International Scientific Journal

Thermal Science - Online First

online first only

Optimization and sensitivity analyses of a combined cooling, heat and power system for a residential building

In the quest for a better use of energy resources, energy integration and cogeneration strategies have been employed in the industrial and commercial sectors with considerable benefits realized. However, the residential sector remains underexplored. An optimization procedure should be carried out whenever there is a need to ensure or verify the economic viability of an energy system. This study uses Mixed Integer Linear Programming to optimize the energy supply to a residential building, with 20 floors and 40 apartments, located in the city of João Pessoa (Northeast Brazil). The equipment available includes gas engines, electric and natural gas boilers, heat exchangers, cooling towers, and absorption and mechanical chillers. The optimization establishes the optimal system configuration and operational strategy (operation throughout the year). Economic, technical, and legal aspects were considered in the minimization of the total annual costs associated with the building's energy supply. The energy demands were calculated on an hourly basis, throughout one year, by the EnergyPlus software and corresponded to hot water (83 MWh/year), electricity (171 MWh/year) and cooling (242 MWh/year) demands. The optimal system was entirely reliant on the electric grid to meet the electricity demand directly and to satisfy heating and cooling demands by means of an electric hot water boiler and a mechanical chiller. The optimal solution is tested by varying, within reasonable limits, selected parameters: natural gas and electricity tariffs, the behavior of residents, amortization factor and relationship between the tariffs of electricity and natural gas.
PAPER REVISED: 2020-10-28
PAPER ACCEPTED: 2020-11-13
  1. Lozano, M. A., Carvalho, M., Serra, L. M. Operational strategy and marginal costs in simple trigeneration systems. Energy, 34 (2009), 11, pp. 2001-2008.
  2. EPE, Use of air-conditioning in the Brazilian residential sector: perspectived and contributions to energy efficiency advances, 2018.
  3. EPE, Brazilian Energy Balance, 2019. Access 09 apr 2020
  4. International Energy Agency - IEA. "Tracking Buildings: Not on track." 2020a. Available at: Access 11 feb 2020.
  5. Carvalho, M., Delgado, D., Chacartegui, R. Life Cycle Analysis as a Decision Criterion for the Implementation of Solar Photovoltaic Panels in as Northeast Brazil Hospital, Energy, Transp. Glob. Warm. (2016) pp. 295-314.
  6. Delgado, D., Carvalho, M., Coelho Junior, L. M., Abrahão, R. Chacartegui, R. Photovoltaic solar energy in the economic optimisation of energy supply and conversion, IET Renew. Power Gener., 12, (2018), 11, pp. 1263-1268.
  7. Carvalho, B., Melo, C., Freire A., Khanmohammadi, S., Carvalho, M. Multicriteria Optimization Of Renewable-Based Polygeneration System For Tertiary Sector Buildings, Environ. Eng. Manag. J., 18, (2019) 11, pp. 2441-2453.
  8. Brahman, F., Honarmand, M., Jadid, S. Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system Energy Build.(2015) pp. 65-75.
  9. Esther, B. P., Kumar, K. S. A survey on residential Demand Side Management architecture, approaches, optimization models and methods, Renewable and Sustainable Energy Reviews, (2016) pp. 342-351.
  10. Lauinger, D., Caliandro, P. Van herle J. Kuhn, D. A linear programming approach to the optimization of residential energy systems, J. Energy Storage, 7, (2016) pp. 24-37.
  11. Iturriaga, E., Aldasoro, U., Campos-Celador, A., Sala, J. M. A general model for the optimization of energy supply systems of buildings, Energy, 138, (2017) pp. 954-966.
  12. Szypowski, M., Siewierski, T., Wedzik, A. Optimization of Energy-Supply Structure in Residential Premises Using Mixed-Integer Linear Programming, IEEE Trans. Ind. Electron, 66, (2019), 2, pp. 1368-1378.
  13. Abbasi, M. H., Sayyaadi, H., Tahmasbzadebaie, M. A methodology to obtain the foremost type and optimal size of the prime mover of a CCHP system for a large-scale residential application, Appl. Therm. Eng., 135, (2018) pp. 389-405.
  14. Zheng, X., Wu, G., Qiu, Y., Zhan, X., Shah, N., Li, N., Zao, Y., A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China, Appl. Energy (2018) pp. 1126-1140.
  15. U. S. Department of Energy; Building Technologies Office, 2017. EnergyPlus. Version 8.7.0. Accessed 15 May 2020.
  16. Lindo Systems. 2017. Lingo. Version 16.0. Accessed 15 May 2020.
  17. Roriz, M. 2012. "Climate archives of Brazilian Municipalities". Available at: Accessed 15 May 2020.
  18. ABNT, "NBR. 15220-2: Desempenho Técnico das Edificações - Parte 2," 2005. Parte 2, v. 2.
  19. Yeomans, H. Grossmann, d I. E., A systematic modeling framework of superstructure optimization in process synthesis, Comput. Chem. Eng., 23, (1999) 6, pp. 709-731.
  20. Brazilian Electricity Regulatory Agency. 2010. Resolution nº 414, of September 9, 2010. Establishes the general conditions for the supply of electricity in na updated and consolidated manner. Accessed 20 May 2016.
  21. Energisa, "Conventional Low Voltage Rate Modality," 2017. Accessed: 15 May 2020.
  22. PBGAS, "Tarifas - Companhia Paraibana de Gás," 2017. Accessed: 15 May 2020.
  23. Brazilian Electricity Regulatory Agency, "Resolution no 482, of April 17, (2012). Establishes the general conditions for access of distributed micro and mini-generation and creates an electricity compensation system," 2012.
  24. Brazilian Electricity Regulatory Agency, "Resolution no 687, of November 24, (2015). Alter resolution no 482," 2015.
  25. Carvalho, M., Lozano, M. A. Ramos, J., Serra, L. M., Synthesis of trigeneration systems: Sensitivity analyses and resilience, Sci. World J., (2013).
  26. Yoshida, S., Ito, K., Yokoyama, R., Sensitivity analysis in structure optimization of energy supply systems for a hospital, Energy Convers. Manag., 48, (2007), 11, pp. 2836-2843, .
  27. Ren, H., Gao, W., A MILP model for integrated plan and evaluation of distributed energy systems, Appl. Energy, 87, (2010), 3, pp. 1001-1014.
  28. Pina, E. A., Lozano, M. A., Ramos, J. C., Serra, L. M.,Tackling thermal integration in the synthesis of polygeneration systems for buildings. Applied Energy, 269, (2020), 115115.