August 24, 2026
How can renewable energy be generated in a northern environment? How can wind turbines be kept operational despite icing? How can diesel consumption be reduced in a remote community without compromising the reliability of its power grid? And how can excess renewable energy be stored?
These questions, though seemingly very different, have one thing in common: they form the foundation of ÉTS Professor Adrian Ilinca's research. A specialist in renewable energy, fluid dynamics, and energy system optimization, he develops solutions to reduce greenhouse gas emissions while accounting for the real-world constraints under which these systems operate.
His field of research is extensive. Wind and solar energy, hydrogen, energy storage, off-grid energy systems, diesel engines, fluid dynamics, artificial intelligence, and climate change adaptation... His research projects span a wide range of topics, but they all share a common goal: to design energy systems that are more efficient and better suited to real-world conditions.
Keeping wind turbines running despite icing
One area that has shaped his career is wind turbine icing. In cold regions, ice can accumulate on the blades and completely alter their aerodynamic profile. This change reduces turbine performance and causes vibrations, not to mention the risk posed by ice thrown off by the rotating blades.
This phenomenon is complex, as the amount and shape of the ice depend on numerous factors: temperature, weather conditions, liquid water content, droplet diameter, blade shape, and operating conditions.
To understand and predict this behaviour, Adrian Ilinca and his team combine computational fluid dynamics simulations, heat and mass transfer models, and aerodynamic performance analysis. The goal is to determine not only how much ice can accumulate, but also where it forms and how it affects the blade’s behaviour.
There is also an important economic dimension. To determine whether installing a de-icing system is cost-effective, production losses due to ice must be assessed and compared with the cost of implementing a de-icing system.
When diesel becomes an ally of renewable energy
Another major focus of Adrian Ilinca's research involves isolated power grids. In mines, northern communities, or remote locations, diesel is often essential for ensuring a reliable power supply. But when wind or solar power is added to the mix, an unexpected problem can arise.
When renewable energy sources meet a large portion of electricity needs, the diesel engine must operate at much lower loads. But these engines are generally designed to operate at a relatively high load. At low-load operation, their efficiency decreases and their specific fuel consumption increases.
Therefore, rather than viewing diesel and renewable energy as two competing technologies, Adrian Ilinca aims to make them work intelligently together.
One approach being explored is pneumatic hybridization of diesel engines. When the engine is operating at low load, its turbocharger does not supply enough air for optimal combustion. Pneumatic supercharging improves combustion conditions and engine performance at low-load operation.
Surplus wind energy can then be stored and used to power electric compressors. The compressed air is injected into the diesel engine operating at low load. This approach aims to improve its low-load operation and reduce losses caused by integrating large amounts of renewable energy. This approach may also enable engine downsizing.
These hybrid systems clearly show the complexity of the energy networks studied by Adrian Ilinca. Each new technology alters the system’s balance. A battery, for example, does more than just store electricity: in northern climates, it must be heated in the winter and sometimes cooled in the summer. These additional requirements must be factored into the calculations.
Forecasting for better decision-making
To manage this complexity, the researcher relies heavily on artificial intelligence and optimization techniques.
In isolated grids, his teams are developing models to predict wind and solar power generation, as well as electricity demand, using historical data and weather forecasts. Algorithms are then used to determine how to allocate energy flows among the various sources and storage systems.
The goal may be to reduce diesel consumption, production costs, greenhouse gas emissions, or a combination of these factors.
Artificial intelligence also plays a role in his work on wind energy. Genetic algorithms have been used to optimize wind turbine design, determine their optimal placement within wind farms, and optimize the integration of various generation and storage sources into isolated power grids. Models based on historical data also help estimate the power supplied by wind turbines during peak periods, such as winter peak loads.
Adapting energy systems to climate change
Energy transition is not just about reducing greenhouse gas emissions. Energy infrastructure must also be adapted to a changing climate and to weather events whose frequency and intensity are changing.
As part of a collaborative project with Ouranos, Adrian Ilinca and his teams are studying the potential impacts of climate change on various energy sectors. Changes in temperature, wind, precipitation, and other climate variables can indeed affect the availability of renewable resources, power generation, and energy demand.
The researchers are using climate projections to assess how these changes might affect energy systems over different time horizons. The goal is to identify vulnerabilities and also opportunities for adaptation, so that the infrastructure designed today can continue to operate efficiently and reliably under future climate conditions.
This work adds a time dimension to energy planning: it is no longer enough to optimize a system based on the current climate; it is also necessary to assess its performance and resilience facing conditions it may encounter over its lifetime.
Storing energy to decarbonize remote sites
Energy storage is another central theme of his research. Together with ÉTS professor Daniel Rousse, Adrian Ilinca has studied various solutions to meet the needs of a mining site, including batteries, hydrogen, pumped-storage hydroelectricity, compressed air, and thermal storage.
The goal is not to determine which technology is universally ideal, but rather which combination best suits a given site, depending on its characteristics. The researchers evaluate the solutions based on various criteria, including their technical and financial feasibility.
Hydrogen is also an important avenue for storing renewable energy. As part of another project, electricity generated by a large wind-and-solar energy farm could be used to produce hydrogen through water electrolysis. However, transporting and storing large quantities of hydrogen is challenging. One solution is to convert it into methanol by reacting it with captured carbon dioxide. This approach would facilitate transportation while simultaneously utilizing captured CO₂ to produce an energy carrier.
Generating energy while protecting the coastline
Adrian Ilinca’s research also extends to protecting coastal areas. His team is highly interested in wave energy converters.
These technologies are not yet widely used because their cost is high relative to the amount of electricity they generate. But their value could be different if an additional function is considered: helping protect the coastline from erosion.
Rather than evaluating only the amount of electricity generated, the researchers are attempting to measure the combined benefits of these infrastructures. A project conducted as part of the FRQNT’s AUDACE program is studying their potential in the Magdalen Islands, where coastal erosion is a major concern.
This approach clearly defines the researcher’s methodology: a technology can be attractive when it is no longer evaluated on a single criterion, but rather on the full range of services it can provide.
Including people in the equation
Technical performance alone, however, is not enough to determine whether an energy project will be truly viable. Its social acceptability is just as critical.
Adrian Ilinca and his teams have developed decision-support tools that enable the comparison of various scenarios by incorporating the perspectives of four stakeholder categories: experts, developers, governments, and the general public.
These multi-criteria tools help identify the scenarios that garner the broadest consensus. They also help pinpoint areas of dissension among the groups. The goal, therefore, is not only to find the technically optimal solution, but also to understand where the differences lie and help reconcile these differing perspectives.
Research that reflects the nature of energy systems
From blade icing to diesel engine operation, from hydrogen to thermal storage, from isolated grids to coastal protection, Adrian Ilinca’s scientific research covers a surprisingly broad spectrum.
Yet this diversity does not reflect a lack of focus. Rather, it highlights the complexity of modern energy systems. Reducing greenhouse gas emissions is not simply a matter of replacing one technology with another. It requires understanding how a system’s components interact, predicting their behaviour, optimizing their operation, and considering economic, environmental, and social constraints.
It is in this arena—between modelling and reality, between artificial intelligence and physical phenomena—that Adrian Ilinca conducts his research. This approach seeks to transform renewable technologies into solutions better adapted to local realities, namely in areas where climate conditions and remoteness make the energy transition more complex.
