Electricity generation and the analytic edge
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.
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.