Eversource Energy pioneered a series of significant advancements in the detection and management of leaks within High Pressure Fluid Filled (HPFF) transmission systems, marking a substantial leap forward in operational efficiency and environmental protection. The presenters in this session will discuss how they developed innovative analytics methods and implemented advanced data visualization and machine learning (ML) techniques. Using these tools and methodologies, Eversource’s engineers achieved a remarkable reduction in leak detection times—from the traditional span of two to three days to within four hours. This acceleration in detection capability demonstrates not only Eversource’s leading role in developing data-driven infrastructure management but its commitment to environmental protection and sustainability. At the heart of the achievements is the novel application of pressure variation data to calculate pump starts. They also introduced a ML-based method to dynamically adjust alert thresholds and created a powerful cross-check tool—a PowerBI dashboard that integrates tank level data, temperature readings, and load change information, providing operation engineers with a 360-degree view of system operation. Presenters in this session will provide meaningful details about this data analytics based use case.

As the electric utility industry enters a new era of AI-powered smart meters where the benefits of an interconnected, customer-centric grid can be fully realized, utilities must marry intelligent, AI-enabled software with utility-grade hardware. In this session, presenters will share detail about the recent Revelo meter rollout in New York, digging into the challenges they collectively faced in meeting the state’s reliability and security standards, and the opportunities their intelligent meters unlock for the region. They’ll also talk about the value of embedding AI and distributed computing software into meters to improve efficiency, enhance reliability and security, and support demand response. This use case involved rethinking the way customer data is used by engaging customers and leading them through this energy transition. The project also required National Grid to think about the intersection of customer and grid benefits at the edge and how the next era of grid edge/AMI 2.0 technology, partly driven by AI, will be different from AMI 1.0.

To make informed decisions across various business units, it is imperative that data coming from a utility’s thousands of assets be readily accessible and presented in a clear, actionable manner. In this session, attendees will hear two separate presentations providing examples and strategies that helped utilities get the most from their data analytics programs and processes. The first presenter will discuss a strategic fusion of disparate data, spatial intelligence, and artificial intelligence to prioritize cost-effective remedies for enhancing grid reliability. Attendees will learn how combining these technologies allows utilities to prioritize asset improvements, equipment modernization, network optimization, and vegetation management. The presenter will explain how this approach enables utilities to navigate the complex grid improvement landscape with informed decision-making capabilities that benefit both the utility and consumers. The second presenter will reveal how a utility used IIoT sensors, distributed energy resources (DER) and behind-the-meter (BTM) data to exponentially grow data volume, velocity and variety, providing users with the right tools to perform ad-hoc analyses within the same environment. This capability also allows users to gain valuable insights on how various factors (either time-based or business processes-based) affect grid performance. The speakers also will explore available technologies and innovations, such as geospatial analytics, predictive/prescriptive analytics, AI, generative AI, machine learning, and cloud computing, as well as provide examples illustrating IT/OT convergence.

Artificial intelligence (AI) is revolutionizing industries, including electric utilities. In 2023, BloombergNEF tracked a record-breaking number of 91 AI activities in the global power sector, doubling the number of such activities in 2022. In this session we'll discuss which utilities are applying AI models and what they’re using it for, providing specific examples of utility tools and innovative startups. with applications ranging from power system operations and maintenance to grid planning and cybersecurity. Attendees will hear about the role of advanced AI applications, like generative AI and deep learning, and how they play into the power sector. They’ll learn that the focus of AI applications in the power sector has shifted from generation to the downstream power value chain, in support of increasingly complex grids, distributed energy resource integration and retail programs. Lastly, this talk will also explore what’s next, describing potential growth areas for AI applications, including deep learning algorithms and generative AI.

Energy 4.0 is the electric power industry’s version of the fourth Industrial Revolution, also known as Industry 4.0. The Industrial Internet of Things (IIoT), machine learning, and cloud computing are among the technologies that make up Energy 4.0, and despite the growing usage in other industries, some electric utilities are still reluctant to adopt these technologies. This presentation will explain how Southern Company is deploying Energy 4.0 sensors to monitor their 1,100+ transformer fleet. Presenters will outline the security architecture and the benefits that Southern Company realized by using real-time sensors to move to a condition-based maintenance program. This includes revealing the technology used in Energy 4.0 sensors and the supporting architecture, and how the data and access to the network is safely managed. In addition, they’ll explain how 24/7 monitoring of critical equipment and assets increased safety and reliability and allowed Southern Company to implement a condition-based maintenance program and extend maintenance cycles by 200%.