Analysis of Energy Efficiency Measures in Integrating Light-Duty Electric Vehicles in NZEB Buildings: A Case Study in an Educational Facility in the Brazilian Amazon

Abstract

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.

1. Introduction

Energy efficiency has proven to be one of the most viable solutions in the search for energy transition, both from an energy, financial, and environmental point of view. The efficiency of the final uses of energy has shown a significant reduction in energy consumption, as occurs in buildings. According to the National Program for the Conservation of Electric Energy (PROCEL), when taking into account the guidelines of the regulations, standards, and normative instructions of energy efficiency, it is possible to achieve a reduction in consumption of 50% and 30% in new and existing buildings that undergo renovation, respectively [1].

From a financial point of view, the payback of energy efficiency actions is short due to the possibility of saving electricity, both in the initial phase of implementation of building systems and in the operation and maintenance (O&M) phase throughout their useful life. In Brazil, each state has its tariff, and the State of Pará is in second place in the ranking of the most expensive values in the country, generating a high amount; that is, investments in this region in energy efficiency quickly exceed the initial capital invested. In addition, the savings entail an accumulative cash flow over the useful life of equipment, with annual savings obtained after payback is achieved [2].

Concerning environmental issues, energy efficiency is perceived to have multiple benefits [3] in converting the amount of electricity that is no longer consumed, resulting in lower amounts of gas emissions such as carbon dioxide (CO₂), contributing to climate change mitigation [4].

To obtain these benefits of energy efficiency in buildings, the Brazilian Building Labeling Program (PBE Edifica), by Brazilian National Institute of Metrology, Standardization and Industrial Quality (INMETRO, acronym in Portuguese) Ordinance no. 309 (6 September 2022) [5], establishes the Inmetro Normative Instruction for the Energy Efficiency Classification of Commercial, Service and Public Buildings (INI-C), which evaluates lighting, air conditioning (cooling or heating), water heating, and building envelope systems. These systems entail significant costs arising from electricity consumption over the useful life of the buildings, which can reach 80 years in operation [6].

At the same time, using renewable energy also emerges as another way to minimize the need to expand the energy matrix. Furthermore, the transition to the system of “clean” sources has gained significant adhesion in buildings, being incorporated into the methodologies for analyzing the energy performance of buildings. In Brazil, the revision of the building labeling system included, in 2018, the classification of Near-Zero Energy Buildings (NZEB), in which at least 50% of the energy is produced on-site, and energy-positive buildings (EEP), which exceed the annual demand with on-site production [6].

Innovation and new technologies also make it possible for buildings to support the overall decarbonization of the economy and interact with other sectors, such as transportation. Since there is a greater uptake of electric vehicles (EVs) in the country, this brings challenges and opportunities for consumption, management, and efficiency.

According to the European Commission (2023), buildings are crucial in changing the mobility landscape. They provide the options and resources needed to supply the electric modes incorporated into their private building electrical infrastructure and the external charging stations on roads and in public places. In this sense, the European Parliament’s Alternative Fuels Infrastructure Regulation (AFIR) already foresees that around 60% of recharging will occur in private buildings [7].

1.1. Motivation/Research Questions

Electric mobility is part of the transition strategies that aim to increase the use of renewable energy sources and gradually replace fossil fuels. Electrifying the means of transport is a crucial strategy to reduce CO₂ emissions and other mobility-related pollutants.

Although Brazil is not one of the countries that use EVs the most, like China, Norway, South Korea, Sweden, Germany, and the United States, the growth in the use of passenger vehicles throughout the national territory is an increasingly present reality [8].

The driving factors include the vast consumer market for automobiles, strategic considerations of energy security, and environmental concerns. In addition, the high cost of fuel, the emphasis on corporate environmental and social practices (known as Environmental, Social, and Governance, ESG), and the national technological diversity are elements that solidify and drive this agenda in the country’s sector [9]. Figure 1 shows the annual expansion of the EV fleet in Brazil over the last five years, showing a growth of 89%.

The top five Brazilian states in terms of electric vehicles are as follows: São Paulo, with 72,562 units (32.66%); Rio de Janeiro, with 15,543 units (7%); Santa Catarina, with 14,959 (6.73%); Minas Gerais, with 14,586 (6.56%); and Paraná, with 13,673 (6.15%). Together, they account for 131,323 units, or about 59.10% of all vehicles in the country. The other states account for 90,875 units (40.89%), with the State of Pará accounting for 2515 units (1.26%) [10].

Despite this growth, the use of electric cars in Brazil is still slow compared to other countries, and the supply infrastructure is substantially restricted, with the possibility of improvements and technological innovation in the area. Regional discrepancies also play a role in the low penetration of EVs in the Brazilian market.

As a result, integrating electric car supply in buildings is a solution that impacts their performance, requiring more in-depth technical knowledge to guide energy consumption management decisions and energy efficiency actions, especially in those with low energy performance or with performance not yet measured, which disfavors sustainable integration from an energy point of view.

In addition, the distributed generation of renewable energy, especially solar photovoltaic generation being integrated into buildings, has promoted greater energy autonomy. Moreover, with the emergence of new classifications of energy-efficient buildings through the energy balance, where energy generation and consumption are compared, it is possible to establish the electricity grid’s energy autonomy level.

1.2. Related Works

According to the Mapping and Diagnosis of Electric Mobility initiatives in Brazil, from 2018 to 2021, the literature showed that studies focused on the impact on the electricity grid, energy generation and storage, fuel cells, electric vehicles, vehicle charging, charging stations, and the influence of all elements of electric mobility on the energy supply network [11].

Considering net-zero energy buildings, Keteng et al. evaluated the energy efficiency of hybrid photovoltaic–thermal systems in zero-energy buildings with a case study of an office building in Beijing. The authors developed a whole-process energy efficiency calculation (WPEEC) algorithm for analyzing the factors that influence the building’s energy efficiency and calculated the impact of these factors on the installation area of the hybrid systems and the proportion of energy supply and storage [12].

In [13], although the analysis was carried out using the RTQ-C labeling methodology, which has already been updated to the INI-C’s methodology, the paper shows the relevance of labeling for evaluating the existing systems of public buildings in Brazil. It was possible to estimate the efficiency of the lighting and air conditioning systems and obtain the total energy efficiency rating of this building, considering the contribution of the solar power plant.

Zheng et al. addressed retrofitting building systems such as lighting and air conditioning [14]. One of the methods chosen was the Monte Carlo simulation, which was used to predict the probabilistic distribution of the financial investment and the project’s energy savings. This enabled a more robust analysis of the uncertainties associated with the proposed EE measures. Even when applying this analysis, the study does not provide detailed information and data, such as the value or percentage of energy eco-savings achieved with the measures proposed to retrofit the buildings analyzed.

By proposing energy efficiency measures applied to lighting systems, Han et al. obtained savings of 1581 kWh per day using a system that combined natural and artificial light (LED lamps), which could result in annual savings of 174 kWh, considering favorable climatic conditions (clear, sunny days) [15]. Shankar et al. address using LED lighting integrated with photovoltaic modules in smart buildings, stating that implementing this strategy can help achieve NZEB classification in buildings. This article presents references for the consumption of artificial lighting systems in some typologies, such as academic buildings that operate from 8.30 a.m. to 5.30 p.m., where lighting consumption represents between 4 and 8% of the consumption of renewable energy generated in the building [16].

In the review article “Zero Energy Building Performance and Energy Efficiency Solutions” [17], Belussi et al. discuss the modernization of buildings with artificial lighting and air conditioning systems. They state that, in general, modernization efforts aim to improve energy performance while ensuring comfort and usability within buildings. Among the various EE actions raised, the authors mention the use of LED lamps, their integration coupled with natural lighting systems, and the use of control systems for artificial lighting to minimize electricity consumption while maximizing visual comfort, but do not give consumption reduction figures. Regarding air conditioning systems, in addition to using natural ventilation and system retrofitting, the article also mentions that implementing advanced control algorithms in air conditioning systems can lead to energy savings ranging from 17% to 38% compared to traditional constant air volume systems.

Bjelland, Brozovsky, and Hrynyszyn present various energy efficiency measures in retrofit projects [18]. Concerning air conditioning and artificial lighting systems, the study that analyzes 87 articles quantifies that approximately 63% of the studies dealt with the installation of new HVAC systems and that approximately 26% of the studies analyzed incorporated improvements in lighting, but there is no detailed data on the magnitude of the reduction in energy consumption resulting from these systems’ measures alone. However, it mentions that, in general, most of the retrofit projects analyzed achieved an average reduction in energy consumption of approximately 51.2%, with variations between 39.4% and 63.1% in a 95% confidence interval. This indicates that, in many cases, energy efficiency projects have been effective in significantly reducing energy consumption [18].

Analyzing energy efficiency measures via computer simulation with the DesignBuilder and EnergyPlus software, including the use of active air conditioning technologies and high-efficiency lighting to reduce energy consumption, Park et al. [19] obtained an 11.6% reduction in energy consumption with the implementation of LED lighting, reducing consumption from 75.8 kWh/m² to 67.0 kWh/m². As for the HVAC system, the improvement in the building’s airtightness, one of the measures implemented, resulted in a reduction in heating energy consumption. Energy consumption for heating was reduced by 45.2% to 33.5 kWh/m², but there was an increase in energy consumption for cooling, which rose from 29.3 kWh/m² to 34.0 kWh/m². Even so, the overall efficiency of the HVAC system was improved due to the reduction in the external thermal load, resulting in a more efficient use of energy. In that study, the total value of the building’s energy consumption reduction after implementing a comprehensive conservation package, which included installing LED lighting systems, high-efficiency HVAC systems, and other energy efficiency measures, was 41.4% [19].

