In the Netherlands, Alliander NV is a powerhouse power distribution and electric generating utility that serves about 3.5 million customers. It has over 85,000 kilometers (52,817 miles) of electric lines and 37,000 kilometers (22,990 miles) of natural gas pipeline.
One major problem Alliander faced was the amount of data it collected. The data was so vast and disorganized that forecasts took nearly ten weeks to do – so the company did them only once every year.
Alliander decided to automate the data collection and extraction process by using custom algorithms to find and flag outliers and load shifts in the measurement data. Simultaneously, the company developed an intuitive user interface that made it easier to analyze and interpret data for calculating peak load needs.
Alliander also wanted to empower its customers so they could employ real-time data to make informed choices about their energy use. Being able to aggregate customer usage profiles enabled Alliander to forecast and better balance energy demand and supply requirements.
Already drowning in data, some relevant and some not, Alliander needed another 22,000 sensors to generate more than 3 billion records a year. Clearly, such a volume cannot be sorted and analyzed manually. The company chose to automate the process, and now it can deliver a business forecast in just 3 days – a 95 percent reduction in time. Simultaneously, the company saw a 60 percent reduction in IT resource and design costs.
Alliander was very satisfied with the results, but how did its customers feel? They were pleased as well. Having real-time information on how they used energy and how energy costs differ by time of day, customers could alter their consumption profiles to save costs.
“At Alliander, customers who have real-time access to data about their energy usage have reduced energy bills by 10 to 20 percent per month. We need to help our customers at the household level to use their energy much more wisely.”
The new automated system has other benefits as well. Customers are helping the energy company reduce carbon emissions. Robin Hagemans, manager of grid information and control at Alliander, discussed the benefits of data collection:
…“ the utility’s aggressive goals of reducing carbon emissions by 20 percent by 2020 and boosting renewable energy use by the same amount. That goal can be reached only if consumers become more active participants in the process of conserving energy and balancing their energy use.”
The new data management system helps this happen.
The new data analytics and data collection systems and processes Alliander put in place were a success. Since deployment, the company has implemented 85 new business models to analyze operations.
Alliander CEO Peter Molengraaf notes:
…“[We are] making the right choices for the future of our networks. Whilst continuing to maintain and replace our existing electricity and gas networks, we are also preparing for the radically changing energy landscape of the future. The impact of the energy transition is becoming increasingly visible. For instance, more and more renewable energy is being generated, and this will have major consequences for the networks in the long term.”
A 2013 survey by Tata Consultancy Services found that both utilities and energy/resources companies have the highest expectations for generating returns on investment in Big Data. The Alliander case shows how this can happen.
To learn more about digital transformation for utilities, click here.