Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

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dc.contributor.authorCavus, Muhammed
dc.contributor.authorUgurluoglu, Yusuf Furkan
dc.contributor.authorAyan, Huseyin
dc.contributor.authorAllahham, Adib
dc.contributor.authorAdhikari, Kabita
dc.contributor.authorGiaouris, Damian
dc.contributor.authorBlaabjerg, Frede
dc.date.accessioned2024-02-23T14:35:06Z
dc.date.available2024-02-23T14:35:06Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractSwitched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system's operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model's computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model's accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.en_US
dc.identifier.doi10.3390/app132111744
dc.identifier.issn2076-3417
dc.identifier.issue21en_US
dc.identifier.urihttps://doi.org/10.3390/app132111744
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15864
dc.identifier.volume13en_US
dc.identifier.wosWOS:001121062800001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAuto-Regressiveen_US
dc.subjectControl And Optimizationen_US
dc.subjectEnergy Managementen_US
dc.subjectRecurrent Neural Networken_US
dc.subjectLong Short-Term Memoryen_US
dc.subjectMicrogriden_US
dc.subjectSwitched Model Predictive Controlen_US
dc.titleSwitched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgridsen_US
dc.typeArticleen_US

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