An Integrated Building Energy Management System

  • Carlos Henggeler AntunesEmail author
  • Ana Soares
  • Álvaro Gomes
Conference paper


Environmental concerns and the need to reduce the dependency on imported fossil fuels have fostered several policy and economic mechanisms to incentivize the deployment of renewable generation plants, namely based on wind and photovoltaics, including microgeneration at the residential level. However, these sources are inherently intermittent, and consequently actions should be taken to mitigate the potential undesirable impacts that a large share of renewable generation may have on supply reliability and power quality. Moreover, the promotion of electric mobility requires the consideration of a new significant load, and electric vehicles would be expected to impose further challenges on power systems, in both grid-to-vehicle and vehicle-to-grid modes. Additionally, storage systems suitable for residential use are being announced. Therefore, a paradigm change is emerging in power systems involving a shift from a supply-follows-demand to a load-follows-supply strategy, making the most of the evolution toward smart grids.

Residential demand may play a key role in this transition because of the flexibility that these consumers generally have in the operation of their loads, and this may also positively affect electricity bills, i.e., shifting in time the operation cycle of some loads and modifying the (e.g., temperature) settings of other loads is easily accommodated by residential consumption patterns without compromising the quality of the energy services provided. The adequate control and coordination of residential demand should take into account operational aspects such as the integrated monitoring of electricity consumption at the household level, the evolution of dynamic tariff schemes with energy prices varying in short periods of time possibly with significant differences, and the characteristics of multiple energy resources (manageable loads, microgeneration, storage systems).

However, the continuous monitoring of demand and load control is too demanding for residential end users because of the diversity of decisions to be made (e.g., scheduling cycling loads, thermostat settings) and their time availability to implement management actions. Therefore, the deployment of automated energy management systems (EMSs) is essential to optimizing the integrated management of energy resources. These EMSs should be able to design optimal energy decisions to reduce electricity bills without impacting the quality of the energy services provided (e.g., room temperature below/above a prespecified comfort threshold, completion of the washing machine cycle before a given time, electric vehicle battery in a given state of charge by a required deadline). These decisions are strongly influenced by energy costs, end-user preferences and requirements, potential dissatisfaction of end user when the operation cycle of loads is changed, technical constraints, weather forecasts, and the existence of local microgeneration and storage.

This chapter presents an evolutionary algorithm to optimize the integrated use of residential energy resources and provides an analysis of simulation results under different scenarios. Two objective functions are considered to assess the merit of solutions: minimizing the electricity bill and minimizing the dissatisfaction felt by the end user resulting from the control actions. Results show that significant savings can be achieved, though they will depend on end users’ willingness to accept a certain degree of automated control, the characteristics of managed loads, the pricing structure, and end-user preferences.



This work was partially supported by the projects SusCity: Urban data driven models for creative and resourceful urban transitions (MITP-TB/CS/0026/2013), UID/MULTI/00308/2013, and Grant SFRH/BD/ 241 88127/2012.


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© Springer International Publishing Switzerland 2017

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Authors and Affiliations

  • Carlos Henggeler Antunes
    • 1
    • 2
    Email author
  • Ana Soares
    • 1
    • 2
  • Álvaro Gomes
    • 1
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.INESC CoimbraDEEC - University of CoimbraCoimbraPortugal

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