Etemad et al. applied BIM technology as a resource for analyzing energy efficiency measures in HVAC systems using Revit and EnergyPlus software. The study indicated a reduction of 104,302 kWh in the electricity consumed annually by the building after the installation of the new cooling system with the implementation of the Variable Refrigerant Flow (VRF) system, representing a 50% reduction in the building’s cooling energy consumption compared to the previous system [20].

When analyzing the rational use of energy in sports centers, using computer simulations with TRNSYS (Transient System Simulation Tool) software [21], combined active and passive energy saving measures, as well as photovoltaic energy generation, resulting in a total reduction of 5,026,869 kWh in the building’s energy consumption, which represents a saving of 100.9% in the primary energy consumed, transforming the sports center into a zero energy ZEB facility. After implementing the energy efficiency measures, the analysis revealed a total annual primary energy consumption of 4,981,197 kWh, while primary energy production by the photovoltaic plant was 1,275,969 kWh. Among the EE measures was the replacement of inefficient lighting systems with LED technology, which provided eco-savings of more than 70% in energy consumption for lighting, totaling a reduction of 187.890 kWh.

In the systematic review of data-driven prediction and optimization to achieve energy-positive and net-zero buildings, Mousavi et al. reviewed a total of 177 studies, with 60% focused on energy efficiency, and suggests that the application of machine learning, IoT, and optimization techniques can lead to substantial improvements in energy efficiency. Although it does not quantify a specific total reduction, the study observed a 26% reduction in energy consumption in homes and 17% in commercial environments due to implementing control and optimization technologies, such as presence sensors and light intensity control in lighting systems. The study does not provide total energy consumption reduction figures for HVAC systems. Still, it highlights that optimization and intelligent control can significantly reduce energy consumption [22].

In the case study [23], analyzed 12 retrofit measures, including passive and active technologies. The active technologies include a high-efficiency heating and cooling system; high-efficiency light bulbs; photovoltaic system; and variable frequency fans or pumps. Using eQUEST-3.64 software, the study simulated the energy consumption of near-zero retrofit buildings compared to reference buildings. The results indicated that the retrofitted building could save 37.1% of energy compared to the reference building. Regarding the artificial lighting system, the study showed a reduced energy consumption of up to 72.67% compared to the reference building. The reduction in energy consumption for cooling during the summer months was 37.15%, and the energy generated by the photovoltaic system was 66.7 MWh.

In [24], the study’s objective was to evaluate how electric vehicle charging stations would affect a retail store in Centennial, Colorado, USA, exploring the station size, charging energy level, and modes of use in varying climate zones and seasons. The authors measured the impact of the annual electricity tariff with the changes generated in the building’s demand, using simulations conducted using EnergyPlus software and data from measurements taken on-site. They concluded that taking flexible loads into account, on-site solar PV deployment, energy storage, and controlled charging of electric vehicles can mitigate the high electricity demands and annual electricity bills caused by fast charging.

When analyzing electricity consumption in buildings due to the supply of electric vehicles, some studies have devised innovative strategies. Liao et al. investigated energy management that integrates photovoltaic systems, battery systems, and EV supply systems in four building communities comprising university, commercial, residential, and office buildings [25]. This work analyzed the interactive effects of EV charging loads on building energy consumption. To measure the total energy consumption of the communities, computer simulations were carried out using the EnergyPlus software to measure the energy consumption of the buildings and the energy generation of photovoltaic systems, and Python was used to simulate the charging model for electric vehicles (EVs) using the Monte Carlo method.

Research reveals that the EV charging load varies significantly between different building communities. Residential and commercial communities receive a large charging load, accounting for approximately 40% and 30% of the total load, respectively, while office and campus communities have a load of around 5–10% [25].

Along these lines was [26], which looked at the optimization of energy systems in buildings, with a focus on electric vehicle (EV) infrastructure and Zero Energy Building (ZEB) classifications. As a methodology, the authors used a machine learning model to predict the EV charging demand and used computer simulations with EnergyPlus software to estimate building energy demand. In this article, the EV charging demand is considered in the building’s electrical energy balance equation, so the EV charging demand is combined with the building’s overall electrical demand, measuring the impact on the total capacity required by the energy system. The EV demand represented 2.8% (222.88 kWh) of the total electricity demand in the office typology (7930.5 kWh) and 1.5% (14.36 kWh) in the residential typology (4800.00 kWh).

Another article that addresses vehicle charging in buildings is [27], which presents a PV-EV sizing framework for charging stations powered by local solar energy generation in commercial buildings. The framework considers load-matching performance and the self-consumption-sufficiency balance (SCSB), which reflects the balance between self-consumption (SC) and self-sufficiency (SS). Based on the results presented, the authors concluded that smart charging schemes can significantly improve the load-matching performance by up to 42.6 and 40.8 percentage points for SC and SS, respectively, in this type of building.

The study [28] suggests that integrating EVs with solar energy systems (PV) can significantly increase the rate of self-consumption and self-sufficiency of buildings. This implies that part of the energy consumption of EVs can be met by locally generated energy, reducing dependence on external sources. Although the study does not provide an exact percentage of EV energy consumption in terms of total building consumption, it does mention that the self-consumption rate can increase by up to 94% due to the large storage capacity of EV batteries. This suggests that a significant part of the energy consumed by EVs can come from PV systems installed in buildings.

In the article [29], an efficient EV charging coordination methodology is presented, where the system optimizes the charging of electric vehicles by shifting the charging load to periods of high renewable energy generation, which maximizes the use of locally generated energy. With this, coordinated control improves the daily self-consumption rates of renewable energy by up to 19% compared to conventional controls, and by coordinating EV charging with renewable energy generation, a reduction of up to 36% in daily electricity costs is achieved compared to scenarios without EV control.

Focusing on the optimal scheduling of energy systems, Zand et al. discuss the optimal scheduling of energy systems in buildings, mainly in the context of energy hubs (EHs) and integrated energy systems (IES). The article refers to studies that have proposed comprehensive models for EHs; these models are essential for understanding how to optimally schedule energy production and consumption to meet building load demands. These strategies ensure that energy systems can adapt to changing conditions while maintaining efficiency [30].

In [31], the operation of EVs is considered in simulations to assess their impact on energy management. The main focus of the study is on energy management in island group power systems, addressing aspects such as renewable energy generation, the use of electric vehicles (EVs) for energy transfer, and the optimization of energy management through an adaptive model. The simulations using an adaptive model, which aims to achieve optimal operation of the energy system, showed that it effectively improves the use of available energy resources, ensuring that energy demands are met more efficiently.

Addressing cooperative energy management for multi-energy systems, Li et al. propose an algorithm designed to improve collaboration between energy bodies. The algorithm is formulated as a distributed optimization problem, where each energy body can determine its optimal operations based on local information. It aims not only at day-ahead scheduling but also at managing real-time variations in energy loads and renewable resources [32].

In conclusion, the studies analyze various approaches to improving energy efficiency and integrating electric vehicles (EVs) into buildings, highlighting advanced technologies and intelligent energy management. The research shows that it is possible to achieve significant reductions in energy consumption and optimize EV charging infrastructure through control algorithms, computer simulations, and the integration of photovoltaic systems. However, there is a significant gap in the analysis of the impact of EV integration on maintaining the high level of performance of buildings’ energy efficiency ratings. This low amount of analysis prevents a complete understanding of how electric mobility affects the overall energy efficiency of sustainable and energy-efficient buildings.

1.3. Contribution

Within the context of all that has been exposed so far, the work aims to contribute to the following:

• Measuring the impacts that these energy-efficient measures have to support the insertion of new energy uses arising from systems and equipment that have emerged over the years with technological evolution or even the expansion of existing uses that are outdated;
• Establishing a limit indicator for the expansion of energy consumption of building systems to ensure good energy performance in buildings;
• Evaluating the possibility of increasing the integration of EVs, following the market trend, but without compromising the energy efficiency and self-sufficiency rating of the buildings;
• Determining a consumption management premise of the supply system of integrated EVs in buildings, promoting better management of energy resources while maintaining alignment with the guidelines of the building’s energy performance methodology.

1.4. Paper Organization

This article is organized into six sections. Section 2 presents the methodology proposed in each of the four steps of the study. Section 3 presents the case study’s presentation, where the building is described, as well as its consumption and electricity generation systems. In addition, this section also presents the building’s energy efficiency and self-sufficiency rating. The application of the steps described in the methodology is in Section 4. Then, in Section 5, a discussion is made about the results, presenting a comparative analysis of the synthesis graphs. Finally, the conclusions are made in Section 6, at the end of the article.

2. Materials and Methods

This section describes and applies the four general steps (Figure 2) of case study analysis and their respective methodologies to achieve the main objective proposed in the article. For this figure, it should also be explained that A and B represent levels A (dark green) and B (light green) of energy efficiency in a building, A being the most efficient level there is. Based on the building’s performance level and energy balances, a critical analysis is carried out, identifying the potential for efficiency of the evaluated systems. Step 1 proposes actions for improving the systems whenever there is a possibility of enhancing energy gains. In step 2, the building will be re-evaluated to ascertain the new classification of the efficiency level and energy self-sufficiency achieved with the proposed retrofit actions for the efficiency of the building.

After evaluating the existing and proposed case results, the potential for an increase in building electricity consumption will be analyzed in step 3 after retrofit. This analysis allows the building manager to have a parameter for the limit of increase in consumption that the building may suffer, resulting from the expansion of building systems and loads without harming the building’s energy performance.

