Adjusting the Primal Forces of Transactive Energy

In a famous scene written by Paddy Chayefsky for the movie Network, Howard Beale is summoned by Arthur Jenson who explains to the crazed newsman that when he clings to his outmoded notions of how the world works, he is messing with the primal forces of nature. In transactive energy, the transactions are the only primal force, and notions of specific controls, and detailed manipulations of devices are out of date. Systems communicate only by the transactions, by prices to devices. To be consistence, future guidance to autonomous energy systems must be based on financial communications about energy transfers and energy resilience.

Transactive microgrids can balance energy supply and use over time with transactive energy. Internal prices adjust over time in response to supply and demand. No system needs to know anything about any other system, only what the market tells them. A transactive microgrid acts as a sealed system.

A microgrid manages energy resilience over time. An entire microgrid can act as a single system participating in in a larger microgrid, or interacting with a peer microgrid. A single node in the smaller microgrid also interacts with the larger microgrid. In each microgrid it interacts with, such a node is merely another peer. It buys and sells power in the smaller grid as it does in the bigger grid. The smaller microgrid may appear to be a power source to the larger. The smaller microgrid may always buy from the larger. In either microgrid, it acts precisely as do all others. No node knows that another node has a microgrid “within it”.

A proper transactive energy microgrid is essentially autonomous, creating the best balance with the information it has, making the decisions on the edge. Transactive energy markets are a proxy for management and control decisions. Internal system needs are projected into the market through the activation of bidding strategies. Budgets for each system determine priority, but unless a microgrid is badly under-sourced, systems can find some time to run. Within a microgrid, one can increase how frequently, or for how long a system can run by boosting its budget.

In fractal microgrids, one can identify “bridge” nodes, i.e., a node with a connection into the micromarkets that run each microgrid. In each microgrid it participates in, this bridge node uses only the market communications as does any other node. A bridge node likely has some internal “special” logic, just as a storage node has internal knowledge of its own battery chemistry, but this logic is of no concern to the microgrid markets. If the bridge is bringing ISO power and prices to the microgrid, it may compute a markup based on locational pricing, or based on transmission loss. There is no expectation that a node that interacts with two microgrids pass information from one to the other, unchanged.

The transactive energy markets in the two microgrids do not even need to use the same currency. One could use a fiat national currency, such as a US dollar. The other could be using some sort of cryptocurrency, or even a nominal currency defined only within the microgrid.

Direct policy directives can change the behavior of individual systems. Before the hurricane, I may want the power storage system to be fully charged. Because a storage system both buys and sells, it can be reluctant to sell, and eager to buy, until it is fully charged. Because a microgrid may also be able to buy and to sell power, the bridge node may be able to respond to a directive similar to the one that the storage system receives. These need not be the same at all levels. I may want to “starve” a microgrid even as I tell the same microgrid’s storage systems to charge up. How the overall system of systems responds is an emergent behavior.

Social policy, or command intent, can likely be implemented by a small range of directives issued only to the bridge nodes. In an industrial park, I may issue a directive to make all bridge nodes other than, say, the node representing a hospital microgrid to prefer exporting, just a little. The hospital may then be able to buy all it needs—even while all other microgrids find their new equilibria with somewhat less power.

Because fractal microgrids are self-similar, a directive may have effects several microgrids away. A “downstream” microgrid which is represented by a node in a hungry microgrid, may find itself on a diet as well, as the node is less able to acquire power to sell to that microgrid.

The basic market interactions needed by microgrids are well understood and few. They are being standardized this year in the OASIS Energy Interoperation TC. The directives to alter power flows between and across microgrids are not as well defined in standards. I think I have convinced myself, while writing this, that they can be accomplished solely with directives on preparing for resilience through financial information, just as they are for directives to storage systems.

Back to basics: Some Definitions

Today I am restating some definitions of terms, reflecting how my own understanding has changed over time.

Transactive Energy

Transactive Energy (TE) is the term for an energy balancing approach using economic techniques for dynamic balance of supply and demand within energy and power grids.

TE is a particular case of Transactive Resource Management which uses markets to allocate any commodity (resource) whose value is determined solely by its time of delivery. Transactive Resource Management was first described at the Xerox Palo Alto Research Center. The GridWise Olympic Peninsula Testbed Demonstration Project was the first transactive active energy field experiment in 2008.Transactive Energy was described by Edward Cazalet in a number of papers and talks culminating in the reference book “Transactive Energy: A Sustainable Business and Regulatory Model for Electricity” (Barrager & Cazalet, 2014).

