Radio Systems for a Future Grid: Fast, Clear, and a Quick ResponseBack to Top
Electric utilities are preparing for a future grid that will look much different from what we have today. No matter the technology or infrastructure, communication systems will be front and center in that preparation. As such, with distribution grids being spread across large distances, utilities will continue to rely on radios to communicate with their smart, remote devices.
Many utilities use a radio system to support various applications, such as advanced-metering infrastructure (AMI) or supervisory control and data acquisition (SCADA) systems. These systems were built for a specific purpose, and their network design balanced cost, range, tolerance for missed messages, and speed. As new grids develop, slow, one-way radio transmission will not keep up with the need for high-speed grid control and 24/7 connectivity.
When comparing AMI, SCADA, and self-healing systems, only a self-healing system must recognize a grid’s condition and direction and respond with control commands in seconds, in the context of grid applications.
To simplify the explanation of radio networks, imagine a couple is talking in a quiet car. They can hear everything being said. However, if they are talking in a noisy car, they may have to move closer (less range) and speak slower to hear each other or risk information being lost.
Higher talking rates means more information may be passed along, but comprehension lessens in noisy environments. In “quiet” communications networks, such as on a licensed band, radios hear and efficiently decode the data quickly and accurately. However, in a noisy environment, the data rate falls because transmissions are repeated, so less information is conveyed in the same amount of time.
Information importance also plays a role. For example, if the person speaking in the noisy car is providing the current temperature, the listener can afford to miss what was said or wait for the person to repeat what was said. But if the person was warning of a road hazard or sever weather just ahead, robust, two-way communications is required for immediate action.
AMI networks were designed and built to support primarily limited amounts of meter-generated data traveling in one direction. If the meter data are lost anywhere during this transit period, the AMI application can easily ask the meter to re-generate the data. The “conversation” is one way, with little concern whether a thing or two is missed because the system can always catch up.
SCADA networks historically have been constructed with robust, “quiet” (licensed) 900-MHz, narrow channel radio platforms. Capacity on this “skinny” channel is limited to only necessary data, such as open/closed status, fault indication, or system information, such as voltage or current. Thousands of devices are polled, and data are delivered to a central control center, which is responded to with manual actions.
AMI and SCADA applications operate effectively on communication networks with lower capacities (bandwidth) and a higher tolerance for delays or loss in data being received.
Devices on self-healing systems, meanwhile, require a steady stream of real-time feeder conditions, so grid-reconfiguration operations may complete in seconds or less. They must account for the direction of current flow from multiple sources of generation, multiple current flows caused by faults, and for other items such as critical loads and feeder capacities. To accomplish this, the communication system must be clear (shorter range), fast (low latency) and robust (low/no message loss).
Grids tomorrow will need to talk fast, clearly receive communications, and act quickly. A grid application’s speed and tolerance for lost messages drove the design of a communication network built for a different purpose. The radio systems in place today for other applications may not support your future automation efforts.
I’d be interested in hearing your thoughts on the role radio communications will play to accommodate the needs of the future grid.
十二月 13, 2017