In step 4, the increase in the charging of electric cars will be measured, which can be recharged within a pre-established limit. Remembering that this increase in loading, made possible by the reduction in electricity consumption resulting from the retrofit, will be proposed within the limits of the building’s infrastructure, meeting the premises for the proper functioning of the other building systems and, mainly, maintaining the building’s level A in energy performance.

2.1. Step 1: Retrofit of Building Systems

After verifying the systems’ inefficiency and adding EV chargers, proposals are made for improvements in the systems of buildings with an energy rating level below A or that, even though they are level A, have the potential for higher efficiency.

The efficiency measures in the lighting system are linked to the reorganization of lighting drives, replacement of less efficient lamps and luminaires for more efficient ones, and building automation, such as the adoption of presence sensors in low-stay environments, such as bathrooms and circulation, for the automatic shutdown of the system in the absence of users. For this study, the prerequisites will be considered fulfilled. The luminous flux of the luminaires was also observed so that the visual comfort is in line with the determinations of [33]. The selection of suitable lamps is based on the illuminance limit, glare limit, and color reproduction indicated by the same standard.

Also, INMETRO’s normative instruction includes adjustment factors for installed lighting power. These can be used to estimate the reduction in consumption caused by adopting building automation. Occupancy sensors with automatic shut-off will make lighting efficient, resulting in a 20% reduction in installed power in these environments.

For the refrigeration system, it is proposed to change air conditioners if the A level is not reached, considering the current parameters of the INI-C [6]. This eligibility condition for the A classification varies from system to system, depending, for example, on the technology to be adopted. For split systems, the condition is a minimum IDRS (Seasonal Cooling Performance Index) of 5.50, according to INI-C [6]. Different prerequisites need to be met for this technology. For these, it is also necessary to observe the minimum thickness of the thermal insulation of the pipes; however, for this study, these prerequisites will be considered fulfilled.

2.2. Step 2: Building’s Energy Performance to Retrofit

Step two was carried out using the computer simulation methodology of INI-C [6]. This methodology is applied to any building, regardless of shape and size. This standard utilizes the bioclimatic zoning proposed by the Brazilian Association of Technical Standards (ABNT, acronym in Portuguese) [34,35], facilitating the replication of this methodology across the entire national territory. As an international alternative to this, other labeling systems can be used if the basis of the certification is an energy reduction analysis, as can be seen in Excellence in Design for Greater Efficiencies (EDGE) certification, which awards labels for energy savings, from 20% reduction to 100% reduction, depending on specific labeling standards [36].

The INI-C’s simulation methodology evaluates buildings that are fully air-conditioned, fully naturally ventilated, or hybrid (natural ventilation and air conditioning). From this method, the final consumption per use of the individual systems in electrical energy is obtained, where the following are estimated: the total annual thermal load reduction coefficient for the evaluation of the envelope (walls and roofs that separate interior spaces and the exterior of the building); the reduction in energy consumption of artificial lighting; and the reduction in annual refrigeration consumption [6]. The simulation data are used to calculate the primary energy consumption of the building under study, both in the real condition (C_EP,real) and the reference condition (C_EP,ref).

The consumption of the actual building is compared with that of the same building in a reference condition, which corresponds to classification D. This D classification corresponds to a building with no shading on outside windows, specific materials for exterior walls, roof, and glass, as well as a fixed window–wall ratio (PAFT, acronym in Portuguese) and Lighting Potency Density (DPI, acronym in Portuguese), depending on the building’s type, which for this case, will be a college-level educational building [6].

In addition, floor insulation should be considered nonexistent, and the Coefficient of Performance (COP) for Heating, Ventilation, and Air Conditioning (HVAC) systems should be 2.6 for this reference D building model. It is important to note that all variable acronyms will be used following the ordinance. Based on the systems’ final consumption, the level of energy efficiency classification of each one, and consequently of the building in general, is identified [6].

Item C.I.1 of the INI-C contains the requirements for the type of software to be used in the simulations. Following the regulations, the OpenStudio 1.1.0 software graphic interface was chosen for the 3D modeling of the building. The modeling process consists of creating virtual spaces that resemble the real ones. The EnergyPlus software (version 9.4.0) was chosen to carry out the calculations, which include geo-referenced thermo-energy simulation; characterization of equipment operation; the thermal properties of materials; quantification of internal occupancy loads and artificial lighting loads; and electrical equipment [37].

The modeling procedure consists of creating virtual spaces similar to the real thing. Each space within the building should be marked with a thermal zone where thermal exchanges occur. Each thermal zone is grouped, per typology, according to the similarity of operation, occupation, and presence of electrical equipment and materials. Within the OpenStudio software, the operating schedules are created and assigned to these space typologies identified in the building. Construction materials, with thickness, thermal conductivity, specific heat, density, and thermal absorption data, are also assigned to their specific spaces. These data are obtained through [34,35,38], and all schedules used in the model can be found in Appendix A,

Table A1. Information, variables, and data used in simulations of the CEAMAZON building.

Variables	Description	Values	Source
Architectural dimensions	Dimensions of walls, frames, ceiling, floors, and levels.	A block: Volume 2693.38 m3; Envelope area: 1382.98 m2; Shape coefficient: 0.51; PAFT (Total Facade Opening Area Percentage): 17.34%. B block: Volume 2176.93 m3; Envelope area: 990.86 m2; Shape coefficient: 0.46; PAFT: 13.53%. C block: Volume 450,24 m3; Envelope area; 272.7 m2; Shape coefficient: 0.61; PAFT 5.16%.	Site survey/INI-C
Building materials	Physical characterization of materials used on the internal and external surfaces of the blocks.	Roof with EPS slab, air layer ≥ 5 cm, and plasterboard ceiling. Roof with simple 10 cm concrete slab and asphalt membrane. Roof with thermoacoustic tiles, air layer ≥ 5 cm. Walls of 15, 16, 20, 25, and 32 cm, with internal and external mortar, ceramic bricks with 6 holes in various conditions. Ceramic floors on solid 10 cm concrete slab and on ribbed concrete slab with EPS Wood and aluminum fenestrations Simple clear 6 mm glass.	Site Survey/INI-C
Occupancy Schedules	Timings and days of the year for the occupancy of a space.	Operational days: 1 March–11 July (first semester); 24 August–12 December (second semester). Occupation schedule: 8–18 h for Libraries, Laboratories, Restrooms, Circulation, and Storage. 8–12 h–14–18 h: Auditorium; Professor’s rooms; Classrooms; Meeting rooms; Administration. 12–14 h: Canteen.	Site survey
Lighting Schedules	Timings and days of the year for the activation of lights of a space.	Operational days: 1 March–11 July (first semester); 24 August–12 December (second semester). Lighting schedule: 16–18 h for administration, meeting rooms, libraries, professor’s rooms and classrooms, laboratories, restrooms, storage, and circulation on block A; 16–22 h: for circulation on block B and the canteen 8–12 h–14–18 h: for the auditorium.
HVAC Schedules	Timings and days of the year for the activation of HVAC systems of a space.	Operational days: 1 March–11 July (first semester); 24 August–12 December(second semester). HVAC schedule: 8–18 h for laboratories and libraries; 8–12 h–12–18 h: for the auditorium, administration, classrooms, and professor’s rooms; Always on: Server’s rooms.
Quantity of occupants	Number of people in artificially conditioned environments.	287 people, distributed between 25 spaces, considering maximum occupation.	Site survey/INI-C
DPE (Equipment Power Density)	The electrical power of equipment is distributed per square meter of the built space.	15 w/m2	INI-C
DPI (Lighting Power Density)	Electrical power of artificial lighting per illuminated area of the block.	9.34 W/m2 (Real value); 15.5 W/m2 (Reference value)	Site survey/INI-C
Occupation density	Quantity of square meters per person in the building.	1.5 m2/person	INI-C
HVAC equipment	Models of cooling units for consultation in catalogs.	3516.85; 5275.28; 7033.71; 7033.71; 10,550.56; 16,998.12. (W) divided between 35 different HVAC machines.	Site survey
IDRS	Coeficiente de performance sazonal de resfriamento para máquinas de condicionamento de ar.	2.6 reference model value/variable between 2.51 and 3.40 in real model values	Site survey/INI-C
Temperature Setpoint	Target temperature for achieving thermal comfort in the space.	24 °C	INI-C
Tipology	Predominant activity in the block under analysis.	A.2. (Educational building of college-level)	INI-C
Bioclimatic zone	Brazilian climatic classification of different regions.	ZB8	INI-C

.

Under modeling guidelines from [6], nearby buildings are included in simplified form, and vegetation will not be included in this study. Finally, according to item C.I.2 from [6], the climate file must contain information that is representative of the climate of the city where the building is located. The normative instruction adopts the climate classification proposed by ABNT NBR 15220-3 [34] to verify the influence of climate. The climate archive used for the city of Belém is available at [39] on the PBE Edifica online platform [40]. The climate file under the name “BRA_PA_Belem.816800_INMET.epw” was chosen. The output results to be analyzed in this field are electrical consumption of equipment and artificial lighting, thermal transmittance, solar reflectance, and percentage of total façade opening.

After the computational model is elaborated on in OpenStudio, the information and data on the building are exported to EnergyPlus in .idf format. The first step is to configure the soil type of each block. The Ground Finite Difference model is chosen, as it can produce more reliable results at the expense of simulation time spent [41]. This calculation method uses a one-dimensional finite difference heat transfer model to ensure that the ground utilizes the weather file for the thermal zone simulations [42].

