Matteo Panero: Hi Álvaro, thank you for accepting this interview. To start, could you briefly tell us about your academic and professional journey, and how your interest in renewable energy research developed?
Álvaro García: Sure, I am an industrial engineer and I earned my master’s degree and PhD from the Technical University of Madrid. My career has always been focused on solving real-world combinatorial problems, especially in the fields of production and logistics. In 2011, I founded baobab solutions, a company that allows me to combine my passions for research, teaching, and delivering hard-dollar saving solutions to companies . As a result of baobab’s experience in the field of energy I developed my interest in wind energy optimization, a field with that perfectly illustrates the potential for collaboration between academia and industry.
At baobab, we also work on projects for corporate clients, addressing real problems that allow us to apply the knowledge resulting from our research. Additionally, we participate in funded projects, which not only improve our technological capabilities but also allow us to share our solutions with the scientific community. This combined approach of research and practical application is at the core of our work.
Matteo Panero: In your work, you have participated in various international conferences, including the Summer Conference in Valencia in 2024. What motivated you to participate, and what were the most important outcomes of that event?
Álvaro García: I also attended last year’s conference in Milan, which was a fantastic experience. What I appreciate most about these events organized by the Decision Science Alliance (DSA) is the goal of bringing together researchers and professionals. I am convinced that integrating the academic and business worlds is crucial for addressing complex problems in a more practical way.
In Valencia, I had the opportunity to strengthen existing collaborations and initiate new ones, such as with a researcher who will soon start an his industrial PhD program in collaboration wIith us and the Polytechnic University of Valencia. It is precisely these kinds of connections that make these conferences so valuable.
Matteo Panero: In your latest study, you focused on optimizing turbine placement in wind farms. Why is this such a crucial factor for maximizing energy production?
Álvaro García: The arrangement of turbines has a significant impact on the efficiency of a wind farm. If turbines are too close together, they interfere with each other, reducing the amount of wind available for each. Conversely, placing them in areas with more wind ensures greater production. The challenge is to find the right balance between these two factors, as an optimal arrangement can significantly increase long-term revenues without incurring additional operational costs.
The interesting part is that, once a wind farm is built, operational costs generally remain stable. Therefore, optimizing turbine placement means obtaining more energy over the life of the farm without further investment. It’s a strategy that increases profitability without raising costs. Thanks to this approach, companies can reinvest the generated profits into new projects or improvements, making energy more accessible overall.
Matteo Panero (follow-up): It’s fascinating how optimization can increase production without additional costs. In practice, how does this process work, and how much can it impact the results?
Álvaro García: Exactly, this is the crux of the issue. When we optimize turbine placement, we can make the most of the available wind for decades, without additional operational costs. By simply improving placement, a wind farm can produce up to 10% more energy, which represents a significant increase in revenue for the company. It’s a strategy that generates immense long-term value.
Matteo Panero: In comparing different optimization methods, you found that random biased optimization is particularly effective. How does this approach differ from traditional techniques like integer programming or particle swarm optimization (PSO)?
Álvaro García: Integer programming works well for small problems, but it becomes ineffective at larger scales. Random biased optimization, on the other hand, is a method that allows us to explore many more combinations of solutions in a smarter and more flexible way. We don’t limit ourselves to selecting the best solution in a single step; instead, we create a variety of possible solutions, exploring them in a guided manner.
This technique, when combined with PSO, allows us to achieve even better results. We have discovered that by combining these two techniques, we can improve the efficiency of a wind farm by 10% compared to traditional solutions already in use in the industry.
Matteo Panero (follow-up): This 10% improvement in energy production is significant. Can it also be applied to other industries?
Álvaro García: Yes, absolutely. This approach is applicable to many other industries, such as logistics and manufacturing. In these sectors, similar problems are faced, where there are many variables to consider, and the optimal choice is not always obvious. Combinatorial optimization is a very powerful technique that can be used to solve complex problems in sectors like healthcare, resource management, and logistics. Ultimately, any industry facing complex decision-making with many options could benefit from these techniques.
Matteo Panero: You mentioned the relationship between research and companies. How has the attitude of companies toward using optimization techniques changed over time?
Álvaro García: When we started baobab, it was very challenging to convey what we did to companies, especially when we talked about “operations research.” It was an abstract concept for many, and there was skepticism about whether we could find better solutions than those developed by business experts over decades. Often, we heard comments like, “I’ve worked in this factory for 20 years; how can an algorithm do better than me?” This kind of distrust was common.
Now, thanks to machine learning and artificial intelligence, companies are much more open. The concept of “machines that learn” is easier to understand, and people are accustomed to seeing practical applications of these technologies, such as sales forecasting models or image classification. As a result, it is easier for us to explain how optimization can improve their processes and provide tangible results.
Today, companies trust the mathematics and models we propose much more; the challenge is no longer to convince them of the validity of the technology but to demonstrate that the investment is worth the benefits gained. The focus has shifted to proving the economic return because trust in technology is now established.
Matteo Panero: Looking to the future, do you see new areas of research or emerging applications?
Álvaro García: Certainly, there are several promising lines of research. Historically, machine learning and operations research have been two separate worlds, but now there is a growing integration between these approaches. For example, techniques like reinforcement learning are showing tremendous potential for further improving business decisions.
In the future, I see the possibility of combining these tools to tackle even more complex problems. The idea is not only to use machine learning for predictive data but to integrate it directly into operational decision-making processes to generate optimal solutions in real time. It’s a rapidly growing research field that could provide extraordinary solutions for sectors like, but not only, energy, logistics, and manufacturing.
Matteo Panero: Finally, how do you see the future of decision science and what can its greatest contributions be?
Álvaro García: Decision science has enormous potential in fostering collaboration between academia and industry. This will enable us to tackle problems in a more realistic and efficient way, as well as to open new avenues for addressing complex issues such as demographics and natural disasters. I am convinced that the DSA will play a central role in this evolution, facilitating collaborations and promoting innovation on a global scale.
Matteo Panero: Thank you very much, Álvaro, for your time and availability. We look forward to having you with us again at the next DSA conference.
Álvaro García: Thank you, Matteo! I will do my best to be there; I definitely don’t want to miss it!
