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Intelligence at the Edge

Energy control networking applications require intelligent decision making to deal with failure conditions and other anomalous situations. The traditional approach to decision making is to concentrate all intelligence and decision making at a central control center, and to view the distributed elements of the control network simply as dumb producers, consumers, and transporters of raw data. However, in our view, distributing and pushing intelligence and decision making to the edge is required to transform a basic communications network into a multi-application control network that is more reliable, scalable, and responsive to local conditions.

Improving Reliability

A failure condition may sometimes be accompanied by a lack of communication with the centralized decision-making authority. So field devices and systems at the edge must be intelligent enough to survive autonomously. In the electricity grid, when failures occur, they sometimes lead to cascading failures that impact many users and cost the utility a great deal of time and money. Without intelligence in the low voltage distribution network (the edge of the grid), it is difficult to pinpoint which pieces of equipment have failed and what the sequence of failure actually was. This makes root cause analysis and ultimately repair difficult and time-consuming. Increasing the numbers and types of sensors deployed around the grid, collecting, processing, and correlating the data locally, and sending up only actionable data when required enables a new level of reliability for the grid.

Improving Responsiveness

In the near future, a utility may have 20% or more of its generation capacity on the distribution network. Such distributed generation capacity could be highly variable. The utility will need the ability to reach into and control these distributed assets. Similarly, the demand side of the grid will change rapidly as new variable loads such as electric vehicles become more common. The utility may need to balance and stagger these loads on a transformer by transformer basis. Making quick decisions on balancing variable distributed generation and load will also require Intelligence at the edge. Moreover, the edge of the grid is only going to become more heterogeneous with devices such as meters, re-closers, capacitor banks, PV inverters, street lights, buildings and homes participating in demand response programs, etc.

Echelon’s Open Standard, Multi-Application Approach

The Echelon approach is to unify these disparate systems at the field level using edge control nodes that unify and correlate local data, and take action without always needing to communicate to the data center. This allows many actions to take place in parallel without the need to scale WAN communications and data center capacity.

System unification results from data sharing. We have invented a local data access model that provides for data sharing among applications in the field. That means the system can react rapidly without data center involvement.

Our intelligent control nodes  use a Linux OS to support an open application and driver framework.  This allows for the adding of focused functionality on an as-needed basis, providing utilities with flexibility and distributed intelligence that can be adapted to emerging LV grid needs.

Dramatic Changes in Supply and Demand

Today when people come home and begin preparing the evening meal, it causes a spike in electricity usage. Soon, they will come home, plug-in their cars, and then begin preparing their evening meal. Balancing and staggering these loads is best handled locally by a distributed intelligent controller.

Distributed Intelligence

The Internet heals itself by routing around faults. But, when failures occur on the unintelligent electric grid, they can cascade and impact entire cities and thousands of users. Distributed intelligence is the answer.