Open Access
Review
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. |
[58] |
| 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. |
[59] |
| 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 |
[60] |
| Wind 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. |
[61] |
| 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. |
[62] |
| 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%. |
[63] |
| 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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