Legal guidelines, cost pressure and growing competition are driving the digitalization of the energy sector. Three typical applications illustrate how power companies are benefiting from IoT sensor technology and big data analytics.
Digitalizing the energy sector with IoT and big data
Lufthansa Industry Solutions was recognized as a relevant player in the digital factory in the three use case clusters “Predictive Analytics & Maintenance”, “Traceability” and “Asset & Plant Performance Monitoring” by teknowlogy. According to teknowlogy, the leading independent European market analysis and consulting company for the IT industry, LHIND has proven to be able to address all related use cases in these categories.
1. Extending the life span of underground cables with intelligent sensors
Intact power lines are essential to reliable power distribution. For grid operators, replacing defective line sections often means downtime and high costs. Methods and processes that extend the life span of power cables are therefore a relevant method of optimization with tremendous savings potential for grid operators. One option is to constantly check the temperature of underground cables using intelligent sensors, thereby maintaining control over their operating temperature. The distribution system operator can take immediate countermeasures if the cable temperature rises too sharply. This procedure extends the life span of power cables in the long term and reduces replacement costs.
Reliable transmission technology under a wide range of environmental conditions, along with cables equipped with sensors, is essential to the use of intelligent IoT sensor technology. In connection with mathematical models based on big data technology, the data gathered can then be used to optimize the flow of current, allowing power companies to take countermeasures and reduce flow rates or redistribute loads, for example. They can also use the technology to remotely read power, gas and water meters and control switching devices.
2. Reducing load intervention in the power grid
Feeding renewable energy into the power grid and incomplete information about individual power generation facilities can lead to volatility in load flow that is difficult to forecast. As a result, transmission network operators intervene manually in generation by power plants and decentralized generation facilities to protect line sections, causing hundreds of millions’ worth in costs every year, according to the German Association of Energy and Water Industries.
Big data analytics can be used to generate new findings about how individual aspects influence each other and develop forecasts if power companies merge various sources of data, such as the input-output curves of individual transformers, output measurements of wind and solar power farms and weather forecasts. These findings provide transparency as to how individual plants and generation facilities influence loads on substation nodes, allowing companies to take steps at an early stage and prevent costly redispatch measures in the event of an overload.
3. Improving customer satisfaction with big data analytics
Because utility companies face fierce competition, customer loyalty is an important issue for them. The constant measurement and analysis of needs, desires and satisfaction allows them to strengthen customer satisfaction and loyalty in the long term through individual services, special offers and other measures.
Power companies can employ big data analytics to analyze user behavior on their websites and portals while also gauging opinions about their own brand and products in social media. Merging this data with further findings from online surveys, for example, gives them insights into the needs of their customers and allows them to more precisely target their marketing and sales activities. Improving the way they communicate with customers then makes it possible for power companies to foster lasting customer loyalty.