Day-Ahead Multi-Objective Energy Optimization of a Smart Building in a Dynamic Pricing Scenario
The identification of techniques aimed at a rational use of electric power has nowadays become more important than the production of energy itself. One of the causes for this is the progressive saturation of the Italian electricity grid, which is increasingly subject to connection requests, mainly due to the development of plants which make use of renewable energy sources.
In order to reduce the building’s energy costs during the summer season taking into account the user comfort, in this work we propose a new approach based on Pareto multi-objective optimization combined with a simulator developed in the MATLAB/Simulink environment. The electrical consumption of the entire building is taken into consideration with the aim of air-conditioning it. The goal is to find, the day before, the optimal hourly scheduling of the set points which have to be applied the next day, taking into consideration all external conditions, namely the weather conditions and the hourly energy price. To achieve this objective, the control variables we change are the room temperature set points and the flow water temperature set point. As required by the UNI EN ISO 7730:2006 standard (http://store.uni.com/catalogo/index.php/uni-en-iso-7730-2006.html), comfort measurement has been calculated with the PPD (Predicted Percentage of Dissatisfied) index.
Different scenarios have been investigated. The results show that there is an average of 15% potential cost saving, while maintaining a high level of comfort. Experimentation has been carried out by simulating a real office building in Italy, and the comparisons are shown regarding the actual settings applied to it.
KeywordsOptimization Smart building Genetic algorithm Dynamic pricing Multi-objective Thermal comfort Energy efficiency Economic saving
- 1.ENEA. (2003). Risparmi energetico nelle case. Rome: Author. Retrieved from http://efficienzaenergetica.acs.enea.it/doc/risparmio_casa_agg.pdf.Google Scholar
- 2.Isolani, P. (2008). L’uso razionale dell’energia negli edifici pubblici. Retrieved from https://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwi1y_L0rvDXAhWEbRQKHV4CCqMQFggrMAA&url=http%3A%2F%2Fwww.ri.camcom.it%2Fdownload%2F775.html&usg=AOvVaw0LNQCreHWzF9Lb0RoqZJbA.
- 3.Italian Energy Strategy Plan. Retrieved from http://www.sviluppoeconomico.gov.it/index.php/en/news/2037432-national-energy-strategy.
- 4.European Union. (2003). Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings. Official Journal of the European Union. L1/65.Google Scholar
- 5.European Union. (2010). Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast). Official Journal of the European Union. L153/13.Google Scholar
- 6.Hyvarinen, J., & Karki, S. (1996). Building optimization and fault diagnosis source book, IEA Annex. Espoo: VTT Technical Research Centre of Finland.Google Scholar
- 7.Bovesecchi, G. Comfort termo-igrometrico: Equazione del comfort, PPD e PVM. Rome: Università di r Roma Tor Vergata. Retrieved from http://didattica.uniroma2.it/assets/uploads/corsi/144485/04_-_TT1_-_Comfort_Termoigrometrico_-_Fanger_PPD_e_PMV1.pdf.
- 8.Fanger, P. O. (1970). Thermal comfort: Analysis and applications in environmental engineering. New York, NY: McGraw-Hill.Google Scholar
- 9.Normativa UNI EN ISO 7730:2006. (2006). Ergonomics of the thermal environment-Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria.Google Scholar
- 10.Camponeschi, M., Fonti, A., Leccese, F., Comodi, G., Grossoni, M., & Pizzuti, S. (2015). Winter thermal multi-objective optimization: A simulation case study. International Journal of Engineering Science and Innovative Technology (IJESIT), 4, 1. Retrieved from http://hdl.handle.net/11590/283981.CrossRefGoogle Scholar
- 11.Comodi, G., Fonti, A., Giantomassi, A., Polonara, F., & Longhi, S. (2013). Sviluppo di un simulatore di edifici orientato alla gestione attiva della domanda. Rome: ENEA.Google Scholar
- 13.UNI EN ISO 7730:2006 standard. Retrieved from http://store.uni.com/catalogo/index.php/uni-en-iso-7730-2006.html.