How can digital twins effectively support the energy transition?

AI-powered digital twins — digital replications of the physical world — have huge potential for use in renewable energy systems, and are already being deployed in the sector to improve efficiency, safety and sustainability.

However, while digital twins’ ability to replicate and interact with complex systems has made them a cornerstone of innovation across industries, scientists from the University of Sharjah have cautioned that current models still face notable limitations that restrict their full potential in harnessing energy from renewable sources.

“Digital twins are highly effective in optimizing renewable energy systems,” the researchers wrote in the journal Energy Nexus. “Yet, each energy source presents unique challenges — ranging from data variability and environmental conditions to system complexity — that can limit the performance of digital twin technologies, despite their considerable promise in improving energy generation and management.”

In their study, the researchers conducted an extensive review of existing literature on the application of digital twins in renewable energy systems. They examined various contexts, functions, lifecycles and architectural frameworks to understand how digital twins are currently being utilised and where gaps remain.

To extract meaningful insights, the researchers employed artificial intelligence, machine learning and natural language processing to analyse large volumes of raw data and uncover structured patterns, concepts and emerging trends.

This in-depth analysis allowed them to identify research gaps, propose new directions and outline challenges that must be addressed to fully harness the potential of digital twin technology in the renewable energy sector.

The research team summarised their most significant findings across five major energy sources: wind, solar, geothermal, hydroelectric and biomass. Each source presents unique opportunities and challenges, with the study offering a comprehensive overview of how digital twins can be tailored to optimise performance in each domain.

The findings revealed that digital twins offer significant advantages across various renewable energy systems:

With wind energy, digital twins can predict unknown parameters and correct inaccurate measurements, enhancing system reliability and performance.

In solar energy, they help identify key factors that influence efficiency and output power, enabling better system design and optimisation.

Digital twins can simulate the entire operational process in geothermal energy — particularly drilling — facilitating cost analysis and reducing both time and expenses.

With hydroelectric energy, the AI-driven models simulate system dynamics to identify influencing factors. In older hydro plants, they are used to mitigate the impact of worker fatigue on productivity.

In biomass energy, digital twins improve performance and management by offering deep insights into operational processes and plant configurations.

But, importantly, the study identified critical limitations in the application of digital twin technology across these energy sources, underscoring the need for more robust models that can address specific challenges unique to each renewable energy system.

The authors identified several limitations in the application of digital twins across different renewable energy systems:

Wind energy: Digital twins face challenges in accurately modelling and monitoring environmental conditions. They struggle to simulate critical factors such as blade erosion, gearbox degradation and electrical system performance — particularly in aging turbines.

Solar energy: Despite their potential, digital twins still fall short in reliably predicting long-term performance. They have difficulty tracking panel degradation and accounting for environmental influences over time, which affects their accuracy and usefulness.

Geothermal energy: A major obstacle is the lack of high-quality data, which hampers the ability of digital twins to simulate geological uncertainties and subsurface conditions. The technology also faces complexity in modelling the long-term behaviour of geothermal systems, including heat transfer and fluid flow dynamics.

Hydroelectric energy: In hydroelectric projects, digital twins face challenges in accurately modelling water flow variability and in capturing environmental and ecological constraints. These limitations reduce their effectiveness in optimising system performance and sustainability.

Biomass energy: When used with biomass energy systems, digital twins still struggle to simulate the entire production supply chain. They fall short in providing precise models for biological processes, biomass conversion and the complex biochemical and thermochemical reactions involved.

To address these challenges, the researchers are offering a set of guidelines and a research roadmap aimed at helping scientists enhance the reliability and precision of digital twin technologies.

Their recommendations focus on improving data collection methods, advancing modelling techniques and expanding computational capabilities to ensure digital twins can deliver trustworthy insights for decision-making and system optimisation.

The study, ‘Harnessing the future: Exploring digital twin applications and implications in renewable energy’, by Concetta Semeraro, Haya Aljaghoub, Hamad Khalid Mohamed Hussain Al-Ali, Mohammad Ali Abdelkareem and Abdul Ghani Olabi, can be found at DOI: 10.1016/j.nexus.2025.100415.

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