AI And Digital Twins In the Energy Industry

The energy industry is shifting from centralised systems to distributed, complex grids. Rising demand, renewables, and AI-driven loads require sharper tools. Digital twins and AI provide real-time insight, predictive intelligence, and context, cutting costs, boosting efficiency, and improving resilience. Could these technologies be the true engines of tomorrow’s energy system?

The energy industry is at a turning point. Demand is climbing faster than many expected. According to international energy outlooks, global energy consumption is projected to grow by around 40% between 2006 and 2030. This growth is not only driven by population and urbanisation but also by new infrastructure such as AI, data centres, and digital networks. The same technologies that promise efficiency are themselves creating large new loads on already strained grids.

At the same time, the world faces urgent climate goals. Governments and companies are under pressure to reduce emissions, invest in renewable energy, and align with carbon neutrality targets.

But the challenge is clear: renewables alone cannot yet meet rising demand.

Fossil fuels remain part of the mix, and the task for operators is to manage this complexity in smarter, faster, and more resilient ways.

This is where digital twins and artificial intelligence (AI) come in. Both are changing how energy systems are managed, monitored, and optimised. Together, they offer a way to see the grid not as a static map, but as a living, dynamic system that can be tested, modelled, and improved in real time.

Why AI and Digital Twins matter in the energy industry?

Evidence of their value is already strong. A recent systematic review of digital twin and AI applications in energy found measurable gains across multiple projects:

• 35% reduction in unplanned downtime through predictive maintenance.
• 8.5% increase in energy production by optimising renewable operations.
• 98.3% accuracy in fault detection, which improves reliability and safety.
• 26.2% reduction in energy costs, showing that the financial case is just as strong as the technical one.

These are not small improvements. They represent real progress in efficiency, reliability, and cost control at a time when the industry is more complex than ever.

The energy system of the past was centralised and predictable. Power plants generated electricity, utilities delivered it, and customers consumed it. That model no longer fits today’s reality. The modern grid is distributed, full of intermittent renewables, flexible assets like batteries, and new patterns of demand such as electric vehicles and AI-driven data centres. It is not only more fragmented, but also more dynamic.

To make decisions in this environment, energy leaders need sharper tools. Spreadsheets and static forecasts are not enough. What they need are tools that can capture context, where assets are, how they perform, what risks they face, and how they interact with the wider system.

Digital twins provide exactly that. They are living mirrors of energy assets and networks. They do more than simulate; they absorb real-time data, model scenarios, and generate insights that operators can act on. When paired with AI, they become even more powerful. AI adds intelligence to the twin, spotting patterns, updating forecasts, and improving decisions at speed and scale.

What is driving change in the Energy Industry?

The energy industry is being reshaped by powerful forces that make traditional tools and models insufficient.

• Rising demand is the first driver. Global electricity use is expected to grow by around 40% between 2006 and 2030, fuelled not just by population growth but by AI, data centres, and digital infrastructure. Renewables are scaling quickly; solar and wind added 473 GW in 2023 alone, but this expansion is not yet enough to cover demand growth. Fossil fuels remain part of the mix, making it harder to balance cost, supply, and emissions.
• The second driver is the shift from centralised utilities to distributed energy resources (DERs). Rooftop solar, microgrids, batteries, and wind projects make the system more flexible but also more complex. Each new asset creates more variables to manage, and the old one-way model of generation to consumption no longer applies.
• Climate policy and regulation add further pressure. Countries like China, the USA, and India, major greenhouse gas emitters, face strong expectations to cut emissions. The Climate Change Performance Index highlights the urgency of cleaner energy, and utilities must comply with stricter reporting and sustainability targets.
• On top of this, market volatility and geopolitical shocks, from conflicts to extreme weather, make energy systems harder to predict. Traditional IT systems and static models, designed for simpler grids, cannot provide the speed or context required today.

This is why the industry is turning to digital twins and AI. They allow operators to simulate scenarios, monitor real-time performance, and make informed decisions across distributed, volatile, and expanding grids.

Understanding Digital Twins and AI

To see why these technologies matter, it helps to define them simply.

A digital twin is a virtual mirror of a physical asset, system, or process. Unlike a static model, it updates in real time using data from sensors, control systems, and external sources such as weather or market signals. This makes it possible to simulate scenarios, test decisions before acting, and monitor performance as conditions change. A digital twin is not just a copy; it is a living model that reflects the behaviour of the real system at any moment.

Artificial intelligence (AI) refers to algorithms that can learn from data, recognise patterns, and improve over time. In the energy sector, AI helps forecast demand, predict equipment failures, optimise generation, and manage variability in renewables. AI does the heavy lifting of analysing large volumes of data and turning them into predictions or insights.

