do not cooperate freely, or even if they reveal that they have been asked for information. Sniffers in data centers capture even secure information after decryption, so that even the internet service provider cannot see what is being tracked and recorded.

Some communications providers, such as Verizon, have bad records of privacy protection; they respond to all requests without push-back. Others occasionally push back on over-reaching calls. Based on their stated goals and communications documents, the utilities plan to share freely.

In the US, the utilities develop communication standards within the UCAIUG association. The UCAIUG develops communication processes and business process common to all the US Utilities. The standard for communication of meter data to third parties developed in the UCAIUG is OpenADE (Automated Data Exchange). It is notable that in their own OpenADE development documents, exchange of information with law enforcement is given a higher priority than exchange of information with customers.

The stated priorities of OpenADE have always been troubling. In the last few weeks, even the skeptical have come to see that big data is irresistible to government agencies. Protests by power utilities that they do not want to use the data are meaningless. FISA court data requests typically are known only to small numbers of a company’s employees. Discussing the requests openly, either within or beyond the company can violate federal law. A couple years ago, if worried publicly about this, one could be accused of being a conspiracy theorist. Today, doing so is evidence merely that one reads the paper.

We have it in each of our hands to preserve our own privacy. Consumer technologies exist to smooth power curves and permanently shift load. Energy storage technologies able to accept and provide trickle charges within a business or home can be used to hide the details of our activities and our lives. These technologies don’t let energy data out while they accomplish the goals of smart energy. You can adopt them, or you can allow further monitoring of every activity in your life.

Whatever one feels about Snowden’s revelations about current behavior of the NSA, they are part of making public how government agencies will make use of any sufficiently large trove of data gathered by others. Other news demonstrates a willingness to use information gleaned by one agency to coordinate public and political pressure by other agencies against those who dissent, and to do so without regard for regulation or fourth amendment. This should give anyone pause before contributing reating with the government.

We have it in each of our hands to preserve our own privacy. Consumer technologies exist to smooth power curves and permanently shift load. Energy storage technologies able to accept and provide trickle charges within a business or home can be used to hide the details of our activities and our lives. We can use minimalist economic signals to accomplish everything hoped for of the smart grid. These technologies don’t let energy data out while they accomplish the goals of smart energy. You can adopt them, or you can allow further monitoring of every activity in your life.

Big Data, Buildings, and the Internet of Things

Big Data is the hot new buzz-phrase for something that buildings system integrators have long struggled with. Last Thursday (3/29), the White House Office of Science and Technology Policy (OSTP) launched its public initiative on big data for government, the Big Data Research and Development Initiative.

The purpose of big data is to support analytics, that is the massive crunching and correlating of data to find patterns. Early targets of the initiative include:

• putting the government’s own data sets into open formats
• pushing states to include a data or statistical literacy component in their education plans
• establishing ways to continuously collect data on prescribed topics as opposed to relying on temporary snapshots

The real time use of big data that is most commonly in the news click-stream and advertising analytics. This back-room technology only makes the news when there are privacy violations. Big data analytics are why Google is now in a death-match with Facebook, and why the European Union is in a privacy face-down with Google.

In government, the best known big data analytics are in security and crime prevention. Einstein systems gate all information in or out of each cabinet-level department, searching for patterns that indicate intrusion. The NSA and FBI are doing something with big data; the NSA may or may not be consolidating information on all internet communications at its Utah Data Center.

Buildings have long struggled with big data. They are not designed for storing or to processing too much. System instructions regularly warn to minimize trend reports. Product from a number of leading makers of environmental controls struggle with monitoring just a small portion of the buildings on the UNC campus. Building systems houses all aim at cloud-based analytics in their next release, but each that I have seen struggles with pushing information to the cloud. I have watched very fast networks struggling to handle data collection from a 100 buildings, and watched data edifices crack under the hundreds of gigabytes they produce each week.

We are just now entering the period in which the internet of things (IOT) becomes real, and the IOT stores its data in the cloud. Last month, Ninja Blocks (http://ninjablocks.com/) got its initial funding. Ninja blocks are consumer sensors that are as cheap as X10, and send their data to the cloud. Ninja blocks use open source hardware (download schematics from the site) to sense their environment: acceleration, temperature, current, humidity, motion, distance, sound, light and even capture video. You can create and sell your own Ninja Blocks to connect to the Ninja Cloud.

The Ninja Cloud connects this sensor information to social and cloud services. Sensor events can send tweets, SMS, or email. Ninja photos and video can move automatically into Facebook or Dropbox. The user plugs in a Ninja Block and then uses the web to develop scripts in the Ninja Cloud using point and click.

This may not be the same as energy management, but one of the more successful campus energy projects of recent years set up Facebook pages for buildings on the University of Mississippi campus. Students were encouraged to friend the buildings; systems in the buildings tweeted their energy use. The project raised Student awareness.

