How Digital Twins Will Drive the Future of Renewable Energy

For several centuries now, we have been utilizing oil and gas resources to generate electricity, power vehicles and aircraft, and serve as the basis for a wide range of products, including rubber, plastics, fertilizers, and pharmaceuticals.

These non-renewable, natural resources are produced from carbon and hydrogen and supply as much as 84% of the world’s power generation. The extensive use of these finite, conventional resources, however, has led to pollution and environmental damage. 

By releasing toxic greenhouse gases and harmful pollutants, the extraction and combustion of fossil fuels have been contributing to climate change and global warming, and affecting human health and ecosystems.

A key solution to this significantly negative impact on the planet caused by oil and gas resources is making a shift from fossil fuels to renewable energy sources.

Renewable energy is extracted from environmental sources such as the weather and geographic location. It is zero-emission green energy.

Solar, wind, hydroelectric, geothermal, and biomass energy are the most prominent examples of renewable energy sources, which are sustainable.

Over the last decade, the world has been turning to these renewable energy sources as a way to make a green energy transition, resulting in a consistent increase in their use across various applications.

According to the International Energy Agency (IEA), renewable energy consumption in the power, heat, and transport sectors is forecasted to increase by about 60% between 2024 and 2030, which will boost the share of renewables in final energy consumption from 13% in 2023 to almost 20% by 2030.

While beneficial to the environment, integrating these natural resources in power generation, energy storage, and transportation comes with its own challenges due to their intermittent nature and heavy reliance on external factors like season and location. This dependence requires an energy storage system.

There is also a high initial infrastructure cost involved with renewable energy sources, while their rate of power generation is slow.

As a result, conventional sources are still used for most of the power generation. This makes it crucial to have a new strategy and technology in place to deal better with these challenges. That means understanding, studying, and analyzing the behavior of each system’s parameters during the design, production, and services phases of each renewable energy system’s useful lifecycle. This is where digital twin (DT) technology comes into the picture.

The technology makes use of adaptive models to simulate the real-time performance of physical systems in a digital environment, in turn, helping predict and prevent potential system failures. 

From Physical to Digital: The Emergence of Virtual Replicas

A digital twin is simply the virtual representation or replica of a physical, real-world object, person, system, or process. To mirror its physical counterpart, the digital replica uses real-time data captured with the help of sensors, simulations, and machine learning. 

This allows for monitoring, analysis, and prediction of the physical asset’s behavior in various scenarios, thus allowing us to make better decisions.

The ability of these digital twins to replicate and interact with complex systems has made them highly valuable across industries, where they are driving improvements in efficiency, cost reduction, and the development of innovative solutions.

According to McKinsey estimates, the global market for digital-twin technology will reach $73.5 billion by 2027, growing 60% annually over the next five years.

The term ‘digital twin’ was adopted by NASA’s John Vickers in 2010, but the core idea for the same came much earlier. The space agency actually developed the tech for use in space exploration missions in the 1960s.

It was in 2002, though, that Dr. Michael Grieves formally announced the concept and applied it to manufacturing. The concept was divided into three key parts: the actual physical space, the virtual space of that physical part, and the link connecting the two.

Many years later, in 2011, a digital twin was developed by the U.S Air Force to design aircraft and predict fatigue and maintenance. From there, the technology spread to other fields, including aerospace, transportation, shipping, manufacturing, healthcare, and oil and gas applications.

In renewable energy, the primary function of a digital twin is to collect data from onsite sensors to reproduce the operations of the physical system in a virtual setting.

A digital twin can be created for each type of renewable energy system during its lifecycle phases to serve a specific task. This means a need for vast amounts of data, including each component’s geometry, weather data, previous issues, historical forecasting, experimental and practical data, and real-time data, making the digital twin’s application in the sector complex and challenging. 

The thing is, the application of digital twins in renewable energy systems isn’t really explored that extensively. 

So, the new study takes a deep dive into the concept in this particular sector. Researchers at the University of Sharjah have done an in-depth exploration of AI-powered digital twins as a tool to accelerate the clean energy transition.

In their paper, the researchers conduct a thorough review of the architecture, functions, lifecycle, and applications of digital twin technology in renewable energy systems.

For this, they used AI, machine learning (ML), and natural language processing (NLP) that allowed them to assess large volumes of raw data and uncover meaningful insights about structured patterns and emerging trends.

With this research, the idea is to leverage the technology’s potential to improve efficiency and sustainability while addressing the challenges of data scarcity, complex biological processes, degraded equipment modeling, and environmental variability.