Secondly, the HVAC systems are modeled. EnergyPlus allows it to recreate different HVAC systems using templates through the HVAC Template object [42]. All systems in this model are split systems, which can be created virtually through the HVAC Template: System: Unitary and HVAC Template: Zone: Unitary systems. The main variables required to simulate these systems are the COP, the cooling capacity, in W, and a temperature setpoint, in °C, currently available in Table A1 of Appendix A. Then, the simulations are calibrated based on the annual cooling consumption, total annual thermal load, and total monthly consumption of the building.

The results obtained from the real model are compared with the values of a reference model, which is modeled based on level D parameters, present in Annex A of the normative instruction. According to the guidelines, real buildings with complex geometries must be divided into regularly shaped blocks. Each block must be modeled in separate files, with the other blocks kept as shading objects. Each space within the block must be marked with a thermal zone where heat transfer will occur. As observed in previous works, each enclosed space will receive a thermal zone. Outside, following modeling guidelines from [6], nearby buildings are included in simplified form, and vegetation will not be included in this study.

The simulation is carried out in the same shape and with the exact operating schedules, power density, occupancy density, and density of electrical equipment inserted in the real model. By comparison, it is possible to classify the block according to its bioclimatic zone (ZB) and shape coefficient (FF) for envelope and artificial lighting. The FF is the envelope area’s ratio to the block’s volume. For this study, the envelope areas and volume of all blocks will be added to perform this division. After the simulation, it is possible to obtain the classification of the energy efficiency level and the definition of the general ENCE through Equation (1) [6]:

RedC_EP = ((C_EP,refD − C_EP,real )/C_EP,_refD ) × 100

(1)

where RedC_EP is the reduction in primary energy consumption for blocks; C_{EP,refD A} is the primary energy consumption of the reference (level D) building condition for all the blocks (kWh/year); and C_EP,real is the primary energy consumption of the real building for all the blocks (kWh/year). These variables can be calculated following Equations (2)–(4) [6]:

C_EP,real = (C_EE,real × fC_E) + (C_ET,real × fC_T) − (G_EE × fC_E)

(2)

where C_EP is the primary energy consumption of the real building (C_EP,real) (kWh/year); C_EE is the total electrical energy consumption of the real building (C_EE,real) (kWh/year); fC_E is the conversion factor of electrical energy into the primary energy; C_ET is the total thermal energy consumption of the real building (C_ET,real) (kWh/year), if applicable; fC_T is the conversion factor of thermal energy into primary energy; and G_EE is the energy generated by local renewable energy sources (kWh/year). C_ET and G_EE are not used for this analysis phase.

C_EP,refD = (C_EE,_refD × fC_E)

(3)

where C_EP, is the primary energy consumption of the reference (level D) building condition (C_EP,_refD) (kWh/year) and C_EE is the total electrical energy consumption of the reference (level D) building condition (C_EE,refD).

C_EE,real or _refD = C_R,real or _refD + C_IL,real or _refD + C_AAE,real or _refD + C_EQ

(4)

where C_R is the cooling consumption of the real building’s HVAC (C_R,real) or in its reference (level D) condition (C_R,refD) (kWh/year); C_IL is the lighting consumption of the real building lighting system (C_IL,real) or in its reference (level D) condition (C_IL,refD) (kWh/year); C_AAE electrical energy consumption of the water heating system of the real building (C_AAE,real) or in its reference (level D) condition (C_AAE,refD) (kWh/year); and C_EQ is the equipment consumption (kWh/year). C_AAE,real or _refD is zero.

All these factors are obtained at the end of the computer simulation in the energy end-uses in the HTML file. The conversion factor of electrical energy into primary energy fC_E used is 1.6 [6]. Additionally, the real CgTT or _refD is obtained for envelope classification. Finally, consumption is classified within efficiency classes, ranging from E to A. The factors needed to obtain these classes are the bioclimatic zone, the form factor, and the range, which depends on the evaluated system. The methodology used to obtain the C_EP and CgTT can be observed in Equation (5):

i = (CRC_EPD-A and CRCgTT_D-A × 100)/3

(5)

where i is the calculated value representing the interval between classes, (%); CRC_EPD-A is the Primary Energy Consumption Reduction Coefficient from classification D to A; and CRCgTT_D-A is the total annual thermal load reduction coefficient for classifications from D to A [6]. The application of these variables can be seen in

Table 1. Educational buildings: coefficient for reducing primary energy consumption from classification D to A (CRC_EPD-A), based on the form factor and corresponding climate classification, according to INI-C.

Climatic Classification	CRCEPD-A
	FF ≤ 0.20	0.20 < FF ≤ 0.30	0.30 < FF ≤ 0.40	0.40 < FF ≤ 0.50
ZB 8	0.28	0.30	0.28	0.27

and

Table 2. Range Limits for general building energy efficiency ratings, according to INI-C.

RedCEPweighted (%)
Energy Efficiency Classification	A	B	C	D	E
	RedCEP > 3i	3i ≥ RedCEP > 2i	2i ≥ RedCEP > i	i ≥ RedCEP > 0	RedCEP < 0

, adapted from [6].

Only the bioclimatic zone is considered for the intervals for air conditioning systems. These intervals are shown in

Table 3. The limits of the energy efficiency rating range according to the climate rating of the building in which the air conditioning system is being evaluated, according to INI-C.

Climatic Classification	RedCR (%)
	A	B	C	D	E
ZB 7 and 8	RedCR > 43	43 ≥ RedCR > 29	29 ≥ RedCR > 14	14 ≥ RedCR ≥ 0	RedCR < 0

, adapted from [6].

According to Equation (6), the air conditioning system evaluation is based on the percentage reduction in cooling consumption (RedC_R), which is obtained by comparing the actual condition with the reference condition, C_R.

RedC_R = ((C_R,refD − C_R,real)/C_R,refD) ∙ 100

(6)

where RedC_R is the percentage reduction in cooling consumption (%), for the blocks; C_R,refD is the cooling consumption of the reference (level D) building condition for all blocks (kWh/year); and C_R,real is the cooling consumption of the real building for all block (kWh/year). The cooling consumption (C_R) value is obtained through computer simulation in real reference conditions.

Finally, for artificial lighting, the ranges depend on the lighting consumption for D and A ratings, as per Equation (7):

i ={[(C_IL,refD − C_IL,refA)/C_IL,refD] × 100}/3

(7)

After identifying all ranges except lighting consumption, the classification for all other systems can be obtained by comparing the reduction coefficients and classes, as shown in

Table 4. Range limits of energy efficiency ratings for lighting systems, according to INI-C.

RedCIL (%)
A	B	C	D	E
RedCIL > 3i	3i ≥ RedCIL > 2i	2i ≥ RedCIL > i	i ≥ RedCIL≥ 0%	RedCIL < 0%

.

The evaluation of the lighting system, which is based on the percentage reduction in lighting consumption (RedC_IL), is obtained by comparing the actual condition (C_IL,real) with the reference condition (C_IL,ref), according to Equation (8).

RedC_IL = ((C_IL,refD − C_IL,real)/C_IL,refD) ∙ 100

(8)

where RedC_IL is the percentage reduction in lighting consumption (%) for the blocks; C_IL,refD is the lighting consumption of the building in its reference (level D) building condition (kWh/year) for the blocks; and C_IL,real is the lighting consumption of the real building (kWh/year) for all blocks. The lighting consumption (C_IL) value is obtained through computer simulation in real conditions and as a reference.

The results obtained suggest improvements for each block that needs it.

The regulation follows the classification process by comparing electricity consumption and generation to achieve the NZEB goal. Due to the existence of a central photovoltaic plant, which did not belong to a single block but to the building as a whole, it was necessary to add and weigh the consumption and RedC_EP of the blocks to achieve a classification result and total final consumption for the building in a unified way.

The weighting was performed using the ratio between the RedC_EP product and the C_EP of each block and the sum of the C_EP of each block (A, B, and C), resulting in (RedC_EPweighted). It was also necessary to sum the envelope areas and volumes to obtain the unified FF. The following is Equation (9) [6] for weighting the primary energy consumption of the building:

RedC_EPweighted = (∑RedC_EPn × C_EPn)/∑C_EPn

(9)

where RedC_EPweighted is the weighted reduction in primary energy consumption (kWh/year), ∑RedCEPn is the sum of the reduction in primary energy consumption of a block n (kWh/year), and ∑C_EPn is the sum of the primary energy consumption of a block n (kWh/year).

After obtaining the value of the reduction in primary energy consumption weighted (RedC_EPweighted), the final energy efficiency rating is obtained from Table 2. This value is compared with the calculated ranges to identify the final general energy efficiency rating.

Finally, with the weighted value, the building’s energy balance is carried out, where it is possible to evaluate the contribution of local renewable energy generation systems generated on-site. According to Annex D of INI-C, the level of this contribution can classify the building as near zero energy (NZEB) or Positive Energy Building (EEP, acronym in Portuguese) through the potential for energy generation (PG) by the use of local renewable energy sources, which is obtained using Equation (10):

PG = (G_EE × fC_E × 100)/(C_EE,real × fC_E)

(10)

where PG is the energy generation potential (%); G_EE is the energy generated by local renewable energy sources (kWh/year); fC_E is the conversion factor of electrical energy into primary energy, which will be considered 1.6, sourced from INI-C [6]; and C_EE,real is the total electrical energy consumption of the consumer unit (CU) in real condition (kWh/year).

If the PG was more significant than or equal to 50% but less than or equal to 100%, the building could be considered NZEB. It can be classified as a PEB if it is higher than 100%.

