Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits
2. Methodology Description
3. Problem Formulation and Transformation
3.1. Cost Model of Battery Capacity Fade
3.1.1. Battery Capacity Degradation Analysis
3.1.2. Cost of Battery Capacity Degradation Modeling
3.2. Electricity Cost
3.3. Grid Load Curve Optimization
- Step 1.
- Initialize the parameters visual distance, step length, population size, crowded degree, and iteration times. The visual distance and step values will decrease as the number of iterations increases. A group of artificial fish M is generated randomly in the water, and the information contained in each fish is a charging start time matrix U of all EVs. Each artificial fish here represents a charging start time scheme for all EVs.
- Step 2.
- Calculate the food concentration of each fish’s position, record the best result on the bulletin board. The food concentration (i.e., the variance of distribution network) load is calculated based on the objective function.
- Step 3.
- Artificial fishes move one step by performing one of the four kinds of behavior, which are cluster behavior, following behavior, foraging behavior, and random behavior, according to their situation.
- Step 4.
- Calculate the food concentration of each fish’s new position. Record the best result to the bulletin board if it is better than the old one.
- Step 5.
- If the ending condition is met, finish the algorithm. If not, go to step 3.
3.4. Queuing Theory for Busy Areas
4. Numerical Results
4.1. Regular Routes Planning Simulation
4.1.1. Monte Carlo Method
4.1.2. Results of Regular Routes Simulation
4.2. Irregular Routes Planning Simulation
- A cost model of battery capacity degradation is developed to estimate the cost of battery capacity degradation, which is usually not paid much attention by EV users. The optimal SOC range planning based on this model enables the the cost of battery degradation to be significantly reduced.
- For regular routes, to keep the operating cost low for the routes, such as commuting, charging time is also shifted to achieve the lowest electricity cost.
- For regular routes, the grid operation is optimized by AFSA with constraints which occur in the process of maximizing EV users’ benefit. In the meantime, the daily load curve in the distribution network can still be flattened markedly.
- For irregular routes, the average queue time is greatly decreased due to the application of queuing theory, and the cost of battery degradation is also reduced by the SOC range planning based on the cost model of battery degradation.
Conflicts of Interest
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|Vehicle Type||Capacity (kWh)||Consumption (kWh/100 km)||Sales in 2016|
|BAIC E-Series EV||25.6||15||18814|
|BAIC EU260 EV||41.4||15.9||18805|
|Cost of Battery Degradation (USD)||0.94||0.17|
|Electricity Cost (USD)||0.51||0.46|
|Method||Variance||Peak (kW)||Valley (kW)|
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Su, S.; Li, H.; Gao, D.W. Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits. Energies 2017, 10, 952. https://doi.org/10.3390/en10070952
Su S, Li H, Gao DW. Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits. Energies. 2017; 10(7):952. https://doi.org/10.3390/en10070952Chicago/Turabian Style
Su, Su, Hao Li, and David Wenzhong Gao. 2017. "Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits" Energies 10, no. 7: 952. https://doi.org/10.3390/en10070952