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Day-Ahead Multi-Objective Energy Optimization of a Smart Building in a Dynamic Pricing Scenario

  • M. BotticelliEmail author
  • G. Comodi
  • A. Monteriù
  • A. Pallante
  • S. Pizzuti
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)

Abstract

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.

Keywords

Optimization Smart building Genetic algorithm Dynamic pricing Multi-objective Thermal comfort Energy efficiency Economic saving 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • M. Botticelli
    • 1
    Email author
  • G. Comodi
    • 1
  • A. Monteriù
    • 1
  • A. Pallante
    • 2
  • S. Pizzuti
    • 3
  1. 1.Marche Polytechnic UniversityAnconaItaly
  2. 2.Roma Tre UniversityRomeItaly
  3. 3.ENEA – Energy, New Technologies and Sustainable Economic Development AgencyRomeItaly

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