When combined, digital twins and AI reinforce each other. A digital twin gives AI context, anchoring the algorithms in the operational reality of the grid, a power plant, or a storage facility. AI, in turn, gives the twin intelligence—updating forecasts, spotting anomalies, and suggesting the best response.

For example:

• A solar farm twin linked with AI can forecast output more accurately by learning from weather and historical data.
• A battery twin can optimise charging cycles, reducing wear and extending lifespan.
• A grid twin can run stress tests, showing how outages, congestion, or new demand centres would affect stability.

This integration matters because the energy industry is no longer simple or static. Two assets in the same region may have very different emissions profiles or operational risks. 

Applications of AI and Digital Twins in the energy sector

The strength of digital twins and AI lies in their versatility. They can be applied to every stage of the energy chain, from generation and storage to grid operation and consumption. Below are the main areas where they are already creating value.

Power grids

The modern grid is far more dynamic than the old centralised model. Power no longer flows one way from plants to customers. Instead, thousands of smaller sources, solar panels, wind farms, batteries, and electric vehicles, are constantly feeding in and drawing out. This creates new challenges in balancing supply and demand.

Digital twins allow operators to build a real-time mirror of the grid. They combine data from sensors, markets, and weather to show how power is flowing across different regions. AI adds intelligence by detecting congestion, forecasting demand, and running stress tests.

For example, in North America, the grid is split into nine Independent System Operators (ISOs), each with its own rules and pricing. A grid twin makes it possible to see how actions in one ISO affect the others, giving traders and operators the full picture. In the UK, National Grid uses digital twins to plan transmission upgrades, testing scenarios before committing billions in investment.

Microgrids and distributed energy resources

Microgrids are smaller, localised systems that can run independently or in connection with the main grid. They often include solar panels, batteries, and small wind projects. While flexible, they require careful coordination to stay stable.

AI and digital twins provide that coordination. Twins model each asset and the network around it, while AI optimises when to draw power, when to store it, and when to feed it back. In one study, AI-driven digital twins for microgrids reduced operational costs by over 25%, while improving reliability.

This makes microgrids more attractive for communities, campuses, and industries that need both sustainability and resilience. For instance, in Singapore, entire clusters of distributed energy resources are modelled with digital twins to optimise performance at the city level.

Solar and wind systems

Renewables are the fastest-growing part of the energy system, but also the hardest to manage. Their output depends on, weather, which can change quickly.

Digital twins, combined with AI forecasting, provide a solution. By using historical data, satellite inputs, and live weather feeds, AI can forecast solar irradiance or wind speeds with high accuracy. One study showed that stacked regression models reached 98% fault detection accuracy and allowed operators to predict solar output across multiple climate zones.

Twins are also used for maintenance. Drones can scan solar panels, feed images into a digital twin, and let AI detect cracks, dust, or shading problems automatically. In wind farms, twins simulate blade performance and predict failures before they happen, cutting downtime by 35%.

Energy storage and batteries

Batteries are central to balancing intermittent renewables, but they degrade over time. Poor charging cycles shorten their life and raise costs.

Digital twins of batteries track temperature, charge cycles, and local grid conditions in real time. AI uses this data to optimise when and how to charge or discharge. This extends battery life, reduces replacement costs, and improves grid stability.

In Medicine Hat, Alberta, a digital twin is helping the city determine the right size and location for a new storage facility, ensuring the investment is cost-effective and future-proof.

Industrial energy systems

Factories and heavy industries are among the largest consumers of energy. For them, efficiency is not just an environmental issue but a cost advantage.

AI and digital twins are being used to build virtual factories, modelling machines, production lines, and energy use in detail. By simulating scenarios, companies can spot inefficiencies and test solutions before making changes. Predictive maintenance, powered by AI, cuts downtime and extends the life of critical equipment. Studies report energy cost savings of more than 26% in industrial settings.

Urban energy and smart cities

Cities are becoming the focus of the energy transition. With rising populations, electric vehicles, and decentralised renewables, urban grids are under pressure.

Digital twins make it possible to model entire districts. They simulate how EV charging stations, rooftop solar, local batteries, and building consumption interact. AI analyses these models to optimise traffic, reduce emissions, and balance loads.

In Morocco and China, researchers are already testing urban solar digital twins that detect anomalies and optimise rooftop installations. In Europe, pilot projects link building twins with grid twins to design net-zero neighbourhoods.

Cybersecurity and resilience

As grids and assets become more digital, they also become more vulnerable to cyber threats. AI-enhanced twins can detect anomalies in data traffic, run intrusion simulations, and suggest protective measures before attacks succeed.

