IN Brief:
- SP Energy Networks and Keen AI have developed IConn to support faster pre-application assessments for transmission connections.
- The tool digitises existing, contracted, and planned network capacity to model routes, costs, constraints, and power flows.
- Grid connection reform is increasingly dependent on earlier engineering visibility as clean power, storage, and major demand projects seek access to the transmission system.
SP Energy Networks and Keen AI have launched IConn, a digital tool designed to accelerate early-stage assessments for developers seeking new connections to the electricity transmission grid.
The tool gives customers a view of where and how projects could connect to the transmission network, drawing together information on existing, contracted, and planned capacity. It can generate potential connection routes, estimated costs, power-flow simulations, and technical constraint analysis in less than five seconds, replacing work that has historically relied on lengthy manual engineering processes.
IConn has been developed for the pre-application stage of the grid connection process, where developers need enough technical visibility to assess whether a site, project size, and proposed connection point are viable before detailed design work and formal application activity begins. The tool processes raw network data through locally hosted models and presents the network as a unified digital environment, allowing potential connection impacts to be assessed more rapidly.
The project forms part of the wider Intelligent Connections Explorer programme and supports the UK’s Clean Power 2030 agenda, which places faster grid connections at the centre of energy system delivery. New transmission connection demand has increased sharply as renewable generation, energy storage, industrial electrification, data centres, and other large electrical loads compete for capacity on a constrained system.
At transmission level, the challenge is no longer limited to the physical availability of circuits, substations, and transformers. It also depends on the speed at which system data can be converted into practical engineering decisions. Developers need earlier clarity on where capacity exists, where reinforcement may be required, and how a proposed connection interacts with existing contracted capacity. Network companies must manage increasingly complex queues while maintaining security, operability, and long-term reinforcement planning.
By automating parts of the pre-application assessment, IConn can reduce speculative or poorly targeted connection requests and give design and planning teams a clearer technical basis for early discussions. The tool does not replace detailed connection studies, protection design, consent work, or reinforcement planning, but it addresses one of the most time-consuming stages in the process: the first screening of network impact and viable connection routes.
The same pressure is visible across the UK power system. Distribution and transmission operators are being asked to connect more generation, more storage, and more flexible demand while preparing networks for electrified transport, heat, and industry. Faster queue management depends on regulatory reform, better engineering visibility, and digital tools that expose capacity, constraints, and power-flow impacts earlier in the project cycle.
Grid connection processes are moving away from static queue administration towards more dynamic, data-led assessment of where power can actually move. IConn gives SP Energy Networks a practical route into that model, with network data used earlier in the customer process and fewer decisions pushed into later, higher-risk stages of development.
Further detail on the innovation project is available through the ENA Innovation Portal.
