Open Access

Table 7

Recent research conducted in fuel cell integration in DC microgrids.

Energy sources/energy storage devices Controlling and optimization methodologies Significant achievements References
Solar PV, Wind
Fuel Cell, Supercapacitor, Battery
Maximum power points of Solar PV and wind has been gained via Artificial Neural Networks
Energy management system compromising of fuzzy logic controller.
Voltage regulation by non-linear, integral, double-integral and super-twisting sliding mode controllers
DC bus voltage regulation and transient stability of DC microgrid has been achieved.
Stress on battery in sudden fluctuations has been significantly reduced by fuel cells.
Solar PV
Battery, Fuel Cell, Supercapacitor
PV system de-rating method has been utilized under low demand conditions.
Power supply of fuel cell is controlled by reverse sigmoidal function.
Centralized energy management system has been utilized.
System operated properly under transient fault conditions.
Battery discharge rate has been limited using fuel cell with supercapacitors in sudden large demand fluctuations.
Solar PV
Battery, Fuel Cell
Multi-port DC-DC converter has been proposed to connect all RES, ESS and loads.
Reduce multiple power conversions, voltage boost requirements and elements required.
Significant flexibility has been shown in the proposed buck-boost converter architecture.
Maximum power point tracking of RES has also been achieved
Battery, Fuel Cell, Hydrogen energy storage
Two level control is proposed for the static and dynamic performance of DC microgrid.
Centralized energy management and quick reaching law-based global-terminal sliding mode control for device level has been proposed.
Proposed control method had shown a fast transient response with settling time of 0.099 s.
Accurate steady-state performance has been observed with 0.071% steady state error.
AC microgrid coupled with DC microgrid with distributed generation.
Battery, Fuel Cell, Supercapacitors
Proposed an adaptive energy management strategy with a modified interlinking converter.
Proposed topology improves power transfer in hybrid microgrid.
7.92% voltage improvement has been achieved in AC sub grid.
0.42% voltage improvement achieved DC microgrid.
Grid connected home DC microgrid.
Battery bank, multi stack fuel cell system.
Multi-stage and multi-variable fuzzy logic-based control system has been proposed for power management.
Proposed microgrid management system considers microgrid performance, lifespan of energy storage and grid power costs.
Proposed controlling reduces battery bank degradation up to 180% by utilizing multi stack fuel cells.
Reduce the cost associated with grid connection by 90%.
Solar PV
Battery, Fuel Cell, Supercapacitors
Integral reinforcement learning-based control algorithm has been used for controlling fuel cell in DC microgrids.
Initial stabilization is done by employing twin-delayed deep deterministic policy gradient algorithm.
Proposed topology shows excellent transient and steady state performances with reduced complexity in computation. [64]

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