Abstract
Empowering the consumers will increase the complexity of local communities’ management. Enabling bidirectional communication and appliances to become smarter can be a huge step toward implementing demand response. However, a solution capable of providing the right knowledge and tools must be developed. The authors thereby propose a methodology to manage the active consumers on Demand Response (DR) events optimally, considering the context in which it is triggered. The distribution system operator detects a voltage violation and requests a load reduction to the aggregators. In this study, to test a performance rate designed by the authors to deal with response uncertainty, a comparison between requested and actual reduction is done. The proposed methodology was applied to three scenarios where the goal is predicting the response from the consumers using artificial neural networks, by changing the features used in the input.
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Acknowledgments
This article is a result of the project RETINA (NORTE-01–0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Cátia Silva is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with PhD grant reference SFRH/BD/144200/2019. Pedro Faria is supported by FCT with grant CEECIND/01423/2021. The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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Silva, C., Faria, P., Vale, Z. (2023). DR Participants’ Actual Response Prediction Using Artificial Neural Networks. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_17
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DOI: https://doi.org/10.1007/978-3-031-18050-7_17
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