Publication
A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| datacite.subject.sdg | 07:Energias Renováveis e Acessíveis | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| datacite.subject.sdg | 12:Produção e Consumo Sustentáveis | |
| datacite.subject.sdg | 13:Ação Climática | |
| dc.contributor.author | dos Santos, Joao | |
| dc.contributor.author | Algarvio, Hugo | |
| dc.date.accessioned | 2025-04-16T15:21:31Z | |
| dc.date.available | 2025-04-16T15:21:31Z | |
| dc.date.issued | 2025-03 | |
| dc.description.abstract | ABSTRACT: The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively. | eng |
| dc.identifier.citation | dos Santos, J., & Algarvio, H. (2025). A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations. In: Energies, vol. 18(6), article 1467. https://doi.org/10.3390/en18061467 | |
| dc.identifier.doi | 10.3390/en18061467 | |
| dc.identifier.issn | 1996-1073 | |
| dc.identifier.uri | http://hdl.handle.net/10400.9/5650 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation | Tools for the Design and modelling of new markets and negotiation mechanisms for a ~100% Renewable European Power Systems | |
| dc.relation.hasversion | https://www.mdpi.com/1996-1073/18/6/1467 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Renewable energy sources | |
| dc.subject | Energy markets | |
| dc.subject | Variable renewable energy sources | |
| dc.subject | Forecast | |
| dc.subject | Machine learning | |
| dc.title | A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Tools for the Design and modelling of new markets and negotiation mechanisms for a ~100% Renewable European Power Systems | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/864276/EU | |
| oaire.citation.issue | 6 | |
| oaire.citation.title | Energies | |
| oaire.citation.volume | 18 | |
| oaire.fundingStream | H2020 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | dos Santos | |
| person.familyName | Algarvio | |
| person.givenName | Joao | |
| person.givenName | Hugo | |
| person.identifier.orcid | 0009-0007-5995-8060 | |
| person.identifier.orcid | 0000-0002-4129-838X | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | European Commission | |
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