# Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Analysis of EV Owners’ Charging Behavior

## 3. Determination of Charging Strategy for Charging Station

_{ij}in the matrix represents the charging status of the ith EV during the jth time period and can be specified as:

_{c}is related to the EV initial state of charge $So{C}_{st}$, the EV final state of charge $SO{C}_{en}$, the charging power $P$, the charge efficiency $\eta $, and the size capacity of the battery ${B}_{r}$, which can be expressed as:

#### 3.1. Research on Charging Price of EVs

_{ev}of the charging station operator to the EV owner is comprised of electricity purchase price and the service fee of the station. The specific expression is:

_{ev}is the unit charging price of the EV, p

_{grid}(t) is the unit selling price from the power grid to the charging station, and p

_{sta}(t) is the unit service cost of the vehicle charging during T time period, which is expressed as:

#### 3.2. EV Charging Mode

_{m}, t

_{k}, and t

_{par}are used to represent the fast charging time, slow charging time, and parking time of the vehicle in the charging station. When EVs in the area are fully dispatchable, the charging status of the vehicle at different parking time are shown in Table 2.

_{p}) can be expressed as:

_{k}is the unschedulable fast charging mode, D

_{k}is the schedulable fast charging mode, B

_{m}is the unschedulable slow charging mode, and D

_{m}is the schedulable slow charging mode. ${\lambda}_{1}$, ${\lambda}_{2}$, and ${\lambda}_{3}$ are binary variables, indicating that the charge status of vehicle is incompatible, and it is selected by the owner. P

_{m}and P

_{c}are the charging power of slow charging and fast charging.

## 4. Model Optimization

_{1}is the target function of the vehicle owner charging rates, P

_{i}is the power of the ith vehicle, p

_{j}is the price of the charging station during jth time period, and x

_{ij}is the charging of the ith EV during the jth time period, $\mathsf{\Delta}t$ is a 15 min interval out of 96 scheduling periods in a day. f

_{2}is the system peak-to-valley difference objective function, ${L}_{\mathrm{max}}$ is the load peak, L

_{min}is the load valley, P

_{0j}is the system normal load, and ${{\displaystyle \sum}}_{i=1}^{N}{P}_{i}{x}_{ij}$ is the charging load of $N$ EVs at jth time period.

## 5. Case Analysis

#### 5.1. Hypotheses of Simulation

_{c}to the grid load period within t

_{stay}, the time period of the vehicle staying in the charging station, in the case of the same number of EV grid-connected charging, the peak-to-valley load difference will be greatly reduced. The heuristic algorithm is used to solve the mathematical model above.

_{h}= 90%), and the charging efficiency $\eta $ = 1, the battery capacity of EVs is 33kW (B

_{r}= 33kW), and the battery level must be higher than the desired amount of power required when an EV leaves.

_{st}and the expected pickup time t

_{en}of N EVs in charging stations, EV initial charging SOC

_{st}, and EV desired charging SOC

_{h}. To verify the control effect of coordinated charging, the article also calculates the operational state of the charging station under the condition of disordered charging, and compares the simulated results with the coordinated charging situation.