2.3. Step 3: Electrical Energy Consumption Indicator for Energy Efficiency Level A

In the third and final stage, the methodology is established to estimate the maximum electricity consumption value in kWh/year so that the building is not penalized in its energy efficiency classification. The following mathematical Deduction (11) defines the maximum amount of consumption of a vehicle for this purpose:

L_A,inf ≤ (∑RedC_EPn × C_EPn)/∑C_EPn + (EC × fC_E)

(EC × fC_E) ≤ [(∑RedC_EPn × C_EPn) − L_A,inf × ∑C_EPn]/L_A,inf

(11)

EC ≤ [(∑RedC_EPn × EC_Pn)/L_A,inf × fC_E] − (∑C_EPn/fC_E)

where L_A,inf is the lower limit of classification A for primary energy and EC is the annual electricity consumption in kWh/year.

This formulation was chosen by uniting the consumption of electric vehicles and the final primary energy rating equation. The normative instruction [6] dictates that electrical equipment needs to be included in the consumption of the blocks; however, as the blocks are part of a single building, this is not feasible.

EV consumption then needs to be entered into the building as a whole, which can be accomplished in the weighting step by adding this factor as a sum to the denominator of (9). This way, introducing external loads into the building can be simplified and replicated in other cases. The electricity consumption (EC) also needs to be in the same variable class as the others, so an EV consumption needs to be multiplied by the electrical energy conversion factor fC_E, which can now be considered as the primary energy consumption of the EV.

The equation is then solved to find the maximum allowable value of EC. The dismemberment of the primary VE energy by the EC was chosen because it is a simpler variable to understand. It is easier to understand the variable annual electric consumption than an electric vehicle’s annual primary energy consumption, which also facilitates the application of this formulation in other cases.

2.4. Step 4: Increasing the Supply of Electric Vehicles

This step begins with applying statistical methods and stochastic simulations to EV charging. Monte Carlo simulations, a reliable tool, are highly relevant for addressing challenges in electric vehicle (EV) charging, especially in scenarios characterized by substantial uncertainty and operational constraints. This analysis applies statistical models to predict fluctuations in EV charging demands. Using the Poisson distribution, the frequency of EV arrivals and their state of charge (SOC) at charging stations are estimated. These assessments are conducted at ten-minute intervals during the OpenDSS simulation.

Random numbers generated from a standard distribution dictate the count of EVs arriving for charging, while another set of random numbers derived from a normal distribution determines their initial SOC. This simulation is active only from 8 AM to 4 PM, when vehicles arrive at the building’s parking facility. Employing the Monte Carlo method to simulate the charging process helps elucidate the impact of EV integration on power usage and the efficiency rating of the building.

In the foundations of mathematical modeling in EV charging, λ was defined as the mean EV arrival rate per 10-min interval. The Poisson distribution provides the probability of observing EVs arriving in 10 min. The arrival and initial battery charge levels of the EVs are modeled using a normal distribution, expressed as (12).

X = µ + σZ

(12)

Here, Z represents a standard normal variable, making X a normally distributed random variable with mean µ and standard deviation σ. For this model, an average arrival rate of 1 EV with a standard deviation of 1 is used, and the battery charge at arrival is assumed to have a mean of 20 with a standard deviation of 5. Note that the simulation exclusively uses integer values, requiring the conversion of X to its absolute value before proceeding.

The current energy management system of the electric vehicle charging system considers some consumption restriction assumptions, such as power limit and charging percentage when the supply level reaches 80% of the EV battery.

After determining the maximum energy consumption, allowing to avoid the declassification of the building’s level A, established by the methodology of step 3, it is possible to use it as a new condition for EV charging. To this end, class A’s maximum consumption limit value is divided by 365 days to have a daily reference of available energy, which is not used by the building’s loads, but which could be used to recharge EVs. This way, if on a given day there are EV recharges that exceed 197.26 kWh, on the next day, the algorithm will reduce the charging power to try to compensate for the excess consumption.

The algorithm does this by calculating the rate of variation; the greater the consumption exceeded, the greater the rate of variation. Consequently, more power will have to be reduced in the next recharges so that at the end of the year, the total energy consumed by the EVs is within the maximum limit, according to (13).

∆E = E1 − E0

(13)

where ∆E is the energy variation (kWh/day); E1 is the energy accumulated EV consumption by electric vehicles (kWh/day); and E0 is the daily reference energy for electric vehicles (kWh/day), according to (14).

E0 = 197.26 × number of accumulated days

(14)

With this change, in addition to managing recharging from the reduction in the recharging power and the interruption in charging, another condition was created, which evaluates the rate of change in the daily consumption used versus the available daily energy based on the annual energy allowed for the recharging of EVs. When the rate of variation is positive, the algorithm must act because the energy consumption consumed by EVs exceeds the planned consumption. The aggressiveness rate is the ratio between the amount of energy exceeded and consumed, according to (15).

Tx = 1 − (W − 197.26 × n/W)

(15)

where Tx is the reduction rate, applied on the next day (derivative); W is the energy consumed by electric vehicles (kWh/year); and 197.26 is the daily energy value in kWh.

The aggressiveness rate is the ratio between the amount of energy exceeded and the amount of energy consumed. The higher this value, the more power needs to be reduced in the following charge to reduce EVs’ energy consumption. The algorithm developed to simulate EVs’ electricity consumption, made in the OpenDSS software version 10.0.0.2, considers the value per day.

Therefore, if this value is exceeded, management will consider the overrun difference to reduce the power the next day so that the power is reduced/adjusted to not exceed the annual value. Thus, the derivative conditions the management algorithm’s aggressiveness in reducing power to higher percentages to meet the mentioned condition, according to (13)–(15). This entire process can be summarized with the following flowchart shown in Figure 3.

Therefore, by applying this condition, it is possible to increase the supply of electric vehicles in the building without this increase affecting the energy performance of the evaluated building.

3. Case Study

The building studied is the Center of Excellence in Energy Efficiency of the Amazon (CEAMAZON), located in the Guamá Science and Technology Park, Perimetral Avenue 2651, latitude −1.46667 and longitude −48.44575, building 01, Guamá neighborhood, city of Belém, state of Pará, Brazil. As it is located in the Amazon region, the building belongs to bioclimatic zone 8, according to NBR 152020-3 [34]. This standard stipulates several climatic strategies so that designers (architects, engineers) can develop projects with construction and architectural strategies that provide thermal, visual, and acoustic comfort to the users, according to the climatic conditions of each region of Brazil where the facilities are inserted.

CEAMAZON is a 3-story building with 2395.43 m² of total built area and 1697.3 m² of floor area. The building comprises 3 blocks (Figure A1 in Appendix A), where the block A has three floors, with a total area of 564.48 m² and an “L” shape. Block B also has three floors, a total area of 951.37 m², and a rectangular floor plan. Block C has a fan shape and a total area of 181.45 m². Blocks 2 and 3, although separated by a connecting block (walkway), are organized according to the “L” typology, whose orientation favors cross ventilation and natural lighting.

The research center is open from Monday to Friday, from 8 a.m. to 6 p.m. The building comprises seventy-five people: eight professors, seven administrative technicians, and approximately sixty undergraduates, master’s, and doctoral students who work with research in the seven laboratories that make up the center.

The building has energy meters installed, which allow the definition of load curves of building consumption in real time, 24 h a day. In 2022, according to the database of the Electricity Consumption Management System (SISGEE) platform prepared by the CEAMAZON-UFPA team, CEAMAZON had a total annual electricity consumption of 74,776.15 kWh/year (Figure 4).

As shown in Figure 4, the building’s average monthly consumption is 6218.71 kWh, and the months with the highest energy consumption are May, June, and September.

3.1. Building Systems

The envelope systems of the three blocks that belong to CEAMAZON are similar. There are non-thermally insulated floors on all floors and brick walls with internal and external mortar measuring 15, 16, 20, 25, and 32 cm, with the 15 cm wall being the most common in the building. The slabs between floors comprise solid 10 cm slabs with plaster lining and ribbed slabs with EPS and plaster lining, usually with a 1.5 cm ceramic floor on a subfloor. Block C, as it is an auditorium, has a carpeted floor on a 10 cm solid concrete slab and a vermiculite ceiling under a sandwich metal roof with a 4 cm polyisocyanide (PIR) filling. A list of materials, agendas, and other relevant information about the wrap can be found in Table A1 of Appendix A.

The building’s lighting system consists of 276 lamps, totaling 16,612.00 W of installed power in an illuminated area of 1709.29 m². The lamps range from 18, 20, and 40 W, with LED and fluorescent technologies. The luminous flux of these lamps also varies between 900.00, 1067.00, 1100.00, and 2350.00 lumens (lm). All lamps will be considered fully functional for this study. These data characterize the building as having a DPI of 9.72 W/m², used in the simulations as the actual value.

The air conditioning system comprises 35 split technology devices, with power ranging from 3516.85, 5, 275.28, 6447.56, 7033.71, 10,550.56, and 16,998.12, totaling 262,591.68 W of installed power. The refrigeration simulation setpoint used was 24 °C.

3.2. Photovoltaic System

At CEAMAZON, renewable energy is generated by a photovoltaic system installed on the building site. The system consists of 234 modules of 335 Wp, totaling 80 kW. In 2022, the reference for this study, the system obtained a total annual renewable energy generation of 115,854 kW.

After analyzing the graph in Figure 5, the building’s photovoltaic generation has a monthly average of 9654.47 kWh. When checking the system’s seasonality, it is possible to verify that the months with the highest energy generation are June, July, August, and September.

3.3. Electric Vehicle Charging System

The electric vehicle charging infrastructure comprises four (04) 7.6 kVA slow-charge chargers and three (03) 22 kVA semi-fast chargers. Some premises were defined for managing the system, such as the power and voltage limit of the building’s transformer.

