Online Energy Management Strategy Based On Adaptive Model Predictive Control For Microgrid With Hydrogen Storage

Jun MA, Mustafa Baysal

Abstract


Energy management optimization is still a big challenge for Microgrid, which includes Renewable Energy and Hydrogen Fuel Cell. In this paper, an online Adaptive Model Predictive Control (AMPC) based Energy Management Strategy (EMS) is proposed to increase the energy sources lifetime of the microgrid while minimizing hydrogen consumption. The EMS problem is simplified according to the control structure of microgrid system and the cost function of system is built and transferred to standard format of the Quadratic Programing problem to solve. Moreover, adaptive algorithm is used to automatically adjust the weights of different targets according to the states of system. It is shown by simulation results for different cases from MATLAB-SimulinkTM that proposed EMS has significant effects on controlling State-of-Charge (SoC) of battery and a noticeable improvement on extension of Hydrogen Fuel Cell lifetime and battery charge-sustainability

Keywords


solar energy;Hydrogen & Fuel Cells;Adaptive Model Predictive Control;Microgrid; Online Energy Management Strategy

Full Text:

PDF

References


F. Garcia-Torres C. Bordons, "Optimal economical schedule of hydrogen-based microgrids with hybrid storage using model predictive control", IEEE Trans. Ind. Electron. vol. 62 no. 8 pp. 5195-5207, August 2015.

L. Baumann, E. Boggasch, M. Rylatt, A. Wright, "Energy flow management of a hybrid renewable energy system with hydrogen", Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES), USA, pp. 78-85, September 2010.

C. Dötsch, Energy storage in Technology Guide. Berlin, Germany: Springer-Verlag, 2009, pp. 362–367.

L. Valverde, C. Bordons, and F. Rosa, “Power management using model predictive control in a hydrogen-based microgrid,†38th Annual Conference on IEEE Industrial Electronics Society, QC, Canada, pp. 5669–5676, December 2012,

N. Yang, D. Paire, F. Gao, A. Miraoui, "Power management strategies for microgrid-A short review", IEEE Industry Applications Society Annual Meeting, pp. 1-9, 2013.

G.C., Liao, “Solve environmental economic dispatch of smart microgrid containing distributed generation system—Using chaotic quantum genetic algorithm,†International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 779–787, December 2012.

A. Takeuchi, T. Hayashi, Y. Nozaki, and T. Shimakage, “Optimal scheduling using metaheuristics for energy networks,†IEEE Trans. Smart Grid, Japan, vol. 3, no. 2, pp. 968–974, June 2012.

28. A. Parisio, E. Rikos, L. Glielmo, "A model predictive control approach to microgrid operation optimization", IEEE Transactions on Control Systems Technology, vol. 22 no. 5 pp. 1813-1827, September 2014.

A. Del Real, A. Arce, and C. Bordons, “Hybrid model predictive control of a two-generator power plant integrating photovoltaic panels and fuel cell†, 46th IEEE Conference on Decision and Control, USA, pp. 5447–5452, December 2007.

J. Patino, A. Marquez, J. Espinosa, "An economic MPC approach for a microgrid energy management system", Transmission & Distribution Conference and Exposition - Latin America (PES T&D-LA), Colombia, September 2014.

K. Agbossou, M. Kolhe, J. Hamelin, T.K. Bose, "Performance of a stand-alone renewable energy system based on energy storage as hydrogen", IEEE Transactions on Energy Conversion, vol.19, no.3, pp. 633- 640, September 2004.

Zhang Tian, Huang Wei, Chen Hu, Pei Huiming, Li Pengfei, Ji Shuangquan, “The power generation of photovoltaic systems based on full-time analog integral calculationâ€, Renewable Energy Resources, China, 32(8), pp.1092-1098, September 2014.

C. Dépature, S. Jemeï, L. Boulon, A. Bouscayrol, N. Marx, S. Morando, A. Castaings, "IEEE VTS Motor Vehicles Challenge 2017 - Energy Management of a Fuel Cell/Battery Vehicle," IEEE Vehicle Power and Propulsion Conference (VPPC), China, pp. 1-6, October 2016.

M. Uzunoglu, O. C. Onar, M. S. Alam, "Modeling control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications", Renewable Energy, ELSEVIER, Vol. 34, No. 3, pp. 509-520, March 2009.

MJ Khan, MT Iqbal, “Dynamic modeling and simulation of a small wind-fuel cell hybrid energy systemâ€, Renewable Energy, ELSEVIER, DOI:10.1016/j.renene.2004.05.013, Vol.46, No.3, pp.421–439.

M Debert, G. Yhamaillard, G. A Ketfi-herifellicaud, “Predictive energy management for hybrid electric vehicles - prediction horizon and battery capacity sensitivityâ€, in IFAC Proceedings Volumes, ELSEVIER, vol. 43, no. 7, pp. 270-275, July 2010.

P. Falcone, F. Borrelli, H. E. Tseng, J. Asgari, and D. Hrovat, “Linear time-varying model predictive control and its application to active steering systems: Stability analysis and experimental validation,†International journal of robust and nonlinear control, DOI:10.1002/rnc.1245, vol. 18, no. 8, pp.862-875, May 2008.

Y GE, J Dai, K Qian, D Hepburn and C Zhou, “Simulation of Domestic Electricity Load Profile,†The 23rd International Conference and Exhibition on Electricity Distribution (CIRED), Lyon, France, pp.1-6, June 2015.

A. Hooshmand, B. Asghari, R. Sharma, "A novel cost-aware multi-objective energy management method for microgrids", IEEE Innovative Smart Grid Technologies (ISGT), USA, pp. 1-6, February 2013.

H. Borhan, A. Vahidi, A. M. Phillips, M. L. Kuang, I. V. Kolmanovsky and S. Di Cairano, "MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle," IEEE Transactions on Control Systems Technology, vol. 20, no. 3, pp. 593-603, May 2012.




DOI (PDF): https://doi.org/10.20508/ijrer.v8i2.7493.g7409

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4