In a recent survey by OMNETRIC Group,1 US utilities executives noted that they would adopt digital technologies such as mobility, social and analytics in pursuit of business priorities such as greater efficiency, optimization or customer engagement—in fact, in that order.
Digital is key to the transformation the utilities industry is undergoing, and it touches every part of the value chain. However, in such a traditionally investment-strapped industry, executives need to be strategic about the application of such technologies, and clearly map investments to priority outcomes and stakeholder, regulator and customer expectations. While grid reliability continues to be a business priority around the world, it is especially critical for US utilities, driving them to closely examine and adopt digital as a way to improve the ability to predict and manage power outages.
The cost of outages is rising, consumer expectations are higher and severe weather in the United States has raised regulatory scrutiny in many regions. Outages particularly erode customer satisfaction and shareholder value. According to some forecasts, the US economy potentially loses more than $150 billion annually to unplanned power outages, even after utilities have implemented an outage management system. To improve grid reliability and increase customer confidence, utilities need to make better use of outage intelligence to enable them to predict and prevent outages, and react faster. Many US utilities still struggle with generating accurate insights for prediction, and there is still room to improve the ability to effectively act on available information and around closing the feedback loop.
When an outage occurs, decisions need to be made quickly. To make those decisions, a utility needs to be able to analyze multiple data streams, act on intelligence, engage customers and allocate resources efficiently. On the question of securing better insights, technology providers have made significant progress with powerful in-memory computing, more cost-effective data storage and access, optimized techniques for batch and stream processing and far better tools for analytics and visualization.
But it is the integration of data that remains the sticking point, particularly in a utility’s asset-intensive environment, where viewing an accurate picture of activity requires bringing together disparate data from information and operations technology systems. In the same recent survey of North American utilities executives, only half see information and operations technology applications merging for the handling of smart data. This hinders utilities from generating better information, including in the context of outage.
To accurately predict outages, utilities need to integrate their asset data, which is held in systems operated by the utility, with weather data and data related to vegetation management provided by third parties. However, in addition to the performance and security considerations related to integrating external data sources with traditional operational data sets, utilities understand that it is simply difficult to achieve. Rather, they should consolidate and aggregate data from operational systems and other sources into a separate data store for analytics.
By seamlessly integrating evolving data sources and applications, such as social media mining and smart grid analytics with core systems (traditional outage management systems such) and with IT systems, utilities can leapfrog limitations imposed by the current generation of utility technologies. The resulting solutions can offer the distribution/outage operator insight simulating the upcoming severe weather event in a geospatial presentation. As a result, utilities can understand the relative impacts and take the necessary proactive measures related to pre-allocation, crew needs, equipment requirements and customer communication. For example, by analyzing weather data, network models and historical feeder performance, analytics can compute the likelihood of a specific feeder failing. Analytics can also help assess outage impacts and prioritize outage-related work based on the effect on reliability measures such as SAIDI.
By integrating information and operational applications across technology, people and processes, utilities can use predictive analytics to achieve better awareness, improved assessment and better preparedness for power outages. And that sounds like a more effective way for utilities to no longer simply weather the storm, but to get ahead of it.
Authored in collaboration with OMNETRIC Group, a joint venture between Accenture and Siemens, dedicated to the development and delivery of integrated smart grid solutions.
1 Source: OMNETRIC Group-sponsored survey of North American utilities executives with more than 500,000 customers: “How does the smart utility platform look today?” 7.29 – 9.30.2015, in conjunction with Electric Energy online and Desert Sky Group LLC.