Transactive techniques allow dynamic balance of supply and demand where energy is in surplus or shortage, in contrast to traditional techniques which address surplus less effectively. Transactive Energy (TE) is seen as a fundamental organizing principle of future smart grids.

Microgrids & Micromarkets

Microgrids are independent systems that manage the distribution of power. Microgrid components may supply, consume, or store electric power. TE can be paired with microgrids to make decisions about power management and distribution without constraining technology choice or technology evolution inside each microgrid component.Micro-markets require physical delivery of the product or service. Micro-grids allow (and in fact are defined by) the ability to shift energy and power within them. There is a useful symmetry of managing the balance of supply and demand in a micro-grid by means of a co-extensive micro-market.

Participants in more than one micro-grid must be able to deliver and receive the products bought and sold. North American markets typically distinguish between transmission (longer distances) and distribution (shorter distances, more end points) with different regulatory regimes. Micro-grids (composed or standalone) can be structured to allow avoidance of complex regulations designed for much larger scale enterprises.

Common Transactive Services

The interactions of Transactive Energy were defined in the Energy Interoperation Specification (OASIS, 2012). The Common Transactive Services (CTS) refers to a minimal set of standardized services originally developed as part of the NIST Transactive Energy Challenge.

The Market Code resource uses CTS-defined messages for all interactions between components. CTS defines a restricted profile of Energy Interoperation that can interoperate with each of the transactive systems within a microgrid. CTS includes minimal extensions and is at an architectural level appropriate to the semantics of all transactive systems minimized for local micromarkets, and able to enable decoupled evolution. A recent project of NIST and the Energy Mashup Lab made the first use of CTS. The Energy Mashup Lab (http://www.theenergymashuplab.org/) (EML). The Lab is a non-profit (501C3) whose purpose is the development and promulgation of microgrids through promoting open source software for TE based microgrids.

Cyber-Physical Systems

Cyber-physical systems (CPS) are systems built around co-engineered interacting networks of physical and computational (IT) components. The term CPS includes the overlapping technologies referred to as the Internet of Things (IoT), the Industrial Internet, and Operational Technology (OT). CPS applications in specific economic sectors are referred to with the terms smart manufacturing, smart transportation (including autonomous vehicles), smart healthcare, and smart energy. (NIST Cyber-Physical Systems Public Working Group , 2017).

Many CPS applications are inherently distributed and equipped with wire-bound or wireless communication facilities. When their components are largely autonomous, the organizing principle is coordination rather than control. A CPS providing critical services such as dynamic and prospective traffic safety, factory and process control, or healthcare needs to be highly dependable requiring the availability of reliability performance, availability, safety, and security.

Any consideration of CPS always includes the physical. Whatever software they run, their physical actions are constrained by physics: mass, momentum, chemistry, biology, and, for TE, electricity. When CPS are deployed in infrastructure, they will likely operate in place longer than typical IT systems. With long-life comes diversity; even if one built a CPS monoculture, with common ownership and a single vendor, the evolution of products and technology over time would lead to diversity of components.

This inevitable necessary internal diversity of components within a CPS rewards an abstract or service-oriented model for CPS integration. Service-Oriented Architectures (SOA) coordinate systems not by orchestrating processes, but by coordinating effects. For TE, one can understand this not as “turn off pump #3” but instead “use less power for 10 minutes”, not as “charge battery” but as “power is currently available”. This consideration points to the desirability of integrating CPS using abstract coordination such as CTS.

Many CPS are social systems. Autonomous cars drive to human chosen destinations. Autonomous domotic power systems must balance domestic services while they balance power and price. Even factory systems may respond to labor shifts and human-provided maintenance schedules. The social component means that few CPS can be “set-and-forget” after install, but instead must respond to and provide services to humans.

Spontaneous Order on a Continental Scale

A recent conversation about European power markets and some “glitches” in early June shown a light on profound issues in cybersecurity, in system architectures for big infrastructure, and to an extent the scalability problems with many of the hottest applications for the Internet of Things (IOT).

The specific observations was a plea for direct central control, even as it used an example that showed the shortcoming of infrastructure architecture based on assumptions of central control. It then learned the wrong lesson, that spontaneous order is too “risky” at large scale.

