Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt
Abstract
:1. Introduction
2. V2G Proposed Optimization Framework
2.1. Proposed Framework
2.2. Battery Degradation Cost Model
2.3. Proposed V2G Scheduling Modelling and Constraints
2.4. Solving Based on the Genetec Algorithm (GA)
3. Results and Discussion
3.1. Case 1: Continuous Parking for a 2 h Interval Time
3.2. Case 2: Stochastic Parking for a 2 h Interval Time
3.3. Case 3: Parking for a 1h Interval Time
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
BEMS  Battery Energy Management System  $SO{C}_{i}$  Initial State of Charge 
EVCS  Electric Vehicle Charging Station  $SO{C}_{f}$  Final State of Charge 
G2V  GridtoVehicle  ${N}_{cl}$  EV Battery Life Span Charging/Discharging Cycles 
GA  Genetec Algorithm  $\alpha $, $\beta $  Coefficients of Battery Specifications 
MILP  Mixedinteger Linear Programming  $d$  Depth of Discharge 
PEVs  Plugin Electric Vehicles  ${E}_{c}^{max}$  Maximum Battery Capacity (kWh) 
SOC  State of Charge  ${\Psi}_{c}^{BC}$  Price of the Battery (EGP) 
V2B  VehicletoBuilding  $c$  Category of EV 
V2G  VehicletoGrid  $t$  Parking Interval Time 
V2V  VehicletoVehicle  ${\eta}_{D}$  Discharging Efficiency (%) 
${P}_{tc}^{G2V}$  EV Charging Power (kW)  $DOD$  Maximum Depth of the Discharge in each segment 
${\Psi}_{tc}^{TC\_G2V}$  EV Charging Tariff Cost (EGP/kWh)  ${a}_{0}$  Polynomial Coefficient of the Cycle Depth Degradation Function 
${\Psi}_{tc}^{RC\_V2G}$  EV Owner Discharging Revenue Cost (EGP/kWh)  ${P}_{tc}^{V2G}$  Discharging Power (kW) 
$\epsilon $  Switching Binary Number 1 or 0  ${T}_{Pset}$  Set of Parking Time Slots (h) 
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Refs.  Battery Degradation Cost  V2G Revenue  Optimization Algorithm (OA)  Battery Degradation Cost without Using the OA  Battery Degradation Cost Using the OA  $\mathit{S}\mathit{O}{\mathit{C}}_{\mathit{i}}$  $\mathit{S}\mathit{O}{\mathit{C}}_{\mathit{f}}$  Number of EVs  Findings Brief 

[23]  √  √  Nonlinear Programming (NLP)  0.4970 $/day  0.4347 $/day  ≈40%  80%  1050 

[24]  √  X  MixedInteger Linear Problem (MILP)  135.02 $/day  6.36 $/day  ≈25%  ≈35%  400 

[22]  √  √  MixedInteger Linear Problem (MILP)  N/A  0.834, 1.119, 2.477 and 2.146 $/kWh  70%  100%  N/A 

N/A  0.834, 0.834, 1.119 and 1.811 $/kWh  70%  100%  N/A 
 
[20]  √  √  CVX  39 $/day  23 $/day  N/A  N/A  100 

[25]  √  √  Nonlinear Programming (NLP)  0.4969 $/day  0.4348 $/day  ≈40%  80%  1000 

[26]  √  √  Generalized Reduced Gradient (GRG)  N/A  168.18 $/day  20–50%  80%  1000 

EV  Category  Rated Battery Capacity (kWh)  Battery Cost per kWh (LE/kWh) 

EV_1  Nissan Leaf (2020)  40  4837.89 
EV_2  Tesla Model S (p100d)  100  3677.58 
EV_3  Mustang MachE  68  4051.24 
Scenario 1 Time: 10:00 to 12:00  Scenario 2 Time: 10:00 to 12:00  

Number of EVs (EVs)  
EV_1  8  14 
EV_2  362  272 
EV_3  1  11 
Final SOC (%)  
EV_1  77.77%  61.31% 
EV_2  49.15%  48.84% 
EV_3  58.01%  52.36% 
Degradation Cost (EGP)  
EV_1  0.9765 EGP  1.6530 EGP 
EV_2  0.0877 EGP  0.1571 EGP 
EV_3  0.6658 EGP  1.1588 EGP 
EV owner Profit (EGP)  
EV_1  −34.2 EGP  −8.8 EGP 
EV_2  4.589 EGP  6.79 EGP 
EV_3  −14.9 EGP  4.429 EGP 
Scenario 1  Scenario 2  

Time: 10:00 to 11:00  Time: 11:00 to 12:00  Time: 10:00 to 11:00  Time: 11:00 to 12:00  
Number of EVs (EVs)  
EV_1  3  3  2  3 
EV_2  1  510  1  42 
EV_3  2  186  2  148 
Final SOC (%)  
EV_1  59.99%  61.92%  61.02%  63.6% 
EV_2  60.01%  59.85%  59.99%  59.09% 
EV_3  59.99%  58.81%  59.99%  58.06% 
Degradation Cost (EGP)  
EV_1  8.5 EGP  0.0158 EGP  9.4623 EGP  0.1258 EGP 
EV_2  3.3875 EGP  0.0359 EGP  4.0179 EGP  0.277 EGP 
EV_3  5.4939 EGP  0.2498 EGP  6.4311 EGP  0.3598 EGP 
EV owner Profit (EGP)  
EV_1  0  −2.9891 EGP  4.8152 EGP  −3.708 EGP 
EV_2  0  0.9611 EGP  0.0528 EGP  5.9294 EGP 
EV_3  0  4.5313 EGP  0.0235 EGP  7.1238 EGP 
Scenario 1  Scenario 2  

Time: 13:00 to 14:00  Time: 13:00 to 14:00  
Number of EVs (EVs)  
EV_1  1  1 
EV_2  277  52 
EV_3  3  1 
Final SOC (%)  
EV_1  60.23%  60.33% 
EV_2  48.26%  41.01% 
EV_3  60.39%  63.78% 
Degradation Cost (EGP)  
EV_1  4.6919 EGP  6.5213 EGP 
EV_2  0.3471 EGP  1.9322 EGP 
EV_3  4.9929 EGP  7.8514 EGP 
EV owner Profit (EGP)  
EV_1  0.0006 EGP  0.0036 EGP 
EV_2  9.3029 EGP  48.0133 EGP 
EV_3  0.0011 EGP  2.8661 EGP 
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Makeen, P.; Ghali, H.A.; Memon, S.; Duan, F. Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt. Sustainability 2023, 15, 1283. https://doi.org/10.3390/su15021283
Makeen P, Ghali HA, Memon S, Duan F. Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt. Sustainability. 2023; 15(2):1283. https://doi.org/10.3390/su15021283
Chicago/Turabian StyleMakeen, Peter, Hani A. Ghali, Saim Memon, and Fang Duan. 2023. "Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt" Sustainability 15, no. 2: 1283. https://doi.org/10.3390/su15021283