Microgrid Operational Planning using a Hybrid Neural Network with Resource-aware Scenario Selection

Darville, Joshua; Curia, Jin; Celik, Nurcin

doi:10.1016/j.simpat.2022.102583

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…As previously stated in the paper, artificial intelligence-mainly machine and deep learning-is used in forecasting. As highlighted in the following papers [106][107][108][109][110][111][112][113][114][115][116][117][118], artificial intelligence, in particular artificial neural networks and fuzzy/neuro-fuzzy systems, can also be used in the management of MGs and NMGs. These papers are highlighted in the followings in order to give to the reader an overview on how AI is used in the domain.…”

Section: Artificial Intelligencementioning

confidence: 99%

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A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

2022

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Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

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Section: Artificial Intelligencementioning

confidence: 99%

“…The proposed approach increases the utilization of local renewable energy sources and improves residents' experience. In [107], a 2-stage reconfigurable framework is proposed for near real-time MG operational planning. An artificial neural network is used to predict energy dispatch decisions.…”

Section: Artificial Intelligencementioning

confidence: 99%