Ayesa develops a quantum solution set to revolutionise the reliability and efficiency of power grids

The company has developed a system based on quantum computing, which will optimise the topology of Spain’s energy networks to avoid blackouts.

The platform has already been tested in real-world scenarios, where it produced stable network configurations in 99% of cases, while also demonstrating an extremely high level of precision.

Ayesa, a global provider of engineering and technology services, has developed a cutting-edge quantum solution to optimise Spain’s electricity distribution network. The system, created through the company’s innovation centre i3B, is set to transform the reliability and efficiency of the network by using advanced QUBO optimisation models to minimise energy losses and maximise availability.

This initiative addresses challenges such as increasing demand for energy as well as the growing complexity of modern energy systems, marked by the integration of renewables, storage solutions and bidirectional power flows. These new needs require sophisticated platforms able to adapt in real time.

The recent blackout in Spain has shown that traditional, hierarchical approaches are no longer effective. In contrast, quantum computing, with its analytical capabilities and intrinsic parallelism, is able to accelerate and support traditional hierarchical systems in analysing and predicting energy flows in all directions. As such, it has emerged as an invaluable tool in preventing critical events like the one we saw just a few weeks ago.

By leveraging quantum computing, Ayesa’s solution maximises the availability and efficiency of the network, while also anticipating future demand. Managing a distribution network that supplies power to over 11 million customers requires a system capable of processing large volumes of data, predicting dynamic scenarios and improving the performance of network components in real time.

Optimising network topology involves determining the best configuration for switching points in medium-voltage networks in order to minimise energy losses and ensure efficient operations. However, this task, which is combinatorial in nature, means evaluating a vast number of possible configurations and therefore requires significant computational power. Traditional methods often prove insufficient for managing this complexity, leading to prolonged processing times and less-than-ideal outcomes.

This is where quantum computing comes in, offering a truly transformative solution to overcome these challenges. By using a quadratic unconstrained binary optimisation (QUBO) model adapted to various quantum tools, the system turns the problem of network topology into something that can be handled by quantum solutions, such as gate-based quantum computers and quantum annealers. This approach means all solutions can be explored, significantly reducing the time needed to identify optimal configurations. In fact, the quantum system can deliver results in just 15 minutes, a significant improvement compared to the hours it takes traditional solutions.

Aitor Moreno Fernández de Leceta, Head of Quantum Computing at Ayesa, highlights the enormous potential of this quantum computing-based approach: ‘By minimising energy losses during transmission and distribution, network efficiency and reliability are significantly enhanced. This is particularly important as renewable energy sources are integrated into the power grid, something that requires an adaptive and flexible solution able to handle fluctuating levels of production’.

He adds: ‘Quantum optimisation helps us plan for future scenarios, enabling the network to adapt proactively to changes in demand, generation and infrastructure. As energy networks expand to accommodate more and more users as well as incorporate renewable energy, having a scalable and adaptable optimisation framework is becoming an increasingly urgent priority.’

The advantages of Ayesa’s solution

The quantum solution developed as part of this project offers a host of transformative benefits, such as a substantial reduction in processing times, improved network efficiency, support for long-term planning, and the scalability inherent in quantum frameworks.

The project is based on a sophisticated hybrid quantum-classical approach. By combining the problem-solving capabilities of quantum solvers with traditional computing systems, it strikes an optimal balance between computational efficiency and real-world usability.

The system relies on historical data, predictive analytics and graph-based models for decision-making. This data-driven approach ensures the optimisation framework is aligned with real conditions, thereby enhancing its reliability and relevance.

Real-world testing has been a key feature of the project. As such, the quantum solution has been evaluated in a number of practical scenarios, where it produced stable network configurations in 99% of cases. This level of precision not only validates the model but also demonstrates the practical applications quantum computing can have in industrial settings.