Despite the vast amount of data collected by the industry in recent years, less than 25% is ever processed. This is often due to enterprises lacking the necessary infrastructure to effectively utilize their data. In this article, we will explore the digital journey of a Power Grid operator, from its initial stages till deploying machine learning at the edge.
Recently, we published an article about the complexities industrial companies face when striving for full IT-OT convergence, enabling them to become truly data-driven enterprises. In that article, we emphasized the importance of establishing a secondary data infrastructure, parallel to the conventional one consisting of SCADAs, Historians, and the like, and highlighted the benefits of having it on-premise at the Edge to address cost, privacy, and latency concerns. Now, we believe it’s time to bring this discussion to life by illustrating how one of our customers, Cuerva, embarked on this journey alongside Barbara.
Cuerva, an energy sector company started providing electricity to rural areas in the province of Granada through renewable hydraulic generation in 1962. Today, it boasts nine main lines of business spanning the entire energy value chain, from generation and distribution to retail and customer energy services across industries and public services. The company is actively implementing a comprehensive digitalization initiative to improve supply quality and provide innovative high-value services.
Learn more: The IT-OT convergence (part I): Syncing the Data Journey and the Digitization of Infrastructures
My first meeting with Cuerva’s head of innovation was in 2018, a conversation that is still very present in my memory. Alberto articulated the challenges they faced despite a substantial rollout of control systems across the network, from substations to end-users. The absence of continuous measurements made them lag behind their objective of enhancing supply quality. Shortly thereafter, Cuerva adopted Barbara’s technology, albeit not without formidable challenges: Each secondary substation housed numerous devices, utilizing various communication systems, including intelligent electronic devices such as line cells breakers and voltage supervisors, alongside smart meters and sensors.
Deploying a cost-effective edge node with Barbara’s Edge Platform per substation enabled Cuerva to gather real-time data from every piece of equipment via ready-to-use data connectors for Smart Grid, with a variety of out-of-the-box protocol connectors such as Modbus RTU, IEC-104, IEC-61850, SOAP, OPC-UA, and FTP that made the data ready to consume via a pub-sub data broker. This opened a new era of possibilities for Cuerva’s digital transformation team, transitioning from a digitally disconnected infrastructure to an open infrastructure—the Connected Edge—that could provide real-time insights into any asset.
However, this was just the beginning. According to NVIDIA, less than 25% of collected edge data in organizations is being exploited.
To ensure data accessibility and visibility, Cuerva proceeded to create substation digital twins, offering a comprehensive virtual representation of each site. Visualizing real-time raw and processed data accurately through synoptics and tables empowered Cuerva to identify potential issues proactively, optimize maintenance schedules, minimize downtimes, simulate new scenarios, and troubleshoot historical data.
Barbara’s Marketplace edge building blocks, particularly databases, and dashboard tools, facilitated the creation of digital twins, remotely deployable, orchestrated, and scalable at each substation. By executing digital twins at the Edge nodes, closer to the data source, Cuerva reduced latency and significantly improved response times, laying the foundation for the next phase—the Intelligent Edge.
With digital twins monitoring every aspect of operations, Cuerva intensified its data exploitation strategy, focusing on deeper data analysis using AI-oriented stacks.
Energy flexibility emerged as the low-hanging fruit use case, given the proliferation of renewable energy sources and distributed energy resources at the distribution level.
Cuerva implemented a flexibility algorithm leveraging Artificial Intelligence to anticipate and address potential overvoltage and congestion events, ensuring automated decision-making and agile responses to evolving grid conditions. Barbara’s Platform Container and Model Orchestration capabilities played a pivotal role, facilitating seamless integration of trained AI models into containerized environments deployed, monitored, and managed in hundreds of edge nodes remotely, ensuring a cost-efficient deployment and future scalability.
I must emphasize the importance of Cuerva’s vision in integrating data-driven engineers and software developers into their OT teams. IT-OT convergence must transcend technical integration and extend to human integration—hiring digitally adept individuals is imperative for digital success.
Personally, it has been an honor and a profound learning experience witnessing the rapid digital transformation of a century-old company, transitioning from data darkness to a visionary Intelligent Edge capable of predicting outcomes over complex systems. Barbara’s Edge technology is playing a pivotal role in this revolution.
Data teams need tools to help them orchestrate all the applications and even AI algorithms that they will eventually deploy in distributed locations.
And Infrastructure teams need tools to help them transition their infrastructure from being isolated, to becoming truly intelligent. And this is exactly what Barbara does.