DSO

Orchestrating the DSO Transition: The Role of Operational Data Coordination

Inkwelldata

24 Mar 2026 • 3 min read

The transition from Distribution Network Operator (DNO) to Distribution System Operator (DSO) represents a fundamental structural shift in the UK energy landscape.

Driven by rapid decentralisation and the proliferation of low-carbon technologies, electricity networks are moving towards a model defined by dynamic flexibility and high-volume, distributed visibility.

Project Harmony—the industry-led initiative developed by LCP Delta with Ofgem oversight—brings this into sharper focus. It identifies flexibility coordination not as a peripheral activity, but as a core operational requirement.

        As distributed energy resources (DERs) scale, the ability to coordinate these assets effectively becomes central to maintaining system stability.
    

The Coordination Gap in Digital Infrastructure

The UK's DNOs have established a strong digital foundation. Advanced Distribution Management Systems (ADMS) provide real-time grid control; historians capture high-resolution telemetry; and cloud-native platforms support advanced analytics and AI-driven insight.

Yet a coordination gap remains. While each system performs effectively within its own domain, a typical flexibility event spans several layers: a commercial dispatch instruction, a physical asset response, and a resulting change in network conditions.

        The challenge is not simply one of data exchange, but of consistency. These perspectives must be aligned in time and interpreted coherently across different data models and temporal resolutions.
    

Complexity at the Low-Voltage Edge

This requirement becomes more acute at the low-voltage (LV) edge.

Networks are now integrating large volumes of data from EV chargers, heat pumps and smart meters—environments that are inherently multi-vendor and heterogeneous. The data generated in these contexts differs materially from that of traditional grid infrastructure.

Core OT platforms were designed for high-resolution, synchronous data streams. By contrast, the LV layer produces asynchronous, event-driven signals, often using a long tail of IoT-native protocols such as MQTT, DLMS/COSEM and LPWAN technologies.

        Coordination enables these disparate data streams to be normalised into a single operational view, bridging established utility standards (IEC 61850, ICCP/TASE.2, DNP3) with newer forms of device communication.
    

Altior: An Execution Layer for Data in Motion

Within this evolving architecture, the handling of data as it moves between systems becomes as important as how it is stored.

Inkwell Data's Altior, an OT–IT Platform-as-a-Service (PaaS), operates as a data-in-motion execution layer. It processes data as it flows through the system, rather than acting on it once stored.

In practice, this involves:

Data-in-Motion Capabilities

Validating incoming data, ensuring completeness and integrity at the point of ingestion

Transforming heterogeneous inputs into CIM-aligned (IEC 61968/61970) structures

Aligning timestamps onto a shared operational timeline

Routing outputs in a consistent and usable form

        This establishes a simple but powerful principle: data is ingested once, aligned at source, and made available across systems. This distinguishes it from traditional middleware or static ETL processes, which typically operate on data at rest.
    

Complementing the Existing Stack

Altior is designed to complement, rather than replace, existing systems. It acts as a connective layer that enhances the performance of established platforms by providing data that is already structured and synchronised.

This enables existing systems to operate on consistent inputs, without requiring modification to their native data models.

The approach is particularly effective in managing the LV "long tail". Digital Twin Templates define the expected behaviour of a class of assets—for example, a specific model of EV charger—while individual Instances represent each deployed device.

        This model supports horizontal scalability across large numbers of heterogeneous assets, while maintaining consistency in how their data is interpreted.
    

Governance and Operational Trust

As visibility extends across organisational boundaries—from the device layer through to the National Energy System Operator (NESO)—governance becomes increasingly important.

Data entering operational environments must be subject to strict control. Zero Trust principles ensure that every data exchange is authenticated, authorised and encrypted.

        Aligned with the NIS2 Directive and RIIO-ED2 resilience requirements, Altior's Aegis framework provides full auditability and supports data sovereignty. It acts as a controlled interface through which third-party data can be introduced into the network environment in a secure and traceable manner.
    

Concluding remarks

The transition from DNO to DSO requires more than the expansion of existing systems. It depends on a digital architecture capable of operating as a coherent, time-consistent whole.

        By focusing on coordinated data handling through a dedicated data-in-motion layer, networks can establish a trusted foundation for advanced analytics and the effective deployment of AI-driven capabilities.
    