The management strategy aims to optimize the charging of electric vehicles without violating the restrictions described above.

3.4. Current Energy Efficiency and Self-Sufficiency Rating Level

The CEAMAZON building is a federal public building, which must be evaluated by the INMETRO Normative Instruction for the Energy Efficiency Classification of Commercial, Service, and Public Buildings (INI-C) if it is to undergo renovation, as stated in MPOG/SLTI Normative Instruction No. 02 of 4 June 2014, published in the Official Gazette of the Union (DOU) [43].

The building has a complex architectural part, where the architecture of its envelope presents varied volumetry and constructive elements of solar shading that vary in each façade, composed of interconnected blocks (Figure A1, Appendix A). Because of these characteristics, the thermo-energetic simulation methodology was chosen to evaluate the level of efficiency of the building. This methodology evaluated the envelope, the lighting system, and the air conditioning of the building under study. The results of each evaluated system are presented below. All values of the variables generated by the computer simulation for each block, in its real and reference condition with EV integration, are presented in

Table A2. The current energy consumption of the CEAMAZON building per block, for the real and reference cases with EV integration, in kWh.

Block	CEE,refD	CEE,refD	CR,real	CR,refD	CIL,real	CIL,refD	CEQ,real	CEQ,refD	CEP,real	CEP,refD
A	113,438.69	126,317.46	91,322.08	101,454.94	4163.44	6909.35	17,953.17	17,953.17	181,501.904	202,107.94
B	21,015.11	26,847.53	13,563.58	15,508.22	5894.96	9782.75	1556.57	1556.57	33,624.18	42,956.06
C	6114.08	6776.66	5591.18	6126.02	205.03	332.77	317.87	317.87	9782.53	10,842.65
EV	56,927.00	56,927.00							91,083.21	91,083.21
Total	197,494.88	216,868.65	110,476.84	123,089.18	10,263.43	17,024.87	19,827.61	19,827.61	315,991.82	346,989.86

in Appendix A. These individual data were used in the weightings to obtain the general values of the complete building.

• Energy Efficiency Rating of the building envelope

The building has 2646.54 m² of envelope area and a total volume of 5320.55 m³, thus obtaining a shape factor (FF) of 0.50. Analyzing the INI-C [6], for bioclimatic zone 8, the shape factor 0.50 corresponds to a Primary Energy Consumption Reduction Coefficient from classification D to classification A (CRC_EPD-A) of 0.27. The classification interval (i) equal to 3.33 was obtained from this value, as shown in (5). This figure (i) resulted in

Table 5. Limits of ranges of energy efficiency classifications of the existing envelope in the CEAMAZON building, adapted from INI-C.

RedCgTT (%)
A	B	C	D	E
RedCgTT > 10%	10% ≥ RedCgTT > 6.67%	6.67% ≥ RedCgTT > 3.33%	3.33% ≥ RedCgTT ≥ 0%	RedCgTT < 0%

.

The computer simulation resulted in a total annual thermal load of the real building (CgTT_,real) and reference building (CgTT_,ref), in the amount of 271,459.51 kWh/year and 320,031.86 Wh/year, respectively. By these values, it was possible to assess that the envelope has a reduction in the total annual weighted thermal load of 15.18%, which, in comparison with Table 5, corresponds to level A of the current energy efficiency class of the envelope; therefore, the envelope of CEAMAZON does not require changes.

2.

Energy Efficiency Rating of Current Air Conditioning System

INI-C presents some tables for the air conditioning system, with the range limits of the energy efficiency ratings for each bioclimatic zone according to the corresponding climate classification. The boundaries of bioclimatic zone 8, where the building under study is located, are shown in

Table 6. Limits of ranges of energy efficiency classifications of the existing air conditioning in the CEAMAZON building, bioclimatic zones 7 and 8, adapted from INI-C.

Climate Classification	RedCR (%)
Bioclimatic Zones	A	B	C	D	E
7 and 8	RedCR > 42%	42% ≥ RedCR > 28%	28% ≥ RedCR >14%	14% ≥ RedCR ≥ 0%	RedCR < 0%

.

The results obtained from the computer simulation of the building indicate that the value of refrigeration consumption of the air conditioning system in its real condition (C_R,real) is 110,476.84 kWh/year, while in its reference condition (C_R,ref), it had a value of 123,089.18. Both final values were obtained by weighting the individual cooling consumption of each block (A, B, and C).

From the values of refrigeration consumption in the real and reference conditions, the percentage reduction in energy consumption of the refrigeration system (RedC_R) was 10.25%. Comparing it in Table 6, we have the energy efficiency class D for the building’s current air conditioning system.

3.

Energy efficiency rating of current lighting system

The lighting system analyzed by the computer simulation meter resulted in the limit lighting power values for classification A and D (PI_LD and PI_LA), at 21,417.72 W and 38,157.98 W, respectively. In addition, there is a value of 23,026.97 W of the total installed lighting power (PI_T) in its actual condition.

Through these values, and with the values of the building’s hours of use per day and the number of days of occupation per year, according to the typology of the tables in Annex A of the INI-C, the consumption values of the building’s lighting system in its reference condition D (C_IL,refD) and reference equivalent to grade A (C_IL,refA) were obtained, resulting in 17,024.87 kWh/year and 9555.95 kWh/year, respectively. From these values, the Equation (7) generated a value of 14.62% for the interval between the classes. With this interval, the EE rating limits of the lighting system were assembled, as shown in

Table 7. Limits of the ranges of the energy efficiency ratings of the existing lighting system in the CEAMAZON building, adapted from INI-C [6].

RedCIL (%)
A	B	C	D	E
RedCIL > 43.87%	43.87% ≥ RedCIL > 29.25%	29.25% ≥ RedCIL >14.62%	14.62% ≥ RedCIL ≥ 0%	RedCIL < 0%

.

The simulation also resulted in the consumption value of the lighting system of the actual building (C_IL,real) of 10,263.43 kWh/year. By applying this value to (8), there is a 39.72% reduction in the consumption of the lighting system of the real building (RedC_IL) compared to Table 7. Then, the energy efficiency class level B is obtained for the current lighting system of CEAMAZON.

4.

Classification of the general energy efficiency of the CEAMAZON building with EV integration

The building has 2646.54 m² of envelope area and a total volume of 5320.55 m³, thus obtaining a shape factor (FF) of 0.50. When analyzing the INI-C for bioclimatic zone 8, the shape factor 0.50 corresponds to a Primary Energy Consumption Reduction Coefficient from classification D to classification A (CRC_EPD-A) of 0.27. The classification interval (i) equal to 9 was obtained from this value, according to (5). This figure (i) resulted in

Table 8. Limits of the ranges of the classification of the general energy efficiency of the CEAMAZON building.

RedCEPweighted (%)
A	B	C	D	E
RedCEP > 27%	27% ≥ RedCEP > 18%	18% ≥ RedCEP > 9%	9% ≥ RedCEP ≥ 0%	RedCEP < 0%

.

In the computational simulation process of EnergyPlus, it was possible to measure the total electricity consumption of all three blocks of CEAMAZON, which resulted in 140,567.88 kW/h per year, considering the consumption values of the lighting system, air conditioning, and other equipment present in the building. In addition to these systems, there is a total of 56,927.00 kWh/year of consumption resulting from the electric vehicle charging system, simulated by OpenDSS, which results in a total final efficiency value of 197,494.88 kWh/year.

This value was multiplied by 1.6, which was the conversion factor of electrical energy (fC_E) into primary energy given by the INI-C, resulting in 315,991.82 kWh/year of the real building’s primary energy consumption. Doing these same calculations for the reference building, we have the value of 346,989.86 kWh/year, the reference building’s primary energy consumption. When applying the weighting according to (9), the value of an 8.47% reduction in the building’s primary energy consumption is obtained. With this percentage, the CEAMAZON building has a level D rating for the building’s overall energy performance.

5.

Classification of Near-Zero Energy Building (NZEB) or Positive Energy Building (EEP, acronym in Portuguese) of the CEAMAZON building with EV integration

With the total electricity consumption in the current real condition at 197,494.88 kWh/year, the conversion factor of electrical energy into primary energy of 1.6, and the value of energy generated by local sources of renewable energy of the center at 116,065.70 kWh, it was possible to obtain the power generation potential (PG) of 58,77% using (10). For this value, being between 50% and 100%, the evaluation of energy self-sufficiency classifies CEAMAZON as a Near-Zero Energy Building (NZEB) in its current state.

4. Results and Analysis

4.1. Results of Step 1

The CEAMAZON building’s envelope presented level A in energy performance, as presented in item 1 of Section 3.4, so the intervention proposals focused on the air conditioning and lighting systems, which presented energy efficiency levels D and B, respectively, as can be seen in

Table A4. Percentage of reduction in energy consumption and energy efficiency rating of existing building systems in each block of the CEAMAZON building.

	Primary Energy		Envelope		HVAC		Lighting
Block	RedCEP (%)	CEP Label	RedCgTT (%)	CgTT Label	RedCR (%)	CR Label	RedCIL (%)	CIL Label
A	10.20	C	12.97	A	9.99	D	39.74	B
B	21.72	B	22.28	A	12.54	D	39.74	B
C	9.78	C	33.82	A	8.73	D	38.39	B
Weighted *	11.90	C	15.18	A	10.25	D	14.62	B

* Weighted with EV integration before the increase in supply consumption.

of Appendix A. The electric car supply system has remained the same for this step.

1.

