Feasibility Assessment of Photovoltaic Systems to Save Energy Consumption in Residential Houses with Electric Vehicles in Chile
Abstract
:1. Introduction
 Study the state of the art for energy efficiency at the residential/grid level and compare different case studies for the implementation of PV systems that aim to save the energy consumption of homes/buildings with/without EV charging;
 Collect data from the selected site for the case study, including real energy consumption, meteorological information, solar irradiation, and technical specifications of the selected EV;
 Calculate the capacities of the equipment and its subsystems for the selection of the required technology for the PV system;
 Determine the available surface space required for the installation of the PV system.
2. Related Work
2.1. Smart Homes and Microgrids
2.2. Local Context of Chile
2.3. Configuration of PV System
2.3.1. PV System Interconnected to the Grid
2.3.2. PV System with BESS
2.3.3. PV System with BESS for EV Charging
2.3.4. PV System without BESS for EV Charging
2.3.5. Hybrid PVWind System with BESS for EV Charging Station
2.3.6. OffGrid PV or Small Wind Turbine System with BESS
3. Data Collection
3.1. Meteorological Information
3.2. Home Power Consumption Information
3.3. Grid Information
4. PV System Dimensioning
4.1. PV Array Dimensioning
4.2. Inverter Dimensioning
4.3. Battery Bank Dimensioning
4.4. Other Components
5. Results
5.1. Results of PV System with BESS
5.2. Results of PV System without BESS
5.3. Simulation Results of PVsyst
5.4. Results of Different Sizing Based on the Available Surface
 None of the scenarios turns out to be profitable for the discount rate considered. This is mainly due to the high initial investment cost that the acquisition of the BESS presents;
 For the scenarios of 4, 12 and 16 panels, the projects are not profitable and the investment would not recover within the time interval;
 For the 8 panels scenario, a payback is achieved within the project horizon, however, a discount rate of at most 0.4% would be needed for the project to be profitable, which turns out to be very below the normally used range of 5 to 10%.
 The cases of 4, 8 and 12 panels are profitable, while the case of 16 panels does not meet the expected profitability for the selected discount rate;
 The most favorable case is 8 PV panels. It is observed that sizing on a smaller scale in relation to sizing based on the energy consumption results in greater profitability. This is mainly due to the fact that, compared to the cases of 12 and 16 panels, a lower initial investment is required, and at the same time a greater fraction of the energy generated is selfconsumed. This allows greater savings to be achieved due to the cost of electricity investment and a smaller amount of surpluses are also generated which are valued at a lower price than selfconsumed energy. On the other hand, compared to the case of 4 PV panels, in the latter case, based on selfconsumption and the injection of surpluses, it is not possible to reduce the electricity rate to zero, so the greatest possible economic savings are not obtained. In the case of 8 panels, despite requiring a greater initial investment, there is a greater generation, achieving greater selfconsumption (for example, in the case of 4 panels, for the first year selfconsumption is of 1338 kWh/year, while in the case of 8 panels it amounts to 1493 kWh/year), which allows greater annual economic savings and recover the investment in a shorter period of time;
 The decrease in profitability from the case of 8 PV panels to 16 PV panels is because there is a selfconsumption limit for PV energy, which is defined based on the same energy consumption of the home. Therefore, by installing a greater number of panels, a greater investment is required, and at the same time the annual economic savings will be capped at the amount equivalent to the annual electricity rates to be paid, corresponding to CLP$565.059 (see Table 12).
 With the exception of the case with 4 PV panels, the IRR increases for all the projects because there is a greater economic reimbursement due to the injection of surpluses. In the case of 4 panels, this does not happen because the total annual savings fail to reduce the electricity rate to zero, so the limitation in this scenario does not affect the results;
 Unlike the case with limitation, now the scenario with 16 panels turns out to have the highest IRR, because the highest level of surplus is generated, and therefore greater economic savings;
 Neither case is profitable at the selected discount rate;
 There is a direct relationship between the number of PV panels and the profitability of the project. Therefore, for PV systems with a number of panels greater than 16, it is projected that an IRR would be obtained within the range between 5% and 10%, allowing these projects to be profitable;
 Cases with 4 and 8 panels, the total annual savings fail to reduce the annual electricity rate to zero. Therefore, the limitation does not affect the results in these cases;
 Despite the increase in the profitability of cases with 12 and 16 panels, the case with 8 panels is still the optimal;
 The cases of 12 and 16 panels are less profitable than the case with 8 panels, because the reimbursement of surpluses cannot compensate for the increase in CAPEX required in those cases.
5.5. Economic Results According to Storage Capacity of the Battery Bank
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref.  Location  Photovoltaic System  Battery Storage System  Electric Vehicle  Wind Turbine  Data Collection 

[9]  Qatar  Yes  Yes  No  No  Yes 
[12]  Ecuador  Yes  No  No  No  No 
[13]  Chile  Yes  No  No  No  Yes 
[14]  United Kingdom  No  No  No  No  Yes 
[15]  Slovenia  Yes  Yes  No  No  No 
[16]  United States  Yes  Yes  Yes  No  Yes 
[17]  South Korea  Yes  No  Yes  No  Yes 
[18]  Sweden  Yes  No  Yes  No  Yes 
[19]  Netherlands  Yes  No  Yes  No  Yes 
[20]  Spain  Yes  Yes  Yes  Yes  No 
[21]  Spain  No  No  Yes  No  No 
[22]  Chile  No  Yes  No  Yes  Yes 
Current work  Chile  Yes  Yes  Yes  No  Yes 
Ref. No.  Contribution  Specifications (A: Generation System, B: Consumption Parameters, C: EV Parameters) 

[9]  Dimensioning of BESS based on monitored data on consumption and experimental data of PV generation for households with different consumption profiles  (A) Simulated PV system power: 5 and 20 kW 
[12]  PV system design for a house from simulated local data of global radiation, consumption, generation, and injection into the grid, using PVsyst software  (A) PV power required: 1.4 kWp, Generation: 1519.3 kWh/year (B) Daily consumption: 17.8 kWh/day, Injection into the grid: 1398.3 MWh/year 
[13]  Economic feasibility evaluation of a PV system for a university library, using a survey to estimate consumption, and the simulation of local generation using data from Solar Explorer  (A) PV panel power: 250 Wp, PV panel efficiency: 18–22%, PV panel dimensions: 1.64 × 0.99 m${}^{2}$ (B) Daily consumption: 150–450 kWh/day, Monthly consumption: 2000–10,000 kWh/month, Total area: 1368 m${}^{2}$ 
[14]  Data collection and monitoring of active power for 2 years, both of the total consumption and of the most demanding appliances in a residential neighborhood  (B) N° monitored houses: 21 
[15]  PV system on site installation in a house, data collection and monitoring of active power, consumption, generation and BESS state of charge  (A) Installed PV power: 6.72 kWp, Useful storage capacity: 6.6 kWh 
[16]  PV system on site installation, data collection and monitoring, both consumption, PV generation and state of charge of the BESS and EV, for a ZEH  (A) PV installed power: 2.5 kWp, BESS storage capacity: 10 kWh, Surface area: 210 ft${}^{2}$ (B) Power consumption: 750–2000 W (C) Model: Toyota Prius, Battery model: Lithiumion 5 kWh 
[17]  Algorithm to optimize the EVs charging for the staff of a shopping center  (A) Simulations: 50 kW PV system generation was simulated from a 3 kW PV system of a school. Consumption was simulated based on real data from a utility company of South Korea (C) 12 identical EVs, Battery capacity: 24 kWh, Power chargers: 1–7.7 kW 
[19]  Simulation of the behavior of consumption curves from four different energy management algorithms for a smart microgrid composed by an office, internet servers, three homes, and two EVs  (A) PV installed power: 31 kWp (B) Injection into the microgrid: 2.0–12.4 MWh/year (C) EV Model:

[20]  Algorithm modeling of EV charging demand and hybrid PVwind generation in a charging station  (A) Multiple cases: PV installation area: 0–1875 m${}^{2}$, Storage capacity: 0–500 kWh, N° wind turbines: 1–4, Wind turbine power: 1–3 kW (B) Power consumption from the grid: 0–300 kW (C) N° EV chargers: 1–10, EV charger power: 50 kW, Battery capacity:

Ref. No.  Contribution  Specifications (A: Generation System, B: Consumption Parameters, C: EV Parameters) 

[21]  Standardized charging methodologies for EVs with different battery capacities  (C) Domestic SAE standard: Voltage: 120–240 Vac, Maximum current (AC): 12–16 A, Charger power: 1.4–1.9 kW, PHEV charging time: 7 h (0–100% battery charge), BEV charge time: 17 h (20–100% battery charge), Installation cost: 500–800 USD (C) IEC domestic standard: Voltage: 230–450 Vac, Maximum current (AC): 16 A, Charger power: 3.7–11 kW 
[22]  Evaluation of the economic feasibility of the implementation of a small wind turbine with BESS in a house isolated from the grid  (A) Wind turbine power: 6 kW, N° batteries: 4, Battery capacity: 200 Ah, N° inverter/charger: 2, Nominal power inverter/charger: 3000 W (B) Installed power: 5202 W, Daily consumption: 28.4 kWh/day, House total area: 52 m${}^{2}$ 
Parameters  Description 

Model  Nissan Leaf e+ 
Actual battery capacity  62 kWh 
Charging mode (home)  Wall plug 2.3 kW (230 V/10 A), Full charge time: 28 h 45 min, Charge speed: 11 km/h 
Parameters  Description 

Model  Ulica Solar UL410M144 
Nominal power  410 Wp 
Nominal efficiency  20.18% 
Effective PV area  1.815 mm${}^{2}$ 
Energy Loss Factor  Estimation  Description 

PV panel power tolerance  1  Manufacturer indicates 0/+5 W, so in the worst case there would be no power loss in the module 
Inverter efficiency  0.88  
PV panel error  0.98  Losses caused by small differences in the parameters between installed PV panels [30] 
Losses in diodes and connection terminals  0.995  
Direct current ohmic losses  0.98  
Altern current ohmic losses  0.99  
PV panel soiling  0.95  Mainly due to dust, bird droppings, among others 
System unavailability  0.98  Either due to PV system component failures or maintenance stops 
External shades  0.9 
Parameters  Description 

Model  Voltronic Power InfiniSolar E 5.5 KW 
Nominal Power  5500 W 
Bank battery voltage  48 Vdc 
Parameters  Description 

Model  Rolls Flooded Deep Cycle Battery 8 CS 17P 
Nominal voltage  8 V 
Nominal capacity C20 (discharge in 20 h)  568 Ah 
Component  Quantity  Unitary Price without VAT (CLP)  Total Price (CLP) 

PV panel  12  $96.720  $1.160.040 
Hybrid inverter  1  $1.150.000  $1.150.000 
Battery  6  $705.037  $4.230.222 
Wiring and Electrical piping  –  $151.243  $151.243 
Electrical proteccions  –  –  $506.612 
Assembly cost  –  –  $719.872 
Installation  –  –  $1.079.807 
CAPEX without VAT  $8.998.395  
CAPEX plus VAT  $10.708.091 
Component  Quantity  Unitary Price without VAT (CLP)  Total Price (CLP) 

PV panel  12  $96.720  $1.160.040 
Inverter  1  $1.150.000  $1.150.000 
Wiring and Electrical piping  –  $140.743  $140.743 
Electrical protections  –  –  $398.170 
Assembly cost  –  –  $284.955 
Installation  –  –  $427.433 
CAPEX without VAT  $3.561.941  
CAPEX plus VAT  $4.238.710 
Configuration Type  PV Panels  Required Surface (m${}^{2}$)  CAPEX (CLP$)  NPV (CLP$)  IRR (%)  Payback (Years) 

PV+BESS  4  from 9  8.229.260  −6.384.060  −4.8  No payback 
PV+BESS  8  from 17  9.143.939  −5.270.035  0.4  23.9 
PV+BESS  12  from 25  10.708.091  −6.804.372  −0.6  No Payback 
PV+BESS  16  from 34  11.724.407  −7.820.688  −1.3  No Payback 
PV  4  from 9  1.859.335  612.966  14.2  6.7 
PV  8  from 17  2.782.648  1.358.395  16.2  5.9 
PV  12  from 25  4.238.710  890.354  12.7  7.5 
PV  16  from 34  5.333.011  −203.947  9.5  9.4 
PV Panels  CAPEX (CLP$)  Annual Tariff without PV System (CLP$)  Average Annual Selfconsumption Savings (CLP$)  Average Annual Surplus Injection Savings (CLP$)  Total Annual Savings (CLP$)  Payback (CLP$) 

4  1.859.335  565.059  215.587  50.185  265.771  6.7 
8  2.782.648  565.059  244.263  200.298  444.561  5.9 
12  4.238.710  565.059  251.708  313.351  565.059  7.5 
16  5.333.011  565.059  255.847  309.212  565.059  9.4 
Configuration Type  PV Panels  Required Surface (m${}^{2}$)  CAPEX (CLP$)  NPV (CLP$)  IRR(%)  Payback (Years) 

PV+BESS  4  from 9  8.229.260  −6.384.060  −4.8  No payback 
PV+BESS  8  from 17  9.143.939  −5.104.013  0.7  23.5 
PV+BESS  12  from 25  10.708.091  −4.900.371  2.7  19.4 
PV+BESS  16  from 34  11.724.407  −4.351.490  4.3  16.9 
PV  4  from 9  1.859.335  612.966  14.2  6.7 
PV  8  from 17  2.782.648  1.358.395  16.2  5.9 
PV  12  from 25  4.238.710  1.485.347  14.5  6.5 
PV  16  from 34  5.333.011  1.960.930  14.7  6.4 
Case  N° Batteries  Nominal Voltage (V)  Capacity C20 (Ah)  Storage Potential (kWh) 

1  6  8  568  12.3 
2  4  12  371  8 
3  4  12  210  4.5 
4  4  12  155  3.3 
5  4  12  85  1.8 
Case  CAPEX  NPV (CLP$)  IRR (%)  Payback (Years) 

1  10.708.091  −6.804.372  −0.6  No payback 
2  8.503.352  −4.170.300  2.4  19.9 
3  5.680.272  −2.197.107  4.2  16.1 
4  5.374.967  −1.003.456  7.4  10.7 
5  4.954.677  −616.922  8.3  10.1 
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SallesMardones, J.; FloresMaradiaga, A.; Ahmed, M.A. Feasibility Assessment of Photovoltaic Systems to Save Energy Consumption in Residential Houses with Electric Vehicles in Chile. Sustainability 2022, 14, 5377. https://doi.org/10.3390/su14095377
SallesMardones J, FloresMaradiaga A, Ahmed MA. Feasibility Assessment of Photovoltaic Systems to Save Energy Consumption in Residential Houses with Electric Vehicles in Chile. Sustainability. 2022; 14(9):5377. https://doi.org/10.3390/su14095377
Chicago/Turabian StyleSallesMardones, Javier, Alex FloresMaradiaga, and Mohamed A. Ahmed. 2022. "Feasibility Assessment of Photovoltaic Systems to Save Energy Consumption in Residential Houses with Electric Vehicles in Chile" Sustainability 14, no. 9: 5377. https://doi.org/10.3390/su14095377