Artificial intelligence at the service of electricity grids: from power electronics to energy management

Introduction

The energy transition towards a more sustainable and decentralised model is profoundly transforming electricity grids. The emergence of renewable energies, the electrification of transport and the rise of self-consumption are introducing new technical and economic challenges that require innovative solutions. In this context, artificial intelligence (AI) is emerging as a key tool to address the increasing complexity of electricity grids, both in the design and control of power electronics equipment and in the energy and economic management of electricity systems.

But what exactly does it mean to apply ‘artificial intelligence’ in this field? And, above all, how is it already being used in real projects?

What is artificial intelligence?

When we talk about artificial intelligence, it is easy to imagine quasi-autonomous systems, capable of making complex decisions as if they were human. But in practice, AI is not a magical black box or a mysterious entity. AI is, at its core, a set of algorithms designed to extract patterns and make data-driven decisions.

More specifically, within AI we find branches such as machine learning and deep learning, which are characterised by their ability to learn from large volumes of data and adjust their behaviour without the need for explicit programming of all the rules.

Why is this relevant in electricity grids? Because the modern electricity system generates huge amounts of data: voltage and current measurements, weather forecasts, demand curves, battery charge states, etc. AI acts as a layer that transforms this data into actionable knowledge, making it possible to optimise processes that previously relied exclusively on static models or human expertise.

In the field of power electronics, for example, AI algorithms make it possible to tune the controllers of converters, inverters and other equipment to better respond to varying conditions, or to set up predictive maintenance operations.

In energy management, AI-based predictive models make it easier to balance supply and demand in systems with a high penetration of renewables, where variability is a constant.

La IA, por tanto, no sustituye a los modelos físicos ni al conocimiento ingenieril: los complementa y los potencia, permitiendo que los sistemas sean más adaptativos, robustos y eficientes.

Real use cases

To better understand the role of AI in electricity grids, let us look at two concrete examples where it is already successfully applied or under investigation.

Predictive control and machine learning in wind turbines

One of the key challenges in wind turbine operation is to maximise the power captured from the wind while minimising the mechanical loads on the structure. Traditionally, this balance is addressed by linear controllers designed for a given operating range.

In a paper released by MathWorks, a model-based predictive controller (MPC) was used to manage wind turbine variables in advance, predicting system behaviour in the face of disturbances. What was innovative was that, on this basis, a machine learning model was incorporated that dynamically adjusted the MPC parameters according to the operating conditions and measured loads.

This hybrid approach made it possible to reduce turbine loads without significantly compromising power output, opening the door to more durable and efficient wind turbine operation, especially in environments with high wind variability.

Virtual power plant in Guadalajara (Castilla-La Mancha, Spain)

Another outstanding example is the Rural VPP project developed in the region of Guadalajara (Spain). This initiative, a pioneer at national level, seeks to integrate various renewable sources – mainly solar photovoltaic – together with energy storage systems and exchange grids between prosumers, to create a virtual plant able to behave as if it were a conventional power plant for the electricity system.

The use of AI algorithms in this project is key to coordinate and optimise the operation of the various distributed assets. AI allows, among other things:

• Predict local generation and demand based on weather and historical consumption variables.
• Manage energy storage in real time, deciding when to store and when to release energy to maximise profitability and secure supply.
• Participate in grid services markets, offering regulation capacity or frequency support in a distributed manner.

Such virtual plants represent a fundamental step towards more resilient, flexible and sustainable power grids, where artificial intelligence acts as the brain that coordinates and optimises energy flows.

Conclusion

Artificial intelligence is moving from being a promise to an everyday tool, including in the development and operation of modern power grids. From improving power electronics controllers to optimising distributed energy systems, AI brings a data-driven approach that complements traditional methods and opens up new possibilities for efficiency, sustainability and resilience.

In a context of global energy transition, understanding and using these technologies is not only an opportunity, but a need to build the electricity system of the future.

Referencias

[1] – Wikipedia, Inteligencia Artificial

[2] – Developing and Testing Model Predictive Control Algorithms for Wind Turbines for Field Testing

[3] – Enhancing Wind Turbines with Model Predictive Control

[4] – Rural VPP, UAH

[5] – https://www.next-kraftwerke.com/vpp/virtual-power-plant