Retrofit proposal for the air conditioning system of the CEAMAZON building

For the air conditioning system, a resizing was proposed based on the thermal load calculation procedures of [44]. The machines chosen for replacement are listed in

Table 9. HVAC models of the retrofit proposal for the air conditioning system of the CEAMAZON building.

Recommendations
Qtd	Potency (W)	Efficiency Class (IDRS)
4	2637.64	7.60
15	5275.28	8.25
2	7033.71	8.20
1	8792.13	7.60
2	9378.27	7.5
7	10,550.56	5.87

.

In all, 35 split-type machines, which had an installed power value of 262,591.67 W, were replaced by 31 machines, also of the split-type, now accounting for a value of 205,149.74 W of installed power. This exchange corresponded to a decrease of 57.441,93 W in the building. A reduction of 21.87% was obtained in the building air conditioning system from the current to the proposed case.

2.

Retrofit proposal for the artificial lighting system of the CEAMAZON building

For the lighting system, the energy efficiency actions proposed for the retrofit consisted of replacing less efficient lamps with more efficient ones, according to PROCEL’s performance classifications, and meeting the lighting comfort guidelines stipulated by the Brazilian standard [33], where the number of lumens was defined for each new luminaire. All new additions can be seen in

Table 10. The quantity and energy potency of lamps for the retrofit proposal of the lighting system of the CEAMAZON building.

Recommendations
Qty.	Potency(W)	Total Potency (W)
230	9	5.580
230	11	2.530
12	14	168

.

In total, 850 lamps, which had a total installed power value of 16,612 W, were replaced by more efficient ones. This exchange corresponded to a global decrease of 8334 W in the building, which resulted in a total value of 8278 W of installed power in the new system. The lamp replacement reduced the installed power in the building lighting system by 50.17%, directly affecting the building’s final consumption.

4.2. Results of Step 2

The proposed energy efficiency measures for the air conditioning and lighting systems were presented in the previous stage. With the results obtained in step 1, the computational methodology for energy performance evaluation is applied in step 2, as presented in Section 2 of the methodology, Section 2.2, to analyze whether the actions proposed for the two systems guarantee the achievement of level A.

After re-simulating and re-evaluating the building’s performance with the measures proposed for the air conditioning and lighting system, new classifications were obtained, as described below.

• Classification of the energy efficiency of the proposed air conditioning system to the retrofit of the CEAMAZON build

With the case proposed for the air conditioning system of the CEAMAZON build, new values of cooling consumption in its real condition (C_R,real) for each block were possible to obtain. A was left with 39,348.90 kW/h, B with 4874.82 kW/h, and C with 2457.86 kW/h, totaling a cooling consumption in its final real condition (C_{R real}) of 46,681.58 kW/h. With this new real C_R value, together with 123,089.18 kW/h of the reference consumption (C_R,refD), and applying (6), the new value of the percentage of reduction in energy consumption of the refrigeration system (RedC_R) is reached, in the amount of 62.07%.

According to Table 3, the reduction needs to exceed 42% to obtain level A, so the air conditioning system’s efficiency measures were sufficient to raise the current level of D to level A in the energy efficiency of the proposed air conditioning system, exceeding the limit established by the INI-C by approximately 20%.

2.

Classification of the energy efficiency of the proposed lighting system to the retrofit of the CEAMAZON build

The efficiency measures proposed for retrofitting the lighting system resulted in new values of lighting consumption in its real condition (C_IL,real), totaling 8314.78 kW/h. This value is the sum of the values of each block, where A had 3374.44 kW/h, B had 4777.82 kW/h, and C had 162.52 kW/h.

With this new C_IL,real value, together with 17,024.87 kW/h of the reference consumption (C_IL,refD), and applying (8), the new value of the proposed energy consumption reduction percentage of the lighting system (RedC_IL) is 51.16%.

According to Table 4, the reduction needs to exceed 43.87% to obtain level A, so the air conditioning system’s efficiency measures were sufficient to raise the proposed lighting system’s current level of D to level A in the energy efficiency, exceeding the limit established by the INI-C by 7.29%.

3.

Classification of the general energy efficiency to the retrofit of the CEAMAZON building with EV integration

If the modernization of building systems is proposed and implemented, the general classification must consider the new consumption values of air conditioning and lighting systems. However, the same values of the current case are maintained for the envelope (area, volume, and shape factor), which results in the same value of the interval between the classes. Thus, the same Table 8, with the limits of the intervals of the general energy efficiency classifications of the building, is maintained.

Finally, the same consumption values of the electric car supply system are also used, as they were not the focus of the proposed measures’ interventions due to the INI-C’s methodology, which does not yet provide guidelines for the individual evaluation of this system.

The new electricity consumption values give us the real primary energy consumption C_EP,real of each block: 97,082.42 kWh/year (block A), 17,934.74 kWh/year (block B), and 4701.20 kWh/year (block C). Adding these values to the C_EP,real of the supply of electric cars (91,083.21 kWh/year), we find the C_EP, the total real of the building in the proposed case, in the amount of 210,801.56 kWh/year of primary energy, as can be seen in

Table A3. The energy consumption of the proposed retrofit of the CEAMAZON building per block, for the real and reference cases with EV increased integration, in kWh.

Block	CEE,Retrofit	CEE,refD	CR,Retrofit	CR,refD	CIL, Retrofit	CIL,refD	CEQ,Retrofit	CEQ,refD	CEP,Retrofit	CEP,refD
A	60,676.51	126,317.46	39,348.90	101,454.94	3374.44	6909.35	17,953.17	17,953.17	97,082.42	202,107.94
B	11,209.21	26,847.53	4874.82	15,508.22	4777.82	9782.75	1556.57	1556.57	17,934.74	42,956.06
C	2938.25	6776.66	2457.86	6126.02	162.52	332.77	317.87	317.87	4701.20	10,842.65
EV *	56,927.00	56,927.00							91,083.21	91,083.21
Total 1	131,750.97	216,868.65	46,681.58	123,089.18	8314.78	17,024.87	19,827.61	19,827.61	210,801.57	346,989.86
EV **	71,224.04	71,224.04							113,958.46	113,958.46
Total 2	146,048.01	231,165.69	46,681.58	123,089.18	8314.78	17,024.87	19,827.61	19,827.61	233,676.82	369,865.11

* EV integration before the increase in supply consumption. ** EV integration after the increase in supply consumption.

of Appendix A, and 131,750.97 kWh/year of electrical energy.

Using the reference primary energy consumption values (C_EP,ref), we have new RedC_EP values for each block: 51.97% (block A); 58.25% (block B); 56.64 (block C); and 0% of the electric charging system. It can be the same in the real case and reference. Again, the RedC_EP of each block was weighted through (9), resulting in a final RedC_EP of the proposed case of 30.15%. All values of the variables generated by the computer simulation to the retrofit for each individual block, in its real and reference condition with EV increased integration, are presented under

Table A5. Percentage of reduction in energy consumption and energy efficiency classification of the new building systems proposed in the retrofit of each block of the CEAMAZON building, with EV integration before and after the increase in supply consumption.

	Primary Energy		Envelope		HVAC		Lighting
Block	RedCEP (%)	CEP Label	RedCgTT (%)	CgTT Label	RedCR (%)	CR Label	RedCIL (%)	CIL Label
A	51.97	A	12.97	A	61.22	A	51.16	A
B	58.25	A	22.28	A	68.57	A	51.16	A
C	56.64	A	33.82	A	59.88	A	51.16	A
Weighted *	30.15	A	15.18	A	62.07	A	51.16	A
Weighted **	27.20	A	15.18	A	62.07	A	51.16	A

* Weighted with EV integration before the increase in supply consumption. ** Weighted with EV integration after the increase in supply consumption.

of Appendix A.

According to Table 2, the reduction needs to exceed 27% to obtain level A, so the efficiency measures of the air conditioning and lighting system, together, raised the current level from D to level A of the classification of general energy efficiency in the case proposed for the retrofit of CEAMAZON.

4.

Classification of Near-Zero Energy Building (NZEB) or Positive Energy Building (EEP, acronym in Portuguese) to the retrofit of the CEAMAZON building with EV integration

Using the same conversion factor of electricity into primary energy of 1.6 and energy generated by local sources of renewable energy in the center at 116,065.70 kWh, which remains the same, and using the new value of total electricity consumption in the real condition 275,539.82 kWh/year resulting from the retrofit, we have the potential for energy generation (PG) of 88.09%, using (10). CEAMAZON has a Near-Zero Energy Building self-sufficiency (NZEB) rating in this scenario.

4.3. Results of Step 3

The result of step 2 showed that the energy efficiency actions resulted in level A in the building’s energy performance, even considering the energy consumption of the electric vehicle power supply system, and without having to apply any changes to this system. In addition, it was possible to achieve a percentage reduction in primary energy consumption with a margin of 3.15% compared to the upper limit of level A, which was 27%.

This clearance allows the energy consumption of building systems to be further expanded. As the lighting and air conditioning systems are redimensioned, meeting the highest level of design and meeting the parameters of thermal and light comfort, it was decided to analyze the expansion of the electric vehicle supply system. This choice is also based on the growing increase in electric vehicle fleets in the world market, especially the national and regional ones, as demonstrated in Section 1.

In this sense, it is relevant to analyze the increase in energy consumption in buildings, aiming to follow the growth of the EV market trend [10]. However, it is also necessary that this increase in consumption resulting from the increase in EV charging in the building is performed with prudence so that good energy performance, in addition to energy efficiency measures, is not impacted, resulting in a lower level. Therefore, the primary consumption value (C_EP) was used, and the product’s value of reducing primary energy consumption (RedC_EP) was used with the primary energy consumption of each block, as seen in

Table 11. RedC_EP and C_EP were used to calculate the primary energy consumption limit for the CEAMAZON building.

