RT Generic
T1 Clustering and forecasting of day-ahead electricity supply curves using a market-based distance
A1 Li, Zehang
A1 Alonso Fernández, Andrés Modesto
A1 Elías, Antonio
A1 Morales, Juan M.
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB Gathering knowledge of supply curves in electricity markets is critical to bothenergy producers and regulators. Indeed, power producers strategically plantheir generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features.Against this background, this article introduces a hierarchical clusteringmethod based on a novel weighted-distance that is specially tailored to non bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables.Additionally, the proposed distance is used in a learning procedure by whichexplanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.
SN 2387-0303
YR 2024
FD 2024
LK https://hdl.handle.net/10016/43805
UL https://hdl.handle.net/10016/43805
LA eng
NO The authors gratefully acknowledge financial support from the Spanishgovernment through the Ministry of Science and Innovation projects PID2019-108311GB-I00, PID2020-115460GB-I00, and PID2022-138114NB-I00. Andrés M. Alonso has been a beneficiary of the Google Cloud Research Credits Program carrying out part of the calculations on this platform. Antonio Elías was supported under PAIDI 2020 funded by the Junta de Andalucía and the European Social Fund. The work of Juan M. Morales has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement No 755705).
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RD 20 may. 2024