Optimizing the Green Shift: The Promise & Challenges of AI-Driven Digital Twins

As the world struggles to reduce carbon emissions and combat climate change, researchers have turned to AI-powered digital twins to reshape the future of energy.

These digital representations of the physical world, according to the researchers, can transform the generation, management, and optimization of renewable energy sources, in turn, accelerating the transition away from fossil fuels. But for that, we need to overcome their notable limitations.

As the researchers noted, “digital twins are highly effective in optimizing renewable energy systems,” but each renewable energy source presents unique challenges that can “limit the performance of digital twin technologies, despite their considerable promise in improving energy generation and management.”

So, after conducting an extensive review of existing literature on the topic, as to how digital twins are being used in the sector, they recognized research gaps, suggested guidelines, and covered the issues that need to be addressed to take full advantage of the digital twin technology in the renewable energy sector.

A research roadmap is also offered to help scientists enhance the reliability and precision of the technology.

In their study, the researchers defined significant advantages of digital twins¹ as well as their limitations across various renewable energy systems. The focus of the recommendations made by the researchers is on expanding computational capabilities, advancing modeling techniques, and improving data collection methods to ensure digital twins can deliver precise and reliable insights for decision-making and system optimization.

Wind Energy

Wind energy harnesses the power of wind to generate electricity. In 2024, its contribution to global power generation grew to 8.1%. It is set to become the second-largest source of global renewable electricity generation behind solar PV by the end of this decade.

To convert wind’s kinetic energy into electricity, wind turbines are installed onshore on land as well as offshore in the sea, either fixed or floating. 

Mainly, two types of wind turbines are used here. The vertical-axis wind turbine (VAWT) is one where the rotation of the axis is perpendicular to the wind motion. The other is horizontal-axis wind turbines (HAWT), which rotate parallel to the wind stream.

While HAWT captures the maximum amount of wind energy, it requires stable airflow with no considerable fluctuation. VAWT, in contrast, captures wind from any direction and operates at a turbulent wind stream location with a lower power generation rate.

The use of digital twins here can help predict unknown parameters and correct any inaccurate measurements. 

However, they face challenges in accurately modeling and monitoring environmental factors and conditions. Unreliable data and gaps in data collected from remote or offshore areas also create issues for digital twins. Moreover, they struggle to simulate critical factors in aging turbines such as gearbox degradation, blade erosion, and electrical system performance.

Click here for a list of top wind energy stocks.

Solar Energy

The main driver of renewables growth is solar energy, which has been making the largest contributions to the clean energy generation for several years now. In 2024, it provided more than 2,000 TWh of electricity, adding 474 TWh to reach a share of 6.9%, which made it the fastest-growing power source for the 20th year in a row. 

The fastest-growing and largest source of new electricity is solar energy. The sunlight is converted directly into electricity using photovoltaics (PV). A PV panel, or solar panel, contains PV cells that are made of an energy-transmitting semiconductor. These cells absorb the sunlight and convert solar energy into electricity.

Meanwhile, concentrated solar power (CSP) is an indirect way to generate electricity, as lenses or mirrors are used to concentrate sunlight into a focal point. 

In regard to solar energy, digital twins utilize real-time data from sensors to find the key factors influencing efficiency and output power. Despite their potential, digital twins here cannot accurately predict performance due to variations in atmospheric conditions. Also, they have trouble monitoring panel degradation and environmental influences over time, affecting their accuracy and usefulness.

Much like with wind energy, data collection from remote or offshore areas can be sparse or unreliable here.

Click here for a list of top solar energy stocks.

Geothermal Energy

This renewable energy is extracted from the interior heat of the Earth’s core and is used for heating and cooling in addition to electricity generation. Its share of renewable energy is less than 3%.

Digital twins can help simulate the entire operational process of utilizing geothermal energy, especially the drilling process. By facilitating cost analysis and predicting fatigue, they can save both time and cost associated with the operation.

The biggest challenge here is the limited availability of high-quality data, which hinders the technology’s ability to simulate geological uncertainties and conditions below the Earth’s surface. Then there are complex long-term behaviours of geothermal systems, such as heat transfer and fluid flow dynamics, which are difficult for digital twins to model.

Hydroelectric Energy

Hydroelectric energy utilizes the flow of water to produce power. It harnesses the effects of gravity and elevation.

In 2024, hydropower accounted for most of the global electricity generation by renewable energy technology. But this single largest renewable source’s 14% share is expected by the IEA to see a one percent decline by 2030 as the growing usage of solar PV and wind energy makes hydropower less prominent. It is still expected to grow as new projects get active. 

