Application of SMES and Fuel Cell System Combined With Liquid Hydrogen Vehicle Station to Renewable Energy Control

2012 ◽  
Vol 22 (3) ◽  
pp. 5701704-5701704 ◽  
Author(s):  
T. Hamajima ◽  
H. Amata ◽  
T. Iwasaki ◽  
N. Atomura ◽  
M. Tsuda ◽  
...  
Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6111
Author(s):  
Nicu Bizon ◽  
Mircea Raceanu ◽  
Emmanouel Koudoumas ◽  
Adriana Marinoiu ◽  
Emmanuel Karapidakis ◽  
...  

In this paper, the optimal and safe operation of a hybrid power system based on a fuel cell system and renewable energy sources is analyzed. The needed DC power resulting from the power flow balance on the DC bus is ensured by the FC system via the air regulator or the fuel regulator controlled by the power-tracking control reference or both regulators using a switched mode of the above-mentioned reference. The optimal operation of a fuel cell system is ensured by a search for the maximum of multicriteria-based optimization functions focused on fuel economy under perturbation, such as variable renewable energy and dynamic load on the DC bus. Two search controllers based on the global extremum seeking scheme are involved in this search via the remaining fueling regulator and the boost DC–DC converter. Thus, the fuel economy strategies based on the control of the air regulator and the fuel regulator, respectively, on the control of both fueling regulators are analyzed in this study. The fuel savings compared to fuel consumed using the static feed-forward control are 6.63%, 4.36% and 13.72%, respectively, under dynamic load but without renewable power. With renewable power, the needed fuel cell power on the DC bus is lower, so the fuel cell system operates more efficiently. These percentages are increased to 7.28%, 4.94% and 14.97%.


2022 ◽  
Author(s):  
Pim C. de Boer ◽  
Albert d. Wit ◽  
Roel C. van Benthem

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Abdulbaset Abdulhamed Mohamed Nureddin ◽  
Javad Rahebi ◽  
Adel Ab-BelKhair

Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.


2012 ◽  
Vol 132 (10) ◽  
pp. 997-1002 ◽  
Author(s):  
Koji Maekawa ◽  
Kenji Takahara ◽  
Toshinori Kajiwara

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