>>> Something went wrong on the 6., 12. and 25. June 2019.
>>> The belief in the Market to fix everything ... may end up in a big
>>> blackout.
>>>
>>> Add-On (2019-07-03):
>>> Today I found more details on the likely reason why we were so close
>>> to big trouble:
>>>
>>> "Due to a faulty data package, the European electricity
>>> exchange EPEX in Paris decoupled the European
>>> electricity market on June 7, 2019. This caused a great
>>> deal of excitement on the markets. Johannes Päffgen,
>>> Head of Energy Trading at Next Kraftwerke, explains the
>>> causes and consequences in an interview.
>>>
>>> Christian Sperling: Johannes - What happened? Why
>>> was there so much trouble at EPEX on the Friday before
>>> the Whitsun holidays?
>>>
>>> Johannes Päffgen: Well - in the end it's a computer error...
>>> but we should go into that later. At about 11:40 this Friday
>>> we noticed that something was wrong at EPEX.
>>> We couldn't place any more bids for the day-ahead electricity
>>> auction on Saturday. ..."
>>>
>>> I guess it was a human error ... somebody didn't take into account
>>> that corrupted data packages will be sent and received ... how could
>>> a faulty package have such a dangerous result?!?!
>>>

While Transactive Energy is superficially similar to the way the bulk power markets have long operated, the power of TE is in local markets. The first benefit of TE is to hide the control complexity/diversity of different technologies behind common signaling. The second benefit is to permit diversity of motivation of each participant in the TE market, as those are also hidden behind the common signals. The power of TE is to allow an emergent order to arise, with balancing of supply and demand occurring without respect to technology or control system or personal beliefs.

One can think of TE as embracing that the Knowledge Problem described by Economics applies to the world of things as well, and that we can use markets, i.e., small decisions made by the participants to participate or not at each moment, to solve power availability without central control. The evolution of life on Earth, of language, of the brain, and of a free market economy are considered systems which evolved through spontaneous order. Naturalists often point to the inherent "watch-like" precision of uncultivated ecosystems and to the universe itself as ultimate examples of this phenomenon.

TE implementations must be aligned with the newer methodology of Laminar Control. Mid-level lamina can coordinate lower level nodes, but do not reach in to provide direct controls. Lamina may however share situation awareness, local effects up, wider area conditions down, to improve the decision-making within each. No Lamina requires the situation awareness of the adjacent lamina.

This has important implications for security and for future technological evolution of power systems on the grid. Aside from the very top level, all lamina are discontinuous. The layer that controls one neighborhood is not actually connected to the controls of a nearby neighborhood except through a common higher level lamina.

The loose coupling of component systems based on abstract communications is characterized as an anti-fragile software pattern. Lightly managed systems coordinated by abstract communications create spontaneous order. Spontaneous orders are distinguished as being scale-free networks, as opposed to the hierarchical networks traditionally used in power distribution management. Spontaneous order is defined as the result of actions, not of design.

For anti-fragile patterns to create resilience and stability, their interactions must be properly scoped so at to not create additional dependencies that create fragility. For TE, this means that not only must the market be local, consistent with the grid lamina, but each market must not rely on additional fragile elements. Making local decisions directly dependent on the communications infrastructure and market infrastructure far away, say at EPEX in Paris, reduces grid resiliency and introduces new cybersecurity challenges.

Besides, the grid is not Magic, and one really cannot buy power from Castille in Antwerp absent the power transmission capability to support such local delivery.

The markets of Transactive Energy will work best when they are based on local markets, able to balance not only power but voltage and frequency within the local distribution loop. Another market may use TE in the district, managing flows between the local distribution systems, and, again, not requiring detailed knowledge of what is inside each. Ideally the market for each will be collocated with the nodes and the controls for each.

Loosely coupled systems in organized in an anti-fragile pattern are manage by objectives and for results. They have no need to expose their internal operations or controls. From a security perspective, this greatly reduces potential attack surfaces. From a policy perspective, this reduces barriers to rapid future introduction of new technologies into a system of systems.

ASHRAE finished defining the Facility/Smart Grid Information Model (FSGIM) some years ago to describe what a Facility should know about itself to participate in these distributed local markets (ASHRAE 201). The abstract information model is consistent with the information model of the Transactive Energy market operations. A Facility that knows its FSGIM, is ready to participate in the local market. Local distribution markets can then replace the wasteful statistical and historic models that manage local power delivery today.

