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Artificial intelligence to support the deployment of renewable energy generators

A doctoral thesis on artificial intelligence applied to micro-grids is presented at Tecnun

PhotoCommunication Service/

07 | 06 | 2021

Fermín Rodríguez Lalanne, PhD in Applied Engineering at Tecnun , has presented his thesis on artificial intelligence applied to microgrids. Specifically, the work of this engineer explores three tools for predicting renewable generation and energy demand based on artificial intelligence to improve the control of smart grids.

 "One of the main problems with renewable energies today is their high degree of uncertainty, because it is not known precisely how much energy they are going to inject into the system at any given moment," says Rodríguez. The aim of his thesis, successfully achieved, is "to be able to tell the different electricity system operators how much energy is going to be generated in the next 15 minutes, based on data from the last 24 hours, in order to favour the control of the electricity grid or microgrid".

The first of the tools developed is aimed at predicting different variables that allow us to know what the production of the renewable generators and the energy demand of the loads will be. These tools are based on algorithms provided by artificial intelligence, taking into account only information from the location where the prediction is to be made.

The second aims to use data not only from the target location where the prediction is to be made, but also from other locations close to the points where generation takes place, to estimate what the future energy production will be.

The third tool seeks to establish confidence intervals for the predictions made in the two previous tools. "It should be borne in mind that it is just as important to make an accurate prediction as it is to establish a range of uncertainty within which this prediction will be produced," continues the young engineer.

Both to meet the requirements for renewable generators to connect to the conventional grid, and to improve the control that governs microgrids, this thesis studies the possibility of making generators and loads more intelligent, predicting what their status will be in the near future.

This would allow renewable generators connected to the traditional grid to compete in the intraday energy market, as well as to offer the system auxiliary services such as frequency regulation, reactive contribution or black start. Likewise, microgrids could see their controls optimised, as it would allow them to know, with a certain degree of uncertainty, what their status will be in that horizon and act accordingly.

For the future, the objectives are clear, as the researcher from Ceit, the technology centre where the project is being developed, points out. The first is "to achieve greater precision so that control is finer, and the second is to extend the time horizon so that the tools can be used by operators for all kinds of activities", he concludes.Communication Service

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