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The increasing reliance on electric vehicle (EV) charging in buildings requires balancing the load from other building systems to support the new demand. This paper uses a study case in a Near-Zero Energy Building (NZEB) educational facility located in the Brazilian Amazon to verify how much the energy efficiency (EE) measures would improve the existing ratings of the building and supply the expansion of EV demand. A comprehensive building energy load and energy performance analysis were conducted in four steps, following the mandatory Brazilian requirements for EE in public buildings, using measured data, computer modeling, and thermoenergetic analyses using OpenStudio version 1.1.0 and EnergyPlus software version 9.4.0. First, the EE retrofit measures were proposed and evaluated, targeting the air conditioning and lighting systems. Subsequently, an equation was elaborated to indicate the maximum level of energy consumption that could be increased without compromising the building's energy performance and NZEB classification. Finally, Open DSS software version 10.0.0.2 was used to simulate the increased availability of EV charging after the retrofit. With the proposed retrofit, the building improved the EE ratings by three levels, and the percentage of the NZEB rating increased by 33.28%. These measures also increased the EV charging load by 20%, maintaining the maximum EE level and the NZEB classification, although EV maximization reduced self-sufficiency by 9.78% compared to the retrofit-only scenario. [ABSTRACT FROM AUTHOR]

The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, greenhouse gas emissions and energy efficiency levels of lighting and air conditioning system, according to the mandatory technical regulation in public buildings in Brazil. Presenting a solution to assist in the decision making of engineers, architects or building managers for the optimal arrangements' choice for lighting and air conditioning equipment, considering each built environment and project profile. For the validation process, a basic building was created with 15 rooms spread over three floors, according to the most common construction parameters in the North of Brazil. First, different combinations of objective-function candidates were investigated to compose the multi-objective algorithm fitness function, analyzing its performance in two central scenarios: (1) adding some "baits" in air conditioning equipment files, and (2) without this inclusion. Thus, it was found that considering only three objective functions—incremental cost, energy consumption and the air conditioning energy efficiency coefficient—it is possible to get optimal non-dominated solutions in both scenarios, thus highlighting the robustness of the proposed methodology. [ABSTRACT FROM AUTHOR]