Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region
Abstract
:1. Introduction
1.1. General Considerations
1.2. Country Overview: Brazil
1.3. Literature Review
1.4. Work Hypotheses
- Depending on the number of electric cars being fueled simultaneously in the building, the integration of EVs may or may not impact the building’s energy performance and its NZEB/PEB rating unless a suitable charging management/strategy is implemented.
- Based on a series of restrictions/limitations, a suitable charging strategy can successfully integrate EVs into buildings, regardless of the number of electric cars being fueled simultaneously, without the risk of losing the NZEB/PEB rating or reducing energy performance.
1.5. Goals and Contribution
- A methodology that aims to insert the EV supply system as an end-use system in buildings, quantifying the impacts that the integration of EVs has on the level of efficiency and energy self-sufficiency of commercial, service, and public buildings, considering the guidelines of the Brazilian EE and NZEB assessment methodology determined by [6];
- Simulation of load level scenarios for EV arrivals and departures, based on stochastic analyses, such as the Monte Carlo method, with a realistic approach based on on-site measurement values, and generating results with good approximations of what would happen in practice with the integration of EVs in buildings;
- Energy management systems (EMS) methodology with optimization techniques, aiming to mitigate the impacts of EV consumption on demand, consumption, and EE and NZEB levels in buildings.
- Diagnose the energy performance of existing building systems to obtain the energy efficiency classification in existing conditions;
- Calculate the energy balance for the determination of the NZEB or PEB classification;
- Model the electric grid of the building and integrate electric vehicle charging, analyzed in two scenarios: one without management and the other with demand-side management techniques for EV charging.
2. Materials and Methods
2.1. Step 1—Energy Efficiency Classification Methodology
2.1.1. Data Survey
2.1.2. Computational Modeling
2.1.3. Thermo-Energetic Simulation
2.2. Step 2—Near Zero Energy Building (NZEB) or Positive Energy Building (PEB) Classification Methodology
2.3. Step 3—Predictability of Integrating Electric Car Charging into Buildings
2.4. Demand Side Response Techniques and Algorithm Explanation
2.4.1. Probabilistic Models and Monte Carlo Simulation
2.4.2. Mathematical Foundation
2.4.3. Demand-Side Management Strategy
- Scenario 1—Unrestricted loading: EVs can charge to their maximum capacity without restrictions. This leads to significant increases in the energy consumption of buildings. This means that during the simulation, the number of EVs remains a random number based on a normal distribution, and the amount of battery charge is also received as a random number based on a normal distribution. Therefore, EVs are inserted into the grid to start charging their batteries to full. Thus, this scenario aims to demonstrate the case in which EVs are charged without any restriction on their charging power and without restriction on the energy they consume from the building.
- Scenario 2—Top-up management: Charging is optimized by reducing the power consumed by each EV and switching off EVs with a charge percentage greater than 80% if necessary. This strategy limits charging power by avoiding consumption peaks and ensures that total energy consumption remains within acceptable limits, mitigating the impact on the building’s energy efficiency rating. This means that during the simulation, the number of EVs remains a random number based on a normal distribution, and the amount of battery charge is also received at a random number based on a normal distribution. However, EVs can have their charging power reduced to avoid consumption peaks at a given time. This is done if the transformer is close to its maximum capacity; that is, the algorithm works by reducing the power of the EVs so that there are no overloads in the building’s transformer. Furthermore, the energy consumed by EVs directly affects the energy efficiency rating of the building. For this, there is also a limitation on the energy consumed, so the algorithm can turn off the charging of an EV so that it can no longer charge that day, seeking to limit this energy consumption of EVs.
2.4.4. Python Implementations
2.5. Step 4—Assessment of Energy Performance in Buildings, Considering the EV Supply System
3. Case Study
4. Results
4.1. Energy Efficiency Level Classification of the CEAMAZON Building
4.1.1. Envelope Energy Efficiency Rating of the CEAMAZON Building
4.1.2. Efficiency Classification of the Air Conditioning System of the CEAMAZON Building
4.1.3. Lighting System Efficiency Rating of the CEAMAZON Building
4.1.4. General Energy Efficiency Classification of the CEAMAZON Building
4.2. Near Zero Energy Building (NZEB) or Positive Energy Building (PEB) Classification of the CEAMAZON Building
4.3. Assessment of Energy Performance CEAMAZON in Buildings, Considering the EV Supply System
4.3.1. Energy Assessment of the CEAMAZON Building, Considering the Consumption of Electric Cars: Scenario 01, without Optimization
4.3.2. Energy Assessment of the CEAMAZON Building, Considering the Consumption of Electric Cars: Scenario 02, with Optimization
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ABNT | Brazilian Association of Technical Standards |
AFIR | Parliament’s Alternative Fuels Infrastructure Regulation |
CAAE,real or refD | Electrical energy consumption of the water heating system of the real building |
CEAMAZON | Center of Excellence in Energy Efficiency of Amazon |
CEE,real | Total electrical energy consumption of the real building |
CEP,real | Primary energy consumption of the building’s real condition |
CEP,ref | The primary energy consumption of the building’s reference condition |
CEP,refDA,B or C | Primary energy consumption of the reference (level D) building condition for all the blocks |
CEPn | Primary energy consumption of a block n |
CEPrealA,B or C | Primary energy consumption of the real building for all the blocks |
CEQ | Equipment consumption |
CET,real | Total thermal energy consumption of the real building |
CgTT,refDA,B or C | Total annual thermal of the reference (level D) building condition for all blocks |
CgTTrealA,B or C | Total annual thermal load of the real building for all blocks |
CIL,refDA,B or C | Lighting consumption of the reference (level D) building condition all blocks |
CILrealA,B or C | Lighting consumption of the real building for all blocks |
COP | Coefficient of Performance |
CR,refDA,B or C | Cooling consumption of the reference (level D) building condition for all blocks |
CRCEPD-A | Coefficient of reduction of primary energy consumption from classification D to A |
CRCgTTD-A | Total annual thermal load reduction coefficient for classifications from D to A |
CRrealA,B or C | Cooling consumption of the real building for all block |
CU | Consumer unit |
CVE | Electric vehicle consumption |
PDE | Equipment Power Density |
DPI | Lighting Potency Density |
EDGE | Excellence in Design for Greater Efficiencies |
EE | Energy efficiency |
EMS | Energy management systems |
ENCE | Brazilian National Energy Conservation Label |
ESG | Environmental, Social & Governance |
EU | European Union |
EVs | Electric Vehicles |
FF | Form Factor |
GEE | Energy generated by local renewable energy sources |
HVAC | Heating, Ventilating and Air Conditioning |
INI-C | Inmetro Normative Instruction for the Energy Efficiency Classification of Commercial, Service and Public Buildings |
INI-R | Inmetro Normative Instruction for the Energy Efficiency Classification of Residential Buildings |
INMETRO | Brazilian National Institute of Metrology, Standardization and Industrial Quality |
NZEB | Near zero energy building |
p.u | Voltage per unit |
PAFT | Total Facade Opening Area Percentage |
PBE-Edifica | Brazilian BuildingLabeling Program |
PEB | Positive Energy Building |
PEDs | Positive Energy Districts |
PG | Energy generation potential |
PILA | Lighting power limit values for classification A |
PILD | Lighting power limit values for classification D |
PIT | Total installed lighting power |
PNME | Brazilian National Electric Mobility Platform |
PV | Photovoltaic |
RedCEPA,B OR C | Reduction in primary energy consumption for blocks A, B or C |
RedCEPn | Reduction in primary energy consumption of a block n |
RedCEPweighted | Weighted reduction in primary energy consumption |
RedCgTTA,B or C | Reduction in total annual thermal loads for all the blocks |
RedCILA,B or C | Reduction in lighting consumption for all blocks |
RedCRA,B or C | Reduction in air conditioning consumption for the blocks |
SC | Self-consumption |
SCSB | Self-consumption-sufficiency balance |
SIN | Brazil’s National Interconnected System |
SISGEE | Electricity Consumption Management System |
SS | Self-sufficiency |
UFPA | Federal University of Para |
V2G | Vehicle-to-grid |
ZB | Bioclimatic Zone |
ZEB | Zero-emission Buildings |
fCE | Conversion factor of electrical energy into primary energy |
fCT | Conversion factor of thermal energy into primary energy |
Appendix A
Variables | Description | Values | Source |
---|---|---|---|
Architectural dimensions 1 | Dimensions of walls, frames, ceiling, floors, and levels. |
| Site survey/INI-C |
Building materials | Physical characterization of materials used on the internal and external surfaces of the blocks. |
| Site Survey/INI-C |
Schedules | Timings and days of the year for the occupancy of a space. |
| Site survey |
Quantity of occupants | Number of people in artificially conditioned environments. | 287 people, distributed between 25 spaces, considering maximum occupancy. | Site survey/INI-C |
DPE 2 | The electrical power of the equipment is distributed per square meter of the built space. | 15 w/m2 | INI-C |
DPI 3 | Electrical power of artificial lighting per illuminated area of the block. | 9.34 W/m2 (Real value); Reference value: 15.5 W/m2 | 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. | 12,000; 18,000; 24,000; 30,000; 36,000; 58,000. (BTU/H) divided between 35 different HVAC machines. | Site survey |
IDRS | Seasonal cooling performance coefficient for air conditioning machines. | Reference cooling performance coefficient value: 2.6 reference model value/Variable between 2.51 and 3.40 in real model values | Site survey/INI-C |
Temperature Setpoint | Target temperature is used to achieve 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 |
Appendix B
Appendix C
Block | CEE,real | CEE,refD | CR,real | CR,refD | CIL,real | CIL,refD | CEQ,real | CEQ,refD | CEP,real | CEP,refD |
---|---|---|---|---|---|---|---|---|---|---|
A | 113,438.69 | 12,6317.46 | 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 |
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 |
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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 |
Climatic Classification | CRCgTTD-A | |||
---|---|---|---|---|
FF ≤ 0.20 | 0.20 < FF ≤ 0.30 | 0.30 < FF ≤ 0.40 | 0.40 < FF ≤ 0.50 | |
ZB 8 | 0.08 | 0.09 | 0.10 | 0.10 |
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 |
Energy Efficiency Classification | RedCgTT, RedCIL e RedCEP or RedCEPweighted (%) | ||||
---|---|---|---|---|---|
A | B | C | D | E | |
ZB 7 and 8 | Red > 3i% | 3i% ≥ Red > 2i% | 2i% ≥ Red > i% | i% ≥ Red ≥ 0% | Red < 0% |
RedCgTT (%) | ||||
---|---|---|---|---|
A | B | C | D | E |
RedCgTT > 10% | 10% ≥ RedCgTT > 6.67% | 6.67% ≥ RedCgTT > 3.33% | 3.33% ≥ RedCgTT ≥ 0% | RedCgTT < 0% |
Climate Classification | RedCR (%) | ||||
---|---|---|---|---|---|
Bioclimatic Zones | A | B | C | D | E |
ZB 7 and 8 | RedCR > 43% | 43% ≥ RedCR > 29% | 29% ≥ RedCR > 14% | 14% ≥ RedCR ≥ 0% | RedCR < 0% |
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% |
RedCEP (%) | ||||
---|---|---|---|---|
A | B | C | D | E |
RedCEP > 27% | 27% ≥ RedCEP > 18% | 18% ≥ RedCEP > 9% | 9% ≥ RedCEP ≥ 0% | RedCEP < 0% |
Scenarios | Base Scenario | Scenario 01 | Scenario 02 | |||
---|---|---|---|---|---|---|
Energy generation and consumption | PV Generation (kWh/year) | Total consumption of the building (kWh/year) | EV consumption (kWh/year) | Total consumption of the building with EV (kWh/year) | EV consumption (kWh/year) | Total consumption of the building with EV (kWh/year) |
Electrical energy | 116,065.70 | 140,567.88 | 97,388.42 | 237,956.30 | 56,927.00 | 197,494.88 |
Primary energy | 18,570,512.22 | 224,908.61 | 155,821.46 | 380,730.07 | 91,083.21 | 315,991.82 |
EE Rating Level | Level C | Level D | Level D | |||
NZEB/EEP Rating Level | PG of- 82.57% = NZEB | PG of 48.78% = no classification | PG of 58.77% = NZEB |
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Souza, A.C.D.B.d.; Vasconcelos, F.M.d.; Moreira, G.A.M.; Alves, J.V.d.R.; Tabora, J.M.; Tostes, M.E.d.L.; Carvalho, C.C.M.d.M.; Nascimento, A.A.d. Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region. Energies 2024, 17, 4060. https://doi.org/10.3390/en17164060
Souza ACDBd, Vasconcelos FMd, Moreira GAM, Alves JVdR, Tabora JM, Tostes MEdL, Carvalho CCMdM, Nascimento AAd. Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region. Energies. 2024; 17(16):4060. https://doi.org/10.3390/en17164060
Chicago/Turabian StyleSouza, Ana Carolina Dias Barreto de, Filipe Menezes de Vasconcelos, Gabriel Abel Massunanga Moreira, João Victor dos Reis. Alves, Jonathan Muñoz Tabora, Maria Emília de Lima Tostes, Carminda Célia Moura de Moura Carvalho, and Andreia Antloga do Nascimento. 2024. "Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region" Energies 17, no. 16: 4060. https://doi.org/10.3390/en17164060