From the SCADA Security perspective, this model moves intrinsically toward defense in depth. From a social and organizational level, each market is a move toward liquid democracy as neighborhoods with their own goals interact with the wider grid. From a technology market perspective, this enables more rapid introduction of new technologies, including those of distributed generation and storage.

Laminar Control and Transactive Energy

Laminar control is drawing a lot of attention from utilities today, and it may just clear the way be the basis for distributed transactive energy (TE).

The problem of smart grids boils down to adapting to intermittent power sources while reducing the operating margin. In power distribution, the operating margin is the amount of “extra” power available at any time. It is the operating margin that protects power delivery from unanticipated power consumption. This causes a volatility of power supply even while it reduces the ability of the traditional grid to adapt to consumers.

The intermittent power sources are distributed, meaning that they cannot supply any consumer not within the local distribution line unless that power travels between lines. For some users, these power sources will be local, and using them locally may not require permission from the grid. Some smart microgrids will not even be attached to the larger grid, so the model cannot rely on central control.

The power utilities have made heroic efforts to try to build a central control system that can manage this growing complexity and volatility with less margin for error. They still have little ability to provide an optimum solution to the knowledge problem of diverse technologies serving diverse purposes to support diverse activities. We are now seeing the beginning of a top-down re-architecting of the grid. 

Laminar Control manes an approach that layers the operation of power distribution. A lamina names a discrete adjacent layer, a term usually used for tissues in biology or for layers in rocks across a geological area. Laminar Control delegates decision-making to the Laminar Control Nodes within each lamina. Upper layers provide guidance based on strategic surveillance and offer situation awareness. Laminar Control nodes respond as best they can and provide telemetry up. Each node may itself have lamina underneath, with its own control nodes. At the lowest level, decision-making may use mechanisms such as traditional demand response (DR). This model pushes decision-making pushed down to the lowest layer, also referred to as the Edge. The Edge is where the local situation can be more clearly perceived and rapidly acted on. Even if there are disruptions in communications or power supplies from above, the elements at the edge can continue in semi-autonomy to complete the mission at hand.

Bottom-up re-architecting of the grid is getting to the same place. A FSGIM-aware facility is a facility ready to act as a Laminar Control Node. A FSGIM-aware node is also ready to negotiate with its peer nodes even in the absence of the higher lamina. A vehicle, then, acts as a mobile control node. Whether it is a peer node to the building systems, or it is a member of a lamina below the building or facility is an implementation decision.

Some early adopters of this edge-based decision-making are those interested in cybersecurity for their systems. For some, it is not enough to hide the internal mechanisms of their power generation and power management, but they want power cloaking as well. They have no interest in sharing any information of the internal workings of their FSGIM-aware facilities. They view the inside of a facility as a discrete security realm. The growing expectations are that a microgrid should cloak power signatures as well as controls. Clearly this model is not accepting of third party monitoring, let alone third party control.

Circling back to the electric vehicle, as a simple cartoon of these issues…

As a mobile control node it needs to understand, about itself, in information model conformant with FSGIM, or the CTS at least. As the EV drives around, it parks within different microgrids, which may opt to not share any information about this control node with the others. We can also imagine a charging station connected directly to the substation, allowing the car to act as a peer control node to the distribution microgrids.

Throughout, this car should be a car as any other. The V2B interactions and the V2G interactions should be the same. In either case, it should be laminar control node, acting autonomously with other nodes, to achieve directives from the lamina above….

The purpose of the Facility Smart Grid Information Model (FSGIM) (ASHRAE/NEMA/ANSI 201) is to prepare building-based systems to talk to the grid. Traditionally, such systems ignored power supply and demand, and simply assume it was there for them. It does not dictate what such a system does with that information. If could be merely to share its upcoming plans with its supplier, or it could negotiate changes to those plans.

The important part is the power *effects* of the activity, and not the details of the activity. There is far too much diversity in building systems and the business activities they support to expose direct control. One of the Regulated Environment facilities that Jim Butler’s company is known for could incur huge losses in dollars, and possible large health and safety risks by simply accepting a HVAC “nudge” from a far-away system operator.

This is exactly the information that an electric vehicle should have about itself. It should internally know those things that FSGIM describes, and use that information to share its upcoming plans with its supplier, or it could negotiate changes to those plans. That negotiation is properly with the facility it is plugged into, and we should not assume that is “the grid.” A car may be in an urban parking lot during the day a home at night, and at charging in an off-grid wilderness retreat on the weekend.