As the world urgently works to reduce carbon emissions and combat climate change, researchers at the University of Sharjah are turning to a cutting-edge technology that could reshape the future of energy: AI-powered digital twins.

According to the researchers, these digital replicas of the physical world have the potential to transform energy production, management, and optimization across various clean energy platforms, accelerating the shift away from fossil fuels—which environmental scientists link to global warming.

The ability of digital twins to replicate and interact with complex systems makes them a cornerstone of innovation across industries, driving efficiency gains, cost reductions, and the development of new solutions.

However, scientists warn that current digital twin models still face significant limitations that restrict their ability to fully unlock the potential of renewable energy sources such as wind, solar, geothermal, hydropower, and biomass.

The researchers write in the journal Energy Nexus: "Digital twins are highly effective in optimizing renewable energy systems."

"However, each energy source faces unique challenges—from data variability and environmental conditions to system complexity—that can limit the performance of digital twin technology, despite its immense potential to improve energy production and management."

In their study, the authors conducted an extensive review of existing literature on the application of digital twins in renewable energy systems. They examined various environmental, functional, lifecycle, and architectural frameworks to understand current applications and identify existing gaps.

To extract meaningful insights, the researchers employed advanced text-mining techniques, leveraging artificial intelligence, machine learning, and natural language processing. This rigorous scientific approach enabled them to analyze large volumes of raw data and uncover structured patterns, concepts, and emerging trends.

Through in-depth analysis, the authors reached several key conclusions. They identified research gaps, proposed new directions, and outlined the challenges that must be addressed to fully harness the potential of digital twin technology in the renewable energy sector.

After detailing the integration of digital twins across various renewable energy applications, the authors summarized their most significant findings in the five major energy domains: wind, solar, geothermal, hydropower, and biomass. Each energy type presents unique opportunities and challenges, and the study provides a comprehensive overview of how digital twins can be tailored to optimize performance in each domain.

The research shows that digital twins offer substantial advantages for various renewable energy systems:

Wind Energy: Digital twins can predict unknown parameters and correct inaccurate measurements, thereby improving system reliability and performance.

Solar Energy: They help identify key factors affecting efficiency and output power, enabling better system design and optimization.

Geothermal Energy: Digital twins can simulate the entire operational process (especially drilling), facilitating cost analysis and reducing time and expenses.

Hydropower: AI-driven models simulate system dynamics to identify influencing factors. In older hydropower plants, these models are used to mitigate the impact of worker fatigue on productivity.

Biomass Energy: Digital twins enhance performance and management by providing deep insights into operational processes and plant configurations.

Yet the authors' contribution stands out particularly in highlighting the key limitations of digital twin technology in these energy domains. Their analysis underscores the need for more robust models capable of addressing the specific challenges unique to each renewable energy system.

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

Wind Energy: Digital twins face challenges in accurately modeling and monitoring environmental conditions. They struggle to simulate critical factors such as blade erosion, gearbox degradation, and electrical system performance, especially in aging wind turbines.

Solar Energy: Despite the great potential of digital twin technology, it remains inadequate in reliably predicting long-term solar performance. They have difficulty tracking solar panel aging and incorporating environmental impacts into long-term assessments, affecting their accuracy and practicality.

Geothermal Energy: A major barrier is the lack of high-quality data, which hinders digital twins' ability to simulate geological uncertainties and subsurface conditions. The technology also faces complexity in modeling the long-term behavior of geothermal systems, including heat transfer and fluid flow dynamics.

Hydropower: When applied to hydropower projects, digital twins encounter difficulties in precisely simulating water flow variations and capturing environmental and ecological constraints. These limitations reduce their effectiveness in optimizing system performance and sustainability.

Biomass Energy: When used for biomass energy systems, digital twin technology still struggles to simulate the entire production supply chain. It cannot provide accurate models for biological processes, biomass conversion, and the complex biochemical and thermochemical reactions involved.

The authors emphasize the broad implications of these shortcomings for the renewable energy sector. To address these challenges, they offer a set of guidelines and a research roadmap aimed at helping scientists improve the reliability and accuracy of digital twin technology.

Their recommendations focus on enhancing data collection methods, advancing modeling techniques, and expanding computational capabilities to ensure digital twins can provide reliable insights for decision-making and system optimization.