Identification of the objective function for optimization of a seasonal thermal energy storage system

Open access

Abstract

The article shows the proposed solution of the objective function for the seasonal thermal energy storage system. In order to develop this function the technological and economic assumptions were used. In order to select the optimal system configuration mathematical models of the main elements of the system were built. Using these models, and based on the selected design point, the simulation of the entire system for randomly generated outside temperatures was made. The proposed methodology and obtained relationships can be readily used for control purposes, constituting model predicted control (MPC).

[1] Budzianowski W.: Modelling of CO2 content in the atmosphere until 2300: Influence of energy intensity of gross domestic product and carbon intensity of energy. Int. J. Global Warm. 5(2013), 1, 1-17.

[2] Bartela L. and Kotowicz J.: Analysis of operation of the gas turbine in a poligeneration combined cycle. Arch. Thermodyn. 34(2013), 4, 137-159.

[3] Guerra C., Lanzini A., Leone P., Santarelli M., and D. Beretta: Experimental study of dry reforming of biogas in a tubular anode-supported solid oxide fuel cell. Int. J. Hydrogen Energy 38(2013), 25, 10559-10566.

[4] Kupecki J., Jewulski J., and Badyda K.: Comparative study of biogas and dme fed micro-chp system with solid oxide fuel cell. Appl. Mech. Mater. 267( 2013), 53-56.

[5] Sánchez D., Monje B., Chacartegui R., and Campanari S.: Potential of molten carbonate fuel cells to enhance the performance of chp plants in sewage treatment facilities. Int. J. Hydrogen Energ. 38(2013), 1, 394-405.

[6] Bakalis D. and Stamatis A.: Incorporating available micro gas turbines and fuel cell: Matching considerations and performance evaluation. Appl. Energ. 103(2013), 607-617.

[7] Chacartegui R., Monje B., Sánchez D., Becerra J., and Campanari S.: Molten carbonate fuel cell: Towards negative emissions in wastewater treatment chp plants. Int. J. Greenhouse Gas Contr. 19(2013), 453-461.

[8] Jannelli E., Minutillo M., and Perna A.: Analyzing microcogeneration systems based on lt-pemfc and ht-pemfc by energy balances. Appl. Energ. 108(2013), 82-91.

[9] McLarty D., Brouwer J., and Samuelsen S.: Hybrid fuel cell gas turbine system design and optimization. J. Fuel Cell Sci. Technol. 10(2013), 4, 04105-1-11.

[10] Qian J., Tao Z., Xiao J., Jiang G., and Liu W.: Performance improvement of ceria-based solid oxide fuel cells with yttria-stabilized zirconia as an electronic blocking layer by pulsed laser deposition. Int. J. Hydrogen Energy 38(2013), 5, 2407-2412.

[11] Sieniutycz S. and Jezowski J.: Energy Optimization in Process Systems and Fuel Cells, Elsevier, 2013. Identification of the objective function for optimization. . . 81

[12] Wang S.-B., Wu C.-F., LiuS.-F., and Yuan P.: Performance optimization and selection of operating parameters for a solid oxide fuel cell stack. J. Fuel Cell Sci. Technol. 10(2013), 5, 051005-1-11.

[13] Wang W., Li H., and Wang X.-F.: Analyses of part-load control modes and their performance of a sofc/mgt hybrid power system. Dalian Ligong Daxue Xuebao/J. Dalian Univ. Technol. 53(2013), 5, 653-658.

[14] Amirinejad M., Tavajohi-Hasankiadeh N., Madaeni S., Navarra M., Rafiee E., and Scrosati B.: Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast nafion membranes. Int. J. Energ. Res. 37(2013), 4, 347-357.

[15] Hajimolana S., Tonekabonimoghadam S., Hussain M., Chakrabarti M., Jayakumar N., and Hashim M.: Thermal stress management of a solid oxide fuel cell using neural network predictive control. Energy 62(2013), 320-329.

[16] Marra D., Sorrentino M., Pianese C., and Iwanschitz B.: A neural network estimator of solid oxide fuel cell performance for on-field diagnostics and prognostics applications. J. Power Sources 241(2013), 320-329.

[17] Ramandi M., Dincer I., and Berg P.: A transient analysis of three-dimensional heat and mass transfer in a molten carbonate fuel cell at start-up. I. J. Hydrogen Energ. 39(2014), 15, 8034-8047.

[18] Stempien J., Sun Q., and Chan S.: Performance of power generation extension system based on solidoxide electrolyzer cells under various design conditions. Energy 55(2013), 647-657.

[19] Grondin D., Deseure J., Ozil P., Chabriat J.-P., Grondin-Perez B., and Brisse A.: Solid oxide electrolysis cell 3d simulation using artificial neural network for cathodic process description. Chem. Eng. Res. Des. 91(2013), 1, 134-140,.

[20] Wee J.-H.: Carbon dioxide emission reduction using molten carbonate fuel cell systems. Renew. Sust. Energy Rev. 32(2014), 178-191.

[21] Zamaniyan A., Joda F., Behroozsarand A., and Ebrahimi H.: Application of artificial neural networks (ann) for modeling of industrial hydrogen plant. Int. J. Hydrogen Energ. 38(2013), 15, 6289-6297.

[22] PGNiG S.A., http://www.pgnig.pl/.

[23] RWE Polska S.A., http://www.rwe.pl.

[24] Hewalex Sp. z o.o. Sp.k., http://www.hewalex.pl/.

[25] Fan J., Chen Z., Furbo S., Perers B., and Karlsson B.: Efficiency and lifetime of solar collectors for solar heating plants. In: Proc. ISES Solar World Congress 2009, 331-340, Johannesburg 2009.

[26] Journal of Laws of the Republic of Poland (Dziennik Ustaw, Dz.U.) No. 75, item 690 of 12 Apr. 2002, as amended (in Polish).

Archives of Thermodynamics

The Journal of Committee on Thermodynamics and Combustion of Polish Academy of Sciences

Journal Information


CiteScore 2016: 0.54

SCImago Journal Rank (SJR) 2016: 0.319
Source Normalized Impact per Paper (SNIP) 2016: 0.598

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 76 76 22
PDF Downloads 28 28 10