Why the Energy Sector is Embracing Digital Twins in 2026

For a sector so complex and data-reliant as energy, having an accurate, up-to-date overview of assets, networks and processes is like gold dust.

Digital twins are increasingly providing that oversight to energy companies the world over.

Drawing on the power of the IoT, these computer models can act as living, real-time mirrors of anything from grid health to renewable energy output.

Their appeal is clear: by synchronising with real-world data, they help operators understand current conditions, test scenarios, predict failures and optimise performance before making costly or risky physical changes. 

In the UK market, adoption is already quickening as a result of grid constraints, cost pressures and delays to expansion projects. 

The Womble Bond Dickinson Energy Outlook Report 2026, based on a survey of more than 650 senior sector leaders, reveals that investment in technologies that optimise energy supply is on the rise.

As such, digital twins are now front-rank priorities among operators seeking to extract more output and resilience from existing assets while new projects wait in planning and connection queues. 

What is a digital twin?

A digital twin is a computer simulation of physical equipment that is updated in near real time with telemetry from sensors and operational systems.

It typically blends physics-based engineering models with statistical or machine-learning components to replicate how real-world equipment behaves under different conditions.

Unlike a one-off simulation, a twin persists over time. 

Field data updates the model continuously, and its insights can then inform day-to-day operational decisions, even going as far as making fully autonomous decisions with limited human intervention. 

Digital twins have all manner of applications in the energy sector, whether that be in design, construction, operations or decommissioning.

At the asset level, twins of turbines and boilers can monitor degradation, optimise set-points and reduce fuel use.

Then, at plant or farm level, they can support asset coordination, diagnose performance issues and evaluate dispatch across generation and storage. 

Market drivers and adoption trends

The Energy Outlook 2026 report found that building new energy infrastructure is frequently delayed, with companies reporting an average of US$325m of lost revenue due to new capacity being held up.

Long grid connection queues and infrastructure constraints are driving a strategic pivot from new greenfield development to optimisation and retrofit strategies for existing assets.

Deployment of new digital optimisation technologies are seen as the primary way to increase production capacity in the short-term, with 49% of energy companies planning to implement technology upgrades within a year.

Technology spending is rising accordingly. Companies responding to the Energy Outlook Report 2026 expected budgets for energy-supply optimisation to increase by about 16% in 2026, with the UK organisations among the leaders.

Three priorities dominate: AI (75%), upgrades to hardware technology (64%), and the deployment of digital twins (58%). 

By using a continuous flow of real-world operating data, a digital twin can rapidly simulate and assess possible changes to operating parameters and apply them instantly to secure the most efficient output. 

Twins can also help simulate the future operating life of equipment, allowing organisations to detect early-stage faults and schedule maintenance at the latest disruptive moment. 

All of this being done in a dynamic digital model speeds up decision-making and maximises the performance of assets.  

Risk issues 

As the use of digital twins grows and begins to influence operational decisions in near real-time, four clusters of risk have been identified.

1. Liability and reliance on model outputs

Digital twins can make mistakes and promised efficiency gains may not be achieved.  With less human intervention in the loop, those errors may go unnoticed until too late. 

Where decisions are influenced by a digital twin, questions around responsibility become more complex if an outage or incident occurs, especially when digital twin technology is supplied by a third-party vendor. 

Supplier contract terms need to clearly allocate responsibilities and provide compensation for physical harm and economic loss.

Operators should also ensure the long-term preservation of a twin’s configuration parameters, inputs and outputs so incidents can be properly investigated and any regulator questions fully answered.

2. Data ownership and access

Digital twins depend on continuous operational data, that may be gathered from assets supplied or maintained by third parties or processed by external analytics platforms.

Operators need to ensure there is clarity over ownership of raw and derived data, as this is the core know-how that is used and created by a digital twin.

There may also need to be restrictions on a third-party supplier's rights to mine operational datasets so to avoid loss of confidential information or it being shared with competitors, which can happen indirectly when digital models are refined and re-trained.

3. Intellectual property and supplier lock-in

Where technology suppliers retain ownership of the underlying intellectual property, operators may struggle to switch vendors or run the twin independently at contract end. 

Technology licences need to give control of IP to operators, and software escrows and step-in rights should be used to ensure an operator's ability to take over control of the twin in the case of a vendor's failure or insolvency, particularly where a twin supports safety operations.

4. Cyber security and operational resilience

Digital twins increase the digital attack surface by deploying large numbers of connected sensors and aggregating their telemetry.

Each device becomes a potential attack vector, increasing the burden on operators to track and risk assess devices, ensure their proper installation, and to keep the devices updated.  

Reforms to the UK Network and Information Security Regulations proposed by the Cyber Security and Resilience Bill will widen incident-reporting obligations by many energy operators and introduce penalties of up to 4% of global turnover for non-compliance.

Combined with rising cyber threat levels, regulators are expected to adopt a more assertive enforcement stance.

Digital twin deployments therefore need to be managed within a structured governance and risk regime to ensure operational resilience and compliance with legal requirements. 

The future of digital twins in the energy sector

The Energy Outlook Report 2026 shows companies using digitalisation to “fill the gap” while new greenfield expansion is delayed, with UK companies planning above-average increases in technology budgets and prioritising AI, predictive maintenance and digital twins. 

Adoption at scale, however, requires disciplined risk and regulatory planning. Done well, digital twins can deliver durable operational value and resilience.

Done casually, they risk introducing new points of failure in an increasingly stringent regulatory environment.