Hydroelectric energy is associated with high construction costs, negatively affects water quality, and has an adverse influence on animal habitats.

Digital twins can be applied to hydroelectric energy to simulate the system in order to identify factors impacting it. In older plants, they can help alleviate the impact of worker fatigue on productivity. 3D laser scanning is used here to detect cost-effective fatigue construction.

The challenge, however, is data scarcity, aging infrastructure performance, and accurately modeling the complex water flow variability, as well as monitoring environmental and ecological constraints.

Biomass Energy

This type of energy is derived from organic material, which involves decomposed animals and plants. It can be extracted from various solid, liquid, and gaseous sources like methane, agricultural crops, vegetable oils, animal manure, and municipal solid waste.

The AI-driven models can help improve the functionality and operation of biomass energy by offering a deeper understanding of the whole process and plant setup, such as a burner.

But when applied to this renewable energy system, digital twins struggle to precisely model biomass conversion and biological, biochemical, and thermochemical processes. They also face challenges in simulating the complete supply of the biomass energy production chain.

Investing in the Digital Twin Tech

Now, if we look at an investment opportunity in this space, PTC Inc. (PTC -2.6%) stands out for its core digital twin focus and strong market performance. The global software company enables manufacturing and product companies to digitally transform how they design, manufacture, and service physical products.

PTC Inc. (PTC -2.6%)

PTC’s suite of products includes Windchill for enterprise product lifecycle management software, Creo to build products with CAD/CAM/CAE, ALM software Codebeamer for modern development, asset-centric ServiceMax for service management, cloud-native PLM platform Arena, cloud-native CAD platform Onshape, Kepware to access and control industrial data, ThingWorx to build and deploy industrial Internet of Things (IIoT) applications, scalable enterprise AR platform Vuforia, Servigistics for service parts management, and Arbortext to create, manage, and deliver content efficiently.

PTC’s digital twins have also been used across the renewable energy sector. 

A couple of years ago, France-based energy group ENGIE teamed up with it to develop a virtual furnace to assist in the transition of industrial assets. EDF, meanwhile, used ThingWorx and Vuforia to monitor operations, improve worker training, and simulate critical maintenance tasks for its nuclear power plant systems. Howden applied the tech to enhance its compressors and fans used in oil & gas and power generation.

When it comes to its market performance, PTC’s shares have hit an all-time high (ATH) above $219, recording an upside of 16.83% YTD while being up 57.5% since April. With that, it has an EPS (TTM) of 4.24 and a P/E (TTM) of 50.64.

For the third fiscal quarter of 2025, it reported a 14% growth in operating and free cash flow, which came in at $850 million.

“Q3 was another solid quarter of execution for PTC,” noted Neil Barua, President and CEO, PTC, as he shared making progress in CAD, PLM, ALM, SLM, and SaaS with new product offerings and enhancements.

During this quarter, the company made $75 million worth of share repurchases as part of its $2 billion authorization.

This week, PTC expanded its collaboration with NVIDIA by announcing the integration of NVIDIA Omniverse technologies into Creo and Windchill to help companies improve product quality, accelerate development, and collaborate more effectively on complex products across their entire lifecycle.

“Today’s most advanced products—from AI hardware to industrial machinery—are more complex, integrated, and engineering-intensive than ever before,” said Barua, noting that with this collaboration, “we’re giving our customers the ability to incorporate design and configuration data in a real-time, immersive simulation environment.”

Earlier this year, PTC released a ServiceMax AI, which will leverage the full documented history of equipment data, service history, and more to help organisations modernize their workflows and field service technicians get more work done in less time.

Latest PTC Inc. (PTC) Stock News and Developments

Final Thoughts on Digital Twins & Clean Energy

Digital twin technology has emerged as an effective instrument for optimizing renewable energy systems. While its potential to maximize efficiency, forecasting, and system integration is indisputable, it also suffers from drawbacks.

It is only by overcoming data availability challenges, managing complex modeling environments, and building cost-effective, scalable solutions that true adoption can be achieved. 

So, as the world makes a shift to renewable energy sources in order to reduce carbon emissions and combat climate change, digital twins stand to define the next era of green energy.

References:

1. Semeraro, C., Aljaghoub, H., Al-Ali, H.K.M.H., Abdelkareem, M.A., & Olabi, A.G. “Harnessing the future: Exploring digital twin applications and implications in renewable energy.” Energy Nexus, vol. 18, 1 June 2025, p. 100415. ScienceDirect. https://doi.org/10.1016/j.nexus.2025.100415