Today’s SCADA / ADMS tools for real-time power flow are vectored around centralized generation, transmission and the high/medium voltage distribution segment in the energy network. These systems are designed for managing thousands or tens of thousands of data points. However, today’s disruption is occurring in the low voltage portion of the network at, or behind, the meter, where the scale is millions of nodes. Real-time visibility and intelligence all the way down to the service points at the home or business is required to bridge this gap.
Itron is focused on providing solutions that operate where this disruption is taking place: at the grid edge, where the distribution network meets new technologies at the customer premises.
Foundational to the Grid Edge Intelligence is distributed intelligence (DI), which is a combination of secure service point edge computing, access to real-time data, local coordination, and lifecycle management and analytics capabilities.
The principle is that analysis and decision making can occur at the edge of the network, closest to the problem, where visibility is highest, scale is manageable, and latency is lowest. Analysis and decision making can be made intra device (within a device) or inter device (peer-to-peer across a collection of local devices) including smart meters and a range of devices in the home.
Secure Service Point Edge Computing
Distributed intelligence agents, also known as distributed intelligence “apps”, analyse data in real time and operate within secure Linux containers that fully isolate DI app operation from metrology and billing register functions.
This distributed intelligence platform affords flexible integration with a range of grid edge intelligent devices, including smart meters, grid devices, customer devices, and customer facing applications.
Access to Real-Time Data
Distributed intelligence enables solution intelligence to be brought closer to the situational environment, affording efficient access to high fidelity actionable data and providing a range of benefits:
- Processing at greater speeds and volumes than otherwise possible, leading to greater action-led results in real time
- Reduction in latency
- Efficient WAN utilization
- Improved awareness
- Higher accuracy
- Real-time event detection
Use cases range from use of 1 second data for measurement of hosting capacity and detection of, poor electrical connections in low voltage distribution use of 32KHz waveform data for enhanced accuracy of premise energy load disaggregation and detection and location of arcing and early equipment failure in the low voltage and medium voltage distribution networks.
Lifecycle Management and Analytics Capabilities
Equally important to distributed intelligence as edge data and processing capability is the provision of a modular head-end software platform that incorporates comprehensive app lifecycle management, enabling the development, testing, deployment, upgrading, configuration, monitoring and secure provisioning of apps for utilities and partners.
The platform also must provide utilities and third-party app developers with a suite of capabilities to enable agile app development – including purpose-built local and cloud development environments, Software Development Kit (SDK), Data Simulators and Tooling, and the ability to allocate, enable, and monitor app download of their applications by utilities.
Importantly, the development and deployment of distributed intelligence apps should be conducted completely independently, without requirement for code integration with the distributed intelligence application platform or other applications and without requirement for regression testing with the head-end software platform, the edge devices, or other applications on the edge devices. This is the only practical way for continuous app innovation to occur over the life of the investment, as it eliminates the bottlenecks and complex schedule coordination that would be required with any form of code integration its associated long and costly regression testing by both vendor and the utility.
DI Apps
DI Apps are targeted at specific use cases that present common challenges within the utility industry. To that end, current distributed intelligence apps typically fall into three themes:
- Increase grid resiliency and reliability by managing rapidly changing conditions, such as voltage/frequency values
- Engage consumers as partners to address grid issues related to DERs
- Integrate renewables and enable responsible energy management for a sustainable future
Each app has its own value proposition and is purpose built to support that value proposition by improving customer safety, reducing operating expenses, increasing capital asset lifetime and enhancing operational efficiency. And when combined, these apps greatly increase the overall value stack of the DI investment.
Over-the-air secure installation of distributed intelligence apps enables provisioning to individual customers and devices, groups of devices or an entire device footprint. In this manner, incremental functionality for the overall solution is simply a function of the set of deployed distributed intelligence apps.
Investing in Grid Edge Intelligence can close the awareness and control gap that exists today with new ways of managing distribution networks and customer relationships. Utilities will gain greater visibility and control at the edge by connecting, detecting, operating and controlling devices and in doing so, utilities can deliver an efficient, optimized and smarter grid for the communities they serve.
Later in this set of articles, we will provide more information regarding distributed intelligence drivers and value, Itron operational experience, and applicability for the Norwegian Energy Market.
https://emea.itron.com/en/w/elfack-2025
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