A comprehensive review of microgrid control methods: Focus on AI, optimization, and predictive techniques

•A review of recent control techniques, with a focus on AI, optimization, and predictive methods, is presented.•Control techniques are reviewed based on the control objectives, architecture, and microgrid characteristics.•This assessment highlights the benefits and limitations of various control met...

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Veröffentlicht in:	Computers & electrical engineering Jg. 125; S. 110442
Hauptverfasser:	Limouni, Tariq, Yaagoubi, Reda, Bouziane, Khalid, Guissi, Khalid, Baali, El Houssain
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2025
Schlagworte:	Artificial intelligence Fault detection Hybrid control technique Metaheuristics algorithms Microgrid control Model predictive control Optimization algorithms Power and energy management MPC DE DEMPC DG MILP LP PV LR ELN ELM Optimization algorithms DL QTLBO AWPSO CDM BBO MLP DT Model predictive control BPNN HDMPC RNN FFT MB ssNN MG FS-MPC ANFIS LSA DMPC QP ML BSO MIQP FFNN AC HSO SMA AI EMS AIS VSG PSO SFLA FCN EV MIP GAN FPA RL FA EMPC HBMO MAS BA ANN CNN ABC Microgrid control SoC MVO BHO SVM FHO COOT SA CSA Metaheuristics algorithms GSA ISA GOA GA SFO GWO LSTM ESS WOA MINLP PID kNN SMPC Power and energy management ACO RCGA SSO SCA DER Fault detection WCA BAS ARIMA PI Hybrid control technique Artificial intelligence DC
ISSN:	0045-7906
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Zusammenfassung:	•A review of recent control techniques, with a focus on AI, optimization, and predictive methods, is presented.•Control techniques are reviewed based on the control objectives, architecture, and microgrid characteristics.•This assessment highlights the benefits and limitations of various control methods.•This review explores the potential and challenges for hybridizing control techniques and emphasizes the need for more intelligent solutions.•Key research directions for future studies on microgrid control were identified. With the depletion of fossil fuels, the integration of renewable energy sources as distributed energy resources has become mandatory. However, the uncertainty and intermittent nature of these sources introduce significant challenges to their integration into microgrids. Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods applied in microgrid management was conducted with a focus on AI, optimization, and predictive techniques. These advanced and intelligent control methods were chosen for their potential to address current challenges. This study examined the benefits, limitations, and areas for future improvement. In addition, it explores the potential and the challenges of hybrid control techniques, which are less discussed in the literature, to further enhance control system efficiency and performance.
ISSN:	0045-7906
DOI:	10.1016/j.compeleceng.2025.110442