Ninja Blocks is a new company. They can probably do most of what they claim. Their team of entrepreneurial young engineers seems smart, quick, and committed. Their business plan is inspired using open source hardware to let others create new value sources for the Ninja Cloud. Still, I wonder whether their approach will scale well. They may hit the same wall that I have seen, when too many sensors are continuously logging too many points to the cloud.

Whether or not Ninja Blocks makes it, they are the future. Other start-ups, such as the Bluetooth-based, open source i-voltmeter will change the way we think sensors work. The data gathered by the internet of things will make its way to the cloud, where it will be Big Data. Building systems that do not participate will find themselves pushed aside.

The value of Big Data is in re-purposing and in re-use. The cost of gathering big data is going down, and will continue to go down. The Big Data from buildings will accumulate at an astounding rate. The value of Big Data will be in continuous re-harvesting for more information, the way click-streams and advertising are harvested again and again. Building operations and failure predictions are only the start.

Big Data from Building systems must learn to share well with others. This industry must consider its own version of the federal goals: open formats for data, better statistical literacy in systems, and the methods to collect and store very large volumes of data loom large. We may need to use the common semantics from Project Haystack as a common ontology for our big data. It will be mandatory to share with the Big Data from the IOT, both to accept IOT data into Building Clouds, and to send Building data into the IOT clouds, including the Ninja Cloud.

It will be a fast ride. Into the Clouds!

Privacy Mosaic: Tiling over the Fourth Amendment Piece by Piece

Regular readers know that I am concerned that the accumulation of many small legal actions can create a violation of privacy that exceeds the sum of the observations. This week, the DC Circuit Court ruled that prolonged recurring legal acts can become an illegal search, or one that requires a specific warrant. If it stands on appeal, this theory may be one of the most important decisions to protect individuals and restrain the modern state ever.

The ruling defines a new "mosaic" theory of the Fourth Amendment under which individual law enforcement actions that are not searches to become a search when collected together. This is an important new theory. Noted fourth amendment scholar Orin Kerr has written how this throws decades of decisions on their head. Lawyers, though, worry about clear predictability of the law more than about theory and justice and the proper balance between the individual and society.

In the case United States v. Maynard, the court ruled that the long term use of a GPS device to monitor a car required a warrant. The government argued that roads are public, and that it could have had a watched public acts on public roads if it wanted; automating that information gathering was no big deal. According to the court, "[T]hat whole reveals far more than the individual movements it comprises. The difference is not one of degree but of kind, for no single journey reveals the habits and patterns that mark the distinction between a day in the life and a way of life." Exactly.

One purpose of constitutional law is to provide a basis for understanding bodies of law as well as individual laws. Law is made up primarily by decisions accumulated over time, in response to uncomfortable circumstances. A small bad decision supports another slightly worse decision until the law is firmly behind something few would choose. Plessey vs. Ferguson, the famous case that defined separate but equal as the law of the land was uncontroversial when passed, unexamined for 50 years, and supported by precedent and cemented by rulings that followed. It was good that we overturned that body of law.

In a similar way, we have been building a series of decisions about the use of technology and surveillance that must be overturned. The law has been willfully ignorant about technology and change. Perhaps it is because judges are older when appointed, and isolated from wider business. Perhaps it is because judges are discouraged from speculating on their thoughts and motives in public. Perhaps they are simply overly deferential to congress and police.

Judges have ruled that the information on personal computers are neither personal “papers or effects”. The current administration is arguing that searches of cell phones, including modern smart phones with call logging, email, documents, and passwords for accessing personal and business web sites, do not require a warrant. For whatever reason, judges have not given sufficient respect to digital papers and digital effects.

I have written before that accumulating data creates something that is of a different quality than each datum, and that quality is more dangerous as the points accumulate. I have written that there is danger to the citizenry even in gathering data required to operate a business, when it is shared outside the purposes of that business. I now know to call this the mosaic theory.

The government will certainly appeal United States v. Maynard. It may be overturned. If it is not, it creates a legal theory that may be held not only against police actions of the government, but against the misuse and re-sale of information gathered in e-commerce. It establishes a legal theory for discussing the repurposing of operational data.

It might help change to direction of the smart grid. Direct load control of homes and commercial buildings collects millions of data points that create their own mosaic. These mosaics will combine with mosaics from the internet and e-commerce. If every market is the sum of the participants knowledge problems, then not only privacy, bit all markets are changed, as one set of participants accumulates an immense imbalance of knowledge about the other.

Economic signals in smart energy collect less personally identifiable details than do direct load control models. We should all prefer them, rather than contribute more to the mosaic owned by others, and created to achieve advantage over each of us as individuals.