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IBM > Case Studies > Oncor delivers customer benefits through an integrated smart grid
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Oncor delivers customer benefits through an integrated smart grid

Technology Category
  • Infrastructure as a Service (IaaS) - Cloud Computing
  • Infrastructure as a Service (IaaS) - Cloud Storage Services
  • Infrastructure as a Service (IaaS) - Hybrid Cloud
Applicable Industries
  • Utilities
Applicable Functions
  • Logistics & Transportation
  • Maintenance
Use Cases
  • Predictive Maintenance
  • Real-Time Location System (RTLS)
  • Remote Asset Management
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
  • Testing & Certification
The Challenge
Oncor Electric Delivery Company LLC, the largest regulated electric distribution and transmission company in Texas, aimed to modernize its operations to proactively manage outages and meet more sophisticated energy needs. The company wanted to develop an interactive power grid that records energy usage data from homes and businesses every 15 minutes and transforms that data into actionable decision-making intelligence for energy providers and consumers alike. The goal was to be able to accurately respond to outages and restore services without requiring any contact from the customer. In many cases, outages occur during the middle of a workday, when nobody’s home. Oncor would experience a significant spike in customer calls and service orders after working hours, which meant customers would unnecessarily end up in a long queue waiting for service.
About The Customer
Oncor Electric Delivery Company LLC (Oncor) is the largest regulated electric distribution and transmission company in Texas and the sixth largest in the United States. The company serves more than three million customers and covers a service area of nearly 117,000 square miles. Oncor began its advanced meter program in 2008 and recently completed the deployment of 3.25 million meters serving the customers of north and central Texas. Customers in Oncor’s service territory showed last year during the company’s biggest energy saver contest that by using the information from Oncor’s advanced meter they could reduce their electric usage and bills by 25 percent or more.
The Solution
Oncor partnered with IBM and IBM Business Partners Ecologic Analytics, along with Advance Metering System provider and IBM Business Partner Landis + Gyr, to implement a Meter Data Management System (MDMS) and deploy smart meters across its grid. The MDMS automatically receives and transmits energy consumption information captured by meters in 15-minute increments. IBM Global Business Services (GBS) provided the solution architecture as well as integration services, connecting the MDMS with a third-party outage management system (OMS) that had been developed in parallel with the MDMS. The GBS team also provided design, development and testing for the range of other components within Oncor’s new smart-grid system. The solution utilizes smart electricity meters to accurately measure the electricity usage of a household in 15-minute increments, providing data inputs essential for consumption insights.
Operational Impact
  • The combined MDMS and OMS system improves overall reliability throughout the grid, providing Oncor with the ability to remotely diagnose potential problems in the grid and fix them before they create an outage.
  • Oncor has a real-time, enhanced view of the extent of that outage. That enhanced visibility, along with an ability to formulate the fastest and most resource-efficient response strategy in the event of severe weather event, add tremendous efficiency to Oncor’s system.
  • Two-way communications capabilities help minimize the number and length of power outages by providing Oncor with an ongoing picture of the overall system and potential trouble spots.
Quantitative Benefit
  • In responding to one such weather event, Oncor—acting on specific, accurate and actionable information—was able to reduce the number of maintenance trucks it dispatched by 40 percent.
  • Of the first 1,482 events identified after deploying the system, 13 percent were not actual outages; they were early-stage issues that eventually would have caused an outage.

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