A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

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.

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Modeling and control of air stream and hydrogen flow with recirculation in a PEM fuel cell system—I. Control-oriented modeling

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2006.02.031 ◽

2006 ◽

Vol 31 (13) ◽

pp. 1879-1896 ◽

Cited By ~ 120

Author(s):

C BAO ◽

M OUYANG ◽

B YI

Keyword(s):

Fuel Cell ◽

Pem Fuel Cell ◽

Cell System ◽

Fuel Cell System ◽

Modeling And Control ◽

Hydrogen Flow ◽

Air Stream ◽

And Control

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Proton Exchange Membrane Fuel Cell System Identification and Control: Part I — System Dynamics, Modeling and Identification

ASME 2006 Fourth International Conference on Fuel Cell Science, Engineering and Technology, Parts A and B ◽

10.1115/fuelcell2006-97119 ◽

2006 ◽

Author(s):

Yee-Pien Yang ◽

Fu-Cheng Wang ◽

Hsin-Ping Chang ◽

Ying-Wei Ma ◽

Chih-Wei Huang ◽

...

Keyword(s):

Fuel Cell ◽

System Identification ◽

Proton Exchange Membrane ◽

Cell System ◽

Proton Exchange ◽

Time Varying ◽

Fuel Cell System ◽

And Control ◽

Exchange Membrane ◽

Auxiliary Input

This paper consists of two parts to address a systematic method of system identification and control of a proton exchange membrane (PEM) fuel cell. This fuel cell is used for communication devices of small power, involving complex electrochemical reactions of nonlinear and time-varying dynamic properties. From a system point of view, the dynamic model of PEM fuel cell is reduced to a configuration of two inputs, hydrogen and air flow rates, and two outputs, cell voltage and current. The corresponding transfer functions describe linearized subsystem dynamics with finite orders and time-varying parameters, which are expressed as discrete-time auto-regression moving-average with auxiliary input models for system identification by the recursive least square algorithm. In experiments, a pseudo random binary sequence of hydrogen or air flow rate is fed to a single fuel cell device to excite its dynamics. By measuring the corresponding output signals, each subsystem transfer function of reduced order is identified, while the unmodeled, higher-order dynamics and disturbances are described by the auxiliary input term. This provides a basis of adaptive control strategy to improve the fuel cell performance in terms of efficiency, transient and steady state specifications. Simulation shows the adaptive controller is robust to the variation of fuel cell system dynamics.

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