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Artículo
Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids
Autor/es | Parejo Matos, Antonio
García Caro, Sebastián Personal Vázquez, Enrique Guerrero Alonso, Juan Ignacio Carrasco Muñoz, Alejandro León de Mora, Carlos |
Departamento | Universidad de Sevilla. Departamento de Tecnología Electrónica |
Fecha de publicación | 2024 |
Fecha de depósito | 2024-03-11 |
Publicado en |
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Resumen | In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization ... In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization approaches, stochastic and probabilistic methods are experiencing a growth in their use. However, one of the problems when applying these approaches is the complexity of creating and evaluating the quality of the required stochastic forecasts compared to deterministic forecasts. To mitigate this difficulty, this paper proposes a probabilistic forecasting framework that integrates model creation, their evaluation, and the selection of the best model for predicting. Additionally, two novel methods are proposed for creating scenario sets, and a new metric is defined for evaluating and selecting which model to use. The proposed framework is applied in a case study over a dataset of ten secondary distribution substations from a real distribution network located in Manzanilla (Spain), showing the effect of the selection criteria over the forecasting quality. —This article was motivated by the challenge of probabilistic forecasting inclusion in automatic management systems applied to power distribution networks and microgrids. Modern stochastic management optimization methods are fed with probabilistic forecasts, which offer richer information than classic deterministic forecasting. Therefore, the management systems should be able to automatically train a certain number of forecasting models (e.g., machine learning models), evaluate and compare them, and apply the best ones for obtaining the forecasts to feed the management optimization system. Considering the variety of models, techniques, probabilistic forecast types, and evaluation metrics, it can be unclear how to perform this process. For these reasons, this article proposes a probabilistic forecasting framework that integrates methods for the construction of diverse types of predictions (quantiles, intervals, and scenario sets), their evaluation, and the selection of the best model for performing each required prediction for feeding optimization systems. This framework could help to facilitate the implantation of modern stochastic optimization management systems for distribution networks and microgrids, as it simplifies the forecasting process |
Cita | Parejo Matos, A., García Caro, S., Personal Vázquez, E., Guerrero Alonso, J.I., Carrasco Muñoz, A. y León de Mora, C. (2024). Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids. IEEE Transactions on Automation Science and Engineering, 1-13. https://doi.org/10.1109/TASE.2024.3361651. |
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