Electricity generation and the analytic edge

Posted on: June 21, 2017 by Franck Freycenon

Further down the power value chain, we’re getting used the idea that digital and progress are inextricably linked – from balancing at local grid level to the delivery of personalized services to domestic and business consumers.

Well it’s time to head back to the start of the chain, and ask how digital transformation and specifically, data analytics can positively impact practices in power generation.

Over the years, established power companies have become experts in the analysis needed to build actionable forecasts for usage and generation. Forecasting remains a key business requirement, but it just got a whole lot more complicated, with the need to open out analysis to include a whole new landscape of renewables and third-party data sources.

In many ways, this also shows the increased need for utilities to extend engineering analysis into the labyrinth of available business data. Interpreting economic and industrial performance data, for example, now becomes a core skill in energy forecasting.

Analytics for operation and prediction

Whether in nuclear, gas or hydro-electrics, we are seeing an increased understanding of the value of realtime analytics in operational decision-making.

This is most clearly seen in the shift away from strict book-schedules in maintenance and renewal in the generating environment.

Smart analytics mean the decisions on where, when and how to intervene no longer need to be made according to rigid maintenance timetables.

New analytics mean operators can look at their service schedules in ways that increase agility without in any way compromising either safety or continuity.

When reliable analytics make it possible to move away from rigid scheduling, power companies can make decisions according to optimized business value. Given the rise in local renewables, for example, is there a case to be made for bringing decommissioning forward on assets nearing end-of-life, rather than continuing to pay for ongoing service and maintenance?

Business and operational advantage

Power companies are already starting to exploit realtime data analytics in pursuit of business and operational advantage.

In a commissioned study conducted by Forrester Consulting on behalf of Atos, 31% of respondents from the utility sectors are already using data analytics in the forecast of demand and production while 47% are planning to do so in the next 12 months.Regarding operational efficiency, the figures are similar. 29% of respondents are already using analytics to drive higher operational efficiency with 46% planning to do so in the next year.

The same interest is also evident in the use of analytics in predictive maintenance, with 24% already active and 43% planning to adopt within the next twelve months.

No instant answers

Every power company is under immense pressure to deliver in an increasingly volatile business landscape.

It’s natural to make the link between better analytics and improved business and operational performance.

But as with all developments in information technology, spending the money is no guarantee of business return. To get the benefits of applied data analytics in power generation, it’s essential that the data scientists and utility company experts establish a common language and a common frame of reference.

At Atos, we are taking the opportunity to build on the practical initiatives undertaken with our energy and utility clients in recent years. We have, for example, put considerable investment into the integration of operational and information technologies – and from here, it is a short step to audit and assess data assets in terms of their potential impact on operational improvement.

With new perspectives supported by previously hidden data, we are making it possible for utility companies to ask questions that perhaps, would not have been considered in the past. In maintenance and renewal programs, for example, we can now place greater emphasis on how to optimize timing for intervention, and indeed to examine the implications of bypassing intervention altogether.

Moving forwards

Professionals in electricity generation have developed far wider business perspectives in recent years. The role of the engineer is as important as it has ever been, but planning, investment and operational scheduling are all integral to effective business behavior.

The wider landscape is more dynamic than it has ever been, and in making sense of this bigger picture, the practical application of data analytics for improving energy production forecast in a volatile market or for optimizing maintenance scenarios becomes an essential skill.

If you’d be interested in learning more about our offering for data-driven insights, Atos Codex, or in participating in a data analytics discovery workshop, do get in touch.

Share this blog article

  • Share on Linked In

About Franck Freycenon
Head of E&U Solution, Atos and member of the Scientific Community
Franck Freycenon began his career in the IT sector focusing on improving performance of IT Systems of Telecoms Operators. He then spent 10 years as Account Manager at Orange Business Services for industrial and banking clients. In 2011, he joined a consultancy and digital agency where he managed the development of the relationship with the energy companies and their ecosystems. Franck joined Atos early 2015. Today, his mission is to enhance the digital transformation in the energy sector and Atos value proposition on this topic. As such, he is responsible for business development of the platform for new services in the energy, based on Atos global platform initiative in Big Data.

Follow or contact Franck