Silver Spring Networks solution delivers smart grid-based outage restoration
The offering leverages data from the smart grid network to deliver reliable and actionable information to existing outage management systems
Redwood City, Calif., November 17, 2011 — Silver Spring Networks, a leading smart grid networking platform and solutions provider, announced enhancements to its UtilityIQ Outage Detection System software to help utilities identify and respond more efficiently, safely, and faster to system outages.
The offering leverages data from the smart grid network to deliver reliable and actionable information to existing outage management systems. Together, the combined intelligence designed for both system outages and simulation tools enable utilities to improve service reliability for their customers and reduce operational costs related to outage preparedness and response.
UtilityIQ Outage Detection System addresses the challenges of integrating smart grid data into utilities' outage management systems. A smart grid network provides a tremendous amount of data during an outage that traditional OMS software should leverage. UtilityIQ ODS interprets this data more effectively so that utilities can quickly identify remaining outages, reduce operating costs and update customers when power has been restored.
Along with the software update, Silver Spring is also delivering new outage simulation tools and services that allow utilities to simulate an outage of any size, from a single customer to a large feeder with thousands of homes and businesses. The new outage simulation capabilities enable utilities to understand network operations during an outage, requirements for system integration, and changes needed in business processes before an actual outage event occurs. As a result, utilities can maximize their operational savings and improve reliability while also building closer and more responsive relationships with their customers.
A Silver Spring utility client has leveraged the outage simulation and testing capabilities to better understand smart grid-based outage detection. The collaboration documented the behavior of the utility's smart grid network during simulated outages and demonstrated that the network delivered outage data effectively.
The utility determined that for each outage scenario, the network delivered more than sufficient outage data to appropriately locate and scope the outage. The testing also highlighted the need for filtering software between the smart grid network and the utility's OMS.