THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Yuanchun Gu

DESIGN AND SIMULATION OF HYBRID THERMAL ENERGY STORAGE CONTROL FOR PHOTOVOLTAIC FUEL CELL

ABSTRACT
Objective: Through the design and simulation of hybrid thermal energy storage control of photovoltaic fuel cell, the hybrid thermal energy storage system of photovoltaic fuel cell is further optimized. Method: Firstly, the mathematical model of photovoltaic power generation is established. Then voltage feedback, power feedback, disturbance observation method and conductance increment method are used to track the maximum power of the system. After that, the dynamic model of proton exchange membrane fuel cell is established, and the former maximum power point tracking control strategy is used to keep the voltage stable. Finally, simulation experiments are carried out to verify the effectiveness and superiority of the proposed control strategy and battery model. Results: The hydrogen pressure on the anode side of the fuel cell can be maintained at 0.3 MP at a fast speed. In the process of output, the voltage of fuel cell is much smaller than the polarization voltage of fuel cell. Its voltage decreases gradually from 14 seconds to 16 seconds. Once the illumination changes suddenly, the system can also accurately locate and track the maximum power point, and output the electric quantity. Conclusion: Based on the mathematical model of photovoltaic power generation and the dynamic model of proton exchange membrane fuel cell, the hybrid thermal energy storage system of photovoltaic fuel cell has great advantages. It can keep the voltage stable and track the maximum power of the system in time, which is of great significance for the follow-up research in photovoltaic power generation.
KEYWORDS
photovoltaic, fuel, thermal energy, MPPT, storage
PAPER SUBMITTED: 2019-11-28
PAPER REVISED: 2020-01-15
PAPER ACCEPTED: 2020-01-26
PUBLISHED ONLINE: 2020-03-15
DOI REFERENCE: https://doi.org/10.2298/TSCI191128117G
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 5, PAGES [3259 - 3267]
REFERENCES
  1. Aniruddha Uniyal, P.N., et al., Image processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P. India. Geocarto International, 31 (2016), 9, pp. 956-965.
  2. Madaci B, et al. Design and control of a stand-alone hybrid power system. International journal of hydrogen energy, 41(2016), 29, pp. 12485-12496.
  3. Shaofei Wu. Study and evaluation of clustering algorithm for solubility and thermodynamic data of glycerol derivatives, Thermal Science, 23(2019), 5, pp.2867-2875
  4. Surender S.R. Optimal power flow with renewable energy resources including storage. Electrical Engineering, 99 (2016), 2, pp. 1-11.
  5. Rumana R.A., et al., Simulating the thermal behavior in Lake Ontario using EFDC. Journal of Great Lakes Research, 42 (2016), 3, pp. 511-523.
  6. Das S, et al. Power flow control of PV-wind-battery hybrid renewable energy systems for standalone application. International Journal of Renewable Energy Research (IJRER), 8(2018), 8, pp. 36-43.
  7. Yanlei X., et al., A New Method of Computer Image Processing and Detection Based on AHP Analysis. Journal of Computational and Theoretical Nanoscience, 13 (2016), 7, pp. 4368-4372.
  8. Shaofei Wu,A Traffic Motion Object Extraction Algorithm,International Journal of Bifurcation and Chaos, 25(2015),14,Article Number 1540039.
  9. Soltani, A., et al., A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis. Biomedical Signal Processing and Control, 40 (2018), pp. 366-377.
  10. Javed, A., et al., Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology. IEEE Transactions on Industrial Informatics, 13 (2016), 1, pp. 1-1.
  11. Anna S. The Influence of Solar Power Plants on Microclimatic Conditions and the Biotic Community in Chilean Desert Environments. Environmental Management, 60 (2017), 2, pp. 1-13.
  12. Healy, E., et al., Can a trained deep neural network be implemented into hearing technology. The Journal of the Acoustical Society of America, 141 (2017), 5, pp. 3813.
PDF VERSION [DOWNLOAD]
Design and simulation of hybrid thermal energy storage control for photovoltaic fuel cell

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence