Lynne Kiesling
One of the recurring themes here at KP is that smart grid technology makes it possible to harness distributed intelligence in the electric power network, and when you couple that network of distributed intelligence with a decentralized flow of information (such as price signals and market processes), you can get reliability through decentralized coordination instead of through the imposition of hierarchical control. Moreover, because individual agents are voluntarily coordinating instead of being forced into a control structure, it’s likely that decentralized coordination generates more social surplus, increases total welfare, etc.
Here’s an exciting new building intelligence product than can start to deliver on that vision. As described in this CNet article, Regen Energy is producing wireless device controllers that, when networked, can adjust device energy use based on individualized, decentralized trigger settings:
Rather than send specific instructions to each controller, the system sets parameters, such as cutting power consumption at peak times during the day. The EnviroGrid controllers individually make adjustments to the chillers, such as turning them off for 10 minutes out of an hour, which in aggregate hit the overall goal, Kerbel explained.
“We tell the controllers, ‘Here are some rough guidelines for upper and lower limits’ (of energy consumption) and they do the work,” he said. “Right now, the way to do this sort of thing is to get a building engineer who does an analysis and then get a software programmer to write custom code.”
The technology is a prime example of how computer and networking technologies are quickly being applied to the energy industry to improve efficiency.
The decentralized decision-making approach is also a break with tradition central building management systems where building equipment is typically purchased for peak power demands.
Yes, indeed it is! It’s also a break with the top-down control hierarchy mindset that pervades the engineering, the regulatory, and the business model cultures in this industry.
Regen refers to their technology as using “swarm logic”, based on the complex adaptive systems ideas of self-organization and emergent order as seen in bees, ants, and other large populations that coordinate with the use of distributed intelligence, rules for responding to observed behavior, and limited hierarchy. As I argue in my book, based on the work of Hayek and other complexity-oriented economists, this kind of self-organization and emergent order also characterizes healthy, competitive markets.
A recent Technology Review article also gives a nice overview of the technology and the idea of swarm logic:
“Every node thinks for itself,” says Mark Kerbel, cofounder and chief executive officer of REGEN Energy, which invented the proprietary algorithm embedded in each device. Before making a decision, he explains, a node will consider the circumstances of other nodes in its network. For example, if a refrigerator needs to cycle on to maintain a minimum temperature, a node connected to a fan or pump will stay off for an extra 15 minutes to keep power use below a certain threshold. “The devices must satisfy the local restraint but simultaneously satisfy the system objective,” says Kerbel, adding that a typical building might have between 10 and 40 controllers working together in a single “hive.” The devices are simple and quick to install and, because there’s no human intervention, require no special training to use.
It’s a dramatic departure from the top-down command model associated with current building-automation systems. Some researchers say that the decentralized approach to energy management offers a cheaper, more effective way to manage supply and demand in a delicately balanced electricity system. Indeed, some believe that it could be an early prescription for an emerging smart grid.
In this case the only hierarchy imposed from the system controller is some global objective function, such as minimizing overall energy use. Each device controller is programmed with trigger points, such as total system use or frequency level, and is then also programmed to take an action once it is at the trigger; note that this trigger is also likely to be more granular and smaller-scale than, say, a “turn off” command, so the functionality of the device to the consumer will only be minimally affected. In aggregate, the individual actions of the distributed responsive devices achieve the system objective without impairing functionality or consumer value in any meaningful way. And using this swarm logic and self-organization model to harness distributed intelligence is also saving consumers money:
Tests have so far demonstrated that building owners–of hospitals, hotels, shopping malls, factories, and other large facilities–could save as much as 30 percent on their peak-demand charges. Those savings, REGEN claims, more than cover the cost of renting the devices, which is an option for major electricity consumers reluctant to buy the technology up front. If the devices are purchased, the payback is less than three years, says Kerbel.
Thus far it sounds like Regen’s device controllers are responding to data on physical conditions. But it’s not a difficult stretch to enable the distributed controllers to respond to price signals, which would make them transactive. Different types of devices could have different trigger price settings, reflecting the preferences of the building owner. Once they do that, Regen would probably pass my transactive test.
