The Trader’s perspective on Transactive Energy

Transactive energy has been notoriously difficult to pull off. The few successful implementations were not interoperable. The people who knew markets did not understand electricity and its use. The people who knew the economic value and operation of building systems did not understand markets. The large regulated economic entities that today control the distribution of electric power see price as something determined by the utilities commission. The bulk power market operators, with the deepest understanding of power markets have been unable to shed their legacy of control.

The US National Institute of Standards & Technology (NIST) defines transactive energy (TE) as “a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter.” TE essentially uses markets to create spontaneous order out of the chaos of free-agent users of energy using different control systems and technologies to live their own best lives. Markets are algorithms are cooperative algorithms that generate solutions to changing supply and demand without the imposition of central control.

More than a decade ago, NIST and the US Department of Energy sponsored the development of communication standards to enable deployment of TE. The result, OASIS Energy Interoperation, saw little adoption. It was too complex. Products developed by different people interoperated only with difficulty. The work of several brilliant economists to define markets in time (or time-of-use) inspired the original specification. But, as any student in business school knows, economics helps understand business, but is not assist market participants accomplish their goals by making daily buying and selling decisions.

Financial traders can use any market in the world; but must know the different rules that govern each market. There are rules for trading hours, for after-market trading, and for market characteristics. There are Order Book venues, which you may recognize if you have traded stocks. There are Auction venues in which everyone who is “in the money” gets the same price. There are Quote-driven venues in which individuals negotiate directly. Each of these rewards different trading strategies.

There are standard ways to interact in financial markets, ways to buy and sell, ways to negotiate directly, ways to express how each market works. The FIX Trading Community (FIX) Protocol Association is the premier developer of communication and business practice standards across financial exchange. The adoption of FIX standards and specifications drives greater transparency of financial markets around the world.

The committee working on CTS has been meeting with representatives of FIX for more than a year. The economists who drove Energy Interoperation posited the smart toaster, a shorthand for the least sophisticated device able to autonomously participate in a TE market. No one is going to program the smart toaster; it must be able to find local markets, discover the trading rules of each, and begin buying. More sophisticated systems can understand venue mechanisms develop strategies that support their more sophisticated strategies. For example, a battery both buys and sells over time. The battery owner can configure policies to support purposes not shared with the market. FIX has taught the committee how to define market descriptions and rules that enable a machine to self-configure for trading in the local markets.

CTS does not design or specify any TE market. FIX has helped CTS to describe any market in time. CTS will support both the toaster and the battery in understanding how each market works, that is, it will support the individual energy trader’s participation in any market based on time.

Chatelaine Security for Smart Buildings and Cities.

 There is a growing recognition that traditional models of cybersecurity are inadequate to secure complex systems, and to detect security flaws in systems of systems. (Many definitions essentially declare complex systems and systems of systems to be equivalent terms.) Full-stack systems are too fragile and ponderous to readily address smart cities, or even smart buildings. The spatial web anticipates unbounded numbers of systems interacting, each with its own technology, and purpose, and even perhaps frame of spatial reference.

 I read this week of applying behavioral economics to cybersecurity and calling the result (and the new book) “Security Chaos Engineering”. It seems aligned with models for security in actor-based systems, which seem to me the only way to develop the IoT at true scale.

 Actors do not know what other actors do in a system. Actors receive messages from other actors in potentially massive concurrent computation systems. In response to a message it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next message received. Actors can only affect each other indirectly through messaging, and cannot know the state of other actors.

 You may recognize this as the fundamental model of cloud-native computing, whether the cloud is in a far away data center, or in many such data centers, or in an on-premises cloud, or in some hybrid of the above. Some name this approach microservices. What makes it work is well defined invariant interfaces, that is, fully defined messages between systems. Any system, then, no matter how large or complex can participate AS IF an actor so long as it limits a specific interaction to these commonly-defined messages.

 Transactive energy is essentially a means to replace an integrated control system with a system of systems, with each economic actor clearly a separate system.

 This last week found me discussing the implications of these approaches for cybersecurity standards. Traditional cybersecurity doctrine, including for cyberphysical systems, assume to much homogeneity of systems, in internal design and in purpose, and thus make assumptions of cybersecurity agent omniscience that is unachievable. In complex systems, it is far easier to influence the system your system relies on than to ever touch your system. Even within a single system, a man-in-the-middle exploit on sensors can more easily be replaced with some chewing gum on the sensor, or with a light tap of a mallet on the sensor, or even remote infrasonic attacks on a sensor array.

 What one can do is monitor the patterns of interactions (messages) between distributed actors, likely with ML (machine learning), and notice that one of the systems is acting less and less as expected. This can work even in a zero-trust environment, in which all the actual messages are encrypted. One still cannot know automatically whether the changes of behavior are due to enemy action, or merely change in programmed motivation.

 Maggie had the misfortune of being on a four-and-a-half-hour drive with me last week, and so endured a long description of this approach and of the week’s conversations. She promptly declared this to be a chatelaine model. Hmmm.

 A chatelaine is the mistress of a large estate (chateau) who may not know everything that is going on, but has all the keys, and notices patterns. There is no butter in the in the bin – is the milk-maid on vacation—did she get married—was she fired—did the cow run dry—do we need a new cow? The chatelaine can notify the owner, and she can invent a plan of action and investigation. I liked it. It is a much more approachable, easier to understand, term than “chaos security engineering”.

 I want a chatelaine in the message fabric.