Blocks	RedCEP	CEP	∑RedCEPn × CEPn
A	51.97	97,082.42	5,045,373.37
B	58.25	17,934.74	1,044,698.61
C	56.64	4701.20	266,275.97
Total		119,718.36	6,356,347.94

.

The sum of primary consumption (C_EP) resulted in 119,718.36 kWh/year and 6,356,347.94 of the total value of the sum of products between (RedC_EP) and (C_EP), without VE integration. With these two values and the result of the weighting of the reduction in the building’s primary energy consumption of 27.01, purposely coinciding with the upper limit of the level A class, the Deduction (11) of the methodology proposed in step 3 is applied.

The calculation results in 115,614.78 kWh/year of primary energy consumption, the limit for classification A of primary energy (L_A,inf), corresponding to 72,247.76 kWh/year of electricity consumption, which is the maximum energy consumption value that building systems can achieve without causing the building’s energy performance class to decrease from level A. As previously explained, this value is used as a reference to generate a more significant increase in electric vehicle charging and will be applied in step 4 below.

4.4. Results of Step 4

The fourth and final stage presents the simulation results of the increase in electricity consumption in the building resulting from the electric car supply system, but within the limits stipulated by the previous step.

• Classification of the general energy efficiency to the retrofit of the CEAMAZON building, with increased integration of electric vehicles

The consumption limit for this system, set at 72,247.76 kWh/year, was divided by 365 days to determine the daily limit value, which was approximately 197.939 kWh/day.

As a result, the management system exercised more assertive consumption control. Through the OpenDSS software version 10.0.0.2, the new energy charging consumption value of 71,224.04 kWh/year was obtained (Table A3 of Appendix A), keeping the consumption below the maximum limit allowed to maintain the general energy performance class at level A for CEAMAZON building, as can be seen in Table A5 of Appendix A. This result confirmed that expansions in the EV charging system can be made cautiously, allowing EVs to be integrated following the energy efficiency precepts of the general classification of buildings.

2.

Classification of Near-Zero Energy Building (NZEB) or Positive Energy Building (EEP, acronym in Portuguese) to the retrofit of the CEAMAZON building, with increased integration of electric vehicles

Keeping the same values for and using the new value of total electricity consumption in the real condition 275,539.82 Wh/year, resulting from the retrofit, we have the energy generation potential (PG) of 79.47%, using (10). With this new percentage, which considers the increase in EV loading, it is concluded that the CEAMAZON that already is a (NZEB) maintains its self-sufficiency classification, as it still has a PG above 50%.

5. Discussion

Based on the results presented in the steps of Section 3, it is possible to examine how the building’s energy consumption significantly varied throughout the work, as seen in Figure 6, with the synthesis of the results.

Comparatively, scenario 1 (real) shows that the building had a high consumption of inefficient building systems. This inefficiency was proven with the application of the INI-C’s evaluation methodology, where it is possible to see the dashed boundary line represented in yellow in the graph in Figure 6. When scenario 1 (real) is compared with scenario 2 (proposed), it becomes clearer how much the building had the potential to make its air conditioning and lighting systems more efficient, pointed out as responsible for level D that the building had.

After the application of the retrofit measures to the energy consumption of the lighting and air conditioning systems, it should be noted that the consumption of the systems analyzed by INI-C came into equilibrium with the individual consumption of the electric car supply system, which remained the same until this point of the study. Lighting and air conditioning systems and other building loads (equipment) now represent 56.79% of total consumption, while the supply system represents 43.21%. Previously, this percentage was 71.18% and 28.82%, respectively.

Knowing the maximum limit of electricity consumption that the building could reach to maintain its highest level of performance, it was possible to foresee a greater charging range for the electric vehicles that supply the building. This change, applied to EV load management, generated scenario 3 (proposed maximum), where new consumption percentages, 51.23% for the building systems and 48.77% for the supply system, show an almost equal load balance.

It is pertinent to comment that this approximation in the equalization of consumption shows the greatness of the supply system to the detriment of the others when a single system is capable of generating consumption of almost 50% in a building compared to the rest of the loads coming from the lighting, air conditioning, and other equipment systems combined.

Figure 7 compares renewable energy generation potential (PG), highlighting the impacts of the actions proposed throughout the work on the performance of building self-sufficient electricity.

As seen in Figure 7, in all stages of the study, the CEAMAZON building had variations in the energy generation potential (PG) percentage, which indicates the energy self-sufficiency classification. In the initial stage, it was presented at 58.77%. This value rose to 88.09% after the efficiency actions and fell to 79.47% with the increase in electric car consumption.

It is noted that the building did not reach the positive energy classification after the retrofit. However, even with the supply system’s consumption changes, it remained with the NZEB classification.

6. Conclusions

The growth in the adoption of electric vehicles and, consequently, in the consumption of electricity in cities is a reality that requires attention. In the context of buildings, this attention is focused on the supply system of electric modes, such as cars, which become part of the building consumption, along with other systems and energy loads.

These “new” end-uses of energy start to be added to the existing ones, impacting the energy performance of buildings, especially in inefficient buildings. In this context, the present study evaluated the impact of the planned integration of EVs in buildings based on energy efficiency and sustainability guidelines based on nationally consolidated normative standards and instructions.

Energy efficiency measures were proposed and aligned with thermal and lighting comfort levels to achieve a good energy performance index and maintain the building’s self-sufficiency. The retrofit proposals for the CEAMAZON building focused on the air conditioning and lighting systems, which had energy efficiency levels D and B, respectively. The air conditioning system was resized, replacing 35 split machines with 31, reducing the installed power from 262,591.67896 BTU/h to 205,149.74700 BTU/h, a decrease of 57,441.93196 BTU/h, resulting in a reduction of 21.87%. The lighting system changed 850 light bulbs, reducing the installed power from 16,612 W to 8278 W, a reduction of 50.17%.

In stage 2, after re-simulation, the cooling consumption fell to 46,681.58 kWh and the lighting consumption fell to 8314.78 kWh, achieving reductions of 62.07% and 51.16%, respectively, raising both systems to A level. The building’s overall rating also rose to level A, with a total consumption of 210,801.56 kWh/year of primary energy and 131,750.97 kWh/year of electrical energy, resulting in a reduction of 30.15% of primary energy. In relation to electrical energy, the reduction was 33% compared to the electrical energy consumption of the base case (197,494.88 kWh/year).

This percentage is in line with the value in the national literature that, according to the National Electric Energy Conservation Program (PROCEL), by meeting the guidelines of regulations, standards and normative instructions for energy efficiency, it is possible to obtain a 30% reduction in consumption in existing buildings that undergo retrofit [1]. This allowed the reclassification from level C to level A and increased the percentage of self-sufficiency with 30% more renewable energy available, allowing for better EV integration capability.

However, as the study’s purpose is to maintain a good level of energy efficiency in buildings before increasing the consumption of the EV supply system, a maximum annual consumption value that the building could achieve without losing level A performance was calculated. With this stipulated value, a new energy management condition was applied for the charging of EVs, which, for example, in addition to limiting the power (according to the availability of the transformer), started to limit the system’s daily consumption and, consequently, its annual value.

The results of stages 3 and 4 showed that the energy efficiency actions resulted in the CEAMAZON building being classified as energy performance level A, even considering the consumption of the electric vehicle supply system. The margin of percentage reduction in primary energy consumption was 3.15% compared to the upper limit of level A, which is 27%, allowing for a prudent expansion of the energy consumption of the building’s systems. Using OpenDSS software version 10.0.0.2, a new energy consumption value of the EV supply system of 71,224.04 kWh/year was obtained, keeping the total consumption of the building below the maximum limit allowed to maintain the overall energy performance class of the building at level A. With the greater integration of electric vehicles, the building maintained its self-sufficiency classification as a near zero energy building (NZEB), with an energy generation potential (PG) of 79.47%, demonstrating the effectiveness of the retrofit measures implemented.

Finally, this well-guided increase in EV consumption can be simulated within the EE parameters. In conclusion, energy efficiency measures play an important role in reducing inefficient energy consumption, energy waste, and money as well as improving energy efficiency and self-sufficiency performance. In addition to all these benefits, they contributed to a 20% increase in the integration of EVs in the building.

Limitations and Recommendations for Future Work

Among the limitations that the study presents, there was no analysis of the possibility of increasing renewable energy generation, with a goal to obtain a higher level of self-sufficiency rating and positive energy, limiting the study only to the use and evaluation of the energy that is already generated today. This increase in photovoltaic energy generation could be applied in a future study, with a goal to increase the energy self-sufficiency rating.

The study was also limited to the use of electric vehicles that supply but do not return energy to the building. Therefore, it is recommended that future studies simulate this technology with load flow in the vehicle-to-building (V2B) direction and assess its impact on the INI-C’s energy efficiency and self-sufficiency rating.

Another suggestion for future work would be to apply energy management to the low (off-peak) and high (peak) energy consumption times of the Brazilian National Interconnected System (SIN), to assess the load restriction, shifting EV loads from peak times to off-peak times, and also assessing the greater coincidence of peak PV generation periods and the building’s energy consumption and the impact of this action on the energy efficiency and self-sufficiency rating using the INI-C’s methodology.

Finally, it would be interesting for the financial and environmental viability of the energy measures applied in this study to be analyzed so that the building manager can have an idea of the costs involved in implementing the proposed retrofit, using them as viability indicators for the initial capital needed to put the actions into practice, the payback on the investment and the cumulative cash flow over the lifetime of the project, and measuring the carbon footprint linked to the application of the system efficiency measures.