Edge computing takes this further. Small processors placed near assets allow AI models to run locally, ensuring that even if a storm or cyberattack cuts internet links, the assets continue to operate safely. This autonomy builds resilience and avoids “blind spots” in the grid.

Benefits and measured Impact of AI and digital twins in the energy sector

The true value of AI and digital twins in the energy sector is reflected in measurable outcomes that improve operations globally.

• Efficiency Gains- One of the biggest advantages is improved efficiency. Digital twins, through predictive maintenance and real-time monitoring, can reduce unplanned downtime by about 35%, leading to fewer breakdowns and smoother grid operations. AI also optimises asset performance; for example, in renewable plants, self-learning algorithms adjust output based on weather, increasing energy production by up to 8.5%. In industries, AI has driven energy cost reductions of over 26%, directly translating to savings.
• Financial Impact- AI-powered digital twins can cut energy costs by 26.2%. This comes from fewer outages, longer asset life, and smarter energy use. For grid operators and traders, digital twins improve visibility into pricing zones, helping them make better trading decisions. For cities and utilities, accurate forecasting reduces overproduction, saving money and reducing emissions.
• Environmental Benefits- Digital twins play a key role in reducing emissions. They map emissions intensity in real time, allowing operators to choose cleaner generation sources and make better decisions about asset locations. Digital twins also reduce the need for backup fossil plants in renewables, lowering carbon emissions and supporting net-zero goals.
• Resilience and Reliability- Digital twins enhance resilience by simulating stress conditions, such as storms, outages, or market shocks, to test system responses. When combined with edge computing, they can run models locally, ensuring that assets keep functioning even if central systems fail. This is especially useful in remote areas where internet access may be disrupted.
• Smarter Decision-Making- The most strategic benefit is decision intelligence. Digital twins transform raw data into useful context, allowing leaders to simulate options before making decisions. For example, using a twin to choose the location for a battery storage facility ensures it delivers maximum value rather than becoming an unnecessary expense.

Case Studies (Global Examples)

Real-world examples show how AI and digital twins are already reshaping the energy industry. From city-level planning to industrial operations, these projects highlight the flexibility and value of the technology.

Medicine Hat, Alberta – Battery Storage Planning

In Canada, the city of Medicine Hat is using a digital twin to design a new battery storage facility. The twin allows planners to test different battery sizes and locations before committing capital. By modelling demand, generation, and local emissions, the city ensures the investment will meet future needs without overspending. This case shows how digital twins support better investment decisions by simulating outcomes in advance.

UK National Grid – Transmission Planning

In the United Kingdom, National Grid has adopted digital twins to plan upgrades to the transmission system. Large projects often take years and billions of pounds. By creating a twin of the network, planners can simulate demand growth, renewable integration, and congestion risks. This makes it possible to test multiple scenarios and choose the most resilient option. The approach reduces the chance of overbuilding or underbuilding infrastructure, saving both money and time.

Singapore – Modelling Distributed Energy Resources

Singapore is a global leader in digital innovation, and its energy sector is no exception. The country is using digital twins to model clusters of distributed energy resources across urban districts. These include rooftop solar, local batteries, and microgrids. AI analyses the models to optimise energy flow, balance loads, and cut emissions. For a dense city-state with limited land, this approach helps maximise every unit of renewable capacity.

Solar Systems in Morocco and China – Predictive Maintenance

Academic studies show how solar systems in Morocco and China are benefiting from AI-driven twins. By combining weather data, satellite imagery, and machine learning, these systems can forecast irradiance and detect faults. Algorithms such as stacked regression and convolutional neural networks (CNNs) have achieved up to 98% accuracy in fault detection, allowing operators to carry out predictive maintenance before failures occur.

Industrial Microgrids – Cost Optimisation

In industrial settings, digital twins of microgrids have shown strong results. A review found that AI-enhanced twins reduced operational costs by more than 25%, while improving reliability. For energy-intensive industries, this translates into millions in savings and a stronger business case for renewables.

The energy industry is entering its most complex phase yet. Demand is rising sharply, driven by AI, data centres, and urban growth, while the push for decarbonisation grows stronger. At the same time, grids are becoming more distributed, dynamic, and vulnerable to shocks. The traditional tools of static forecasts and legacy systems are no longer enough to manage this scale of change.

AI and digital twins offer a way forward. Together, they turn data into context, and context into action. The evidence is clear: downtime cut by 35%, energy production up 8.5%, fault detection accuracy reaching 98.3%, and energy costs reduced by 26.2%. These numbers show real, measurable progress in efficiency, resilience, and sustainability.

But beyond efficiency, digital twins are also strategy engines. They allow leaders to simulate, stress-test, and optimise decisions before committing resources. From city storage projects in Canada to urban grids in Singapore, real-world case studies prove that this technology is already shaping the future.