Intermittent energy cannot be centrally scaled up or down in sync with demand, which means responsibility for balancing supply and demand is being increasingly devolved to consumers and local producers.

This decentralisation and localisation of energy through microgrids, local generators, peer-to-peer energy markets, and community power-sharing schemes could create smarter ‘self-correcting’ networks that continually balance the grid based on data.

In order to be a sustainable option, renewable grids require a move away from centralised energy generation and information towards decentralised energy and data.

Today, many current energy network maps are relics of traditional centralised grids and are not capable of drawing on information from the full array of local energy infrastructure, field workers, and smart meters that make up this newly ‘decentralised’ grid. Top-down, siloed geospatial systems are no longer fit for purpose in decentralised grids that must react to live events on the ground.

Geospatial systems that cannot easily incorporate live information from the field leave many energy organisations with outdated, inaccurate asset records, as well as an incomplete picture of events on the ground. Vital information on recent repairs, inspections, upgrades, hazards, or new builds is fragmented among paper maps and proprietary apps.

I have worked with utility companies where as many as 50% of their as-builds are wrong and there are huge update backlogs. Utilities are now sitting on a rich and abundant array of “field-sourced” as-built network data from engineering teams and IoT devices such as Advanced Metering Infrastructure, but this data is not being integrated.

Rigid, top-down geospatial systems that cannot easily interface with all the sensors and apps on the ground mean it can take utility operators as long as six months to get accurate as-builds back into the core geographic information system.

Many do not even know where their power grids are in relation to all the transport and housing infrastructure that connects to them. As electric and hydrogen vehicles mean that households and transport networks are increasingly run from the same power source, it is critical that their interdependence is understood.

Outdated asset records will make it difficult to accurately model the potential for shortages or surpluses of power in different locations or identify potential new markets for energy services. Power companies that lack oversight of the proximity of assets to nearby ecological features could be more vulnerable to increasing hazards such as extreme weather events.

Inaccurate maps of infrastructure could render it difficult to identify the sites and source of grid imbalances and spot gaps in provision requiring increased generating capacity. For example, electric vehicles will require upgrades and extensions to electric grid infrastructure to support new charging networks.

Renewable energy grids will depend on understanding and influencing local consumption habits and capacity. This requires genuinely current and comprehensive geospatial data on how each aspect of the network intersects with the people and places that depend on it.

The key is to understand that, just as energy generation is being pushed from centre to the edges of a network, energy data must now be drawn from the edges of a network too. Decentralisation of power will require a parallel decentralisation of network data.

Some pioneering companies such as Tokyo Electric Power Company (TEPCO) are now harnessing the digital revolution to decentralise network mapping and enable energy grids to be mapped, monitored, and managed with real-time data from the edges.

Instead of seeing decentralisation of energy as a threat to their visibility and control over the grid, forward-thinking utility companies are turning this to their advantage by allowing everyone from field technicians to engineers to update maps and asset records with smartphones in the field. They are harnessing data from local sources to create a rich, real-time picture of their networks and fuel flexible, adaptable grids.

Field technicians could correct as-builds and asset records and outline their proximity to planned electric vehicle charging infrastructure to identify potential gaps in provision. Utilities could predict and prevent vulnerability to extreme weather events such as the winter storms that recently froze wind turbines responsible for half of the state of Texas’ generating capacity.

TEPCO recently deployed a decentralised mobile-friendly geospatial platform that can be easily accessed and updated by workers in the field to create a comprehensive and current overview of utility grids. When Typhoon Faxai damaged their network, the system was used to allow both central managers and field crews to rapidly view critical network information, blackout locations, and damage in any location.

The system is based on Google Maps technology, making it easy for field technicians and construction teams to find unfamiliar locations and identify facility characteristics. Working online and offline, technicians can instantly see the condition and position of nearby assets, enabling them to quickly find the right equipment and efficiently and effectively target repairs.

This enables more ‘joined-up’ planning to reduce the cost and risk and maximise the market for new infrastructure. Planned infrastructure can be combined with local data on nearby hazards or planned new electric vehicle networks to maximise return on investment and minimise risk.

Crucially, as intermittent energy renders grids more dependent on balancing supply and demand in real-time, it gives operators the ability to predict shortages or surpluses in any location. Geospatial data on local generating capacity could be matched with historic data on local consumption patterns to predict future ‘pinch points’ and plan extra capacity or ‘flexible charging’ infrastructure.

The decarbonisation and decentralisation of energy grids means geospatial systems must be similarly decentralised to chart the drivers behind consumption and production in every location.