#### 5.2. Simulation Results

#### 5.2.1. Coordinated Charging and Disordered Charging

#### 5.2.2. Different Charging Modes in Disordered Charging

#### 5.2.3. Different Charging Modes in Coordinated Charging

#### 5.2.4. Coordinated Charging Analysis under Different EV Controllable Ratios

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Clairand, J.M.; Javier, R.G.; Bel, C.A. Smart Charging for Electric Vehicle Aggregators considering Users’ Preferences. IEEE Access
**2018**, 6, 54624–54635. [Google Scholar] [CrossRef] - Gan, L.; Ufuk, T.; Steven, H.L. Optimal Decentralized Protocol for Electric Vehicle Charging. IEEE Trans. Power Syst.
**2013**, 28, 940–951. [Google Scholar] [CrossRef] - Yao, W.; Zhao, J.; Wen, F. A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles. IEEE Trans. Power Syst.
**2013**, 28, 2768–2778. [Google Scholar] [CrossRef] - Richardson, P.; Flynn, D.; Keane, A. Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems. IEEE Trans. Power Syst.
**2012**, 27, 268–279. [Google Scholar] [CrossRef] - Rashidizadeh-Kermani, H.; Vahedipour-Dahraie, M.; Najafi, H.R.; Anvari-Moghaddam, A.; Guerrero, J.M. A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets. Appl. Sci.
**2017**, 7, 1100. [Google Scholar] [CrossRef] - Wang, B.; Ai, X. Coordinated Charging of Peak-valley Time-period Optimization by Considering V2G User Reactivity. Mod. Electr. Power
**2016**, 33, 39–44. [Google Scholar] - Chokkalingam, B.; Padmanaban, S.; Siano, P.; Krishnamoorthy, R.; Selvaraj, R. Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems. Energies
**2017**, 10, 377. [Google Scholar] [CrossRef] - Tang, D.; Peng, W. Nodal Impact Assessment and Alleviation of Moving Electric Vehicle Loads: From Traffic Flow to Power Flow. IEEE Trans. Power Syst.
**2016**, 31, 4231–4242. [Google Scholar] [CrossRef] - Mao, T.; Zhang, X.; Zhou, B. Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes. Energies
**2019**, 12, 265. [Google Scholar] [CrossRef] - Sun, B.; Huang, Z.; Tan, X. Optimal Scheduling for Electric Vehicle Charging with Discrete Charging Levels in Distribution Grid. IEEE Trans. Smart Grid
**2018**, 9, 624–634. [Google Scholar] [CrossRef] - Erdinc, O.; Uzunoglu, M. Optimum design of hybrid renewable energy systems: Overview of different approaches. Renew. Sustain. Energy Rev.
**2012**, 16, 1412–1425. [Google Scholar] [CrossRef] - Xiao, X.; Wen, J.; Tao, S. Study and Recommendations of the Key Issues in Planning of Electric Vehicles’ Charging Facilities. Trans. China Electrotech. Soc.
**2014**, 29, 1–10. [Google Scholar] - Sarker, M.; Kirschen, D. Optimal coordination and scheduling of demand response via monetary incentives. IEEE Trans. Power Syst.
**2015**, 6, 1341–1351. [Google Scholar] [CrossRef] - Luo, C.; Huang, Y.; Gupta, V. Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage. IEEE Trans. Smart Grid
**2018**, 9, 91494–91505. [Google Scholar] [CrossRef] - Liu, P.; Yu, J.; Mohammed, E. Decentralised PEV charging coordination to absorb surplus wind energy via stochastically staggered dual-tariff schemes considering feeder-level regulations. IET Gener. Transm. Distrib.
**2018**, 12, 3655–3665. [Google Scholar] [CrossRef] - Zethmayr, J.; Kolata, D. Charge for Less: An Analysis of Hourly Electricity Pricing for Electric Vehicles. World Electr. Veh. J.
**2019**, 10, 6. [Google Scholar] [CrossRef] - Luo, Z.; Hu, Z.; Song, Y.; Xu, Z. Coordinated Charging and Discharging of Large-scale Plug-in Electric Vehicles with Cost and Capacity Benefit Analysis. Autom. Electr. Power Syst.
**2012**, 36, 19–26. [Google Scholar] - Federal Highway Administration. US Department of Transportation. 2009 National Household Travel Survey [EB/OL]. Available online: http://nhts.ornl.gov (accessed on 30 April 2019).
- Dang, J.; Tang, Y.; Ning, J.; Fu, X.; Wang, B. A Strategy for Distribution of Electric Vehicles Charging Load Based on User Intention and Trip Rule. Power Syst. Prot. Control
**2015**, 16, 8–15. [Google Scholar] - Zhang, W.; Zhang, D.; Wen, J.; Zhang, C. Optimized Design of Electric Bus Battery Swap Station. J. Beijing Jiaotong Univ.
**2012**, 36, 100–104. [Google Scholar] - Xu, Z.; Hu, Z.; Song, Y. Coordinated Charging of Plug-in Electric Vehicles in Charging Stations. Autom. Electr. Power Syst.
**2012**, 36, 38–43. [Google Scholar] - Luo, Q. Studies on the Impacts of Electric Vehicles Charging to the Power System and Coordinated Charging. Master’s Thesis, Zhejiang University, Zhejiang, China, 2016. [Google Scholar]

**Figure 2.**Coordinated charging and disorderly charging EV load curve in 400 EVs fully controllable mode.

**Figure 3.**Coordinated charging and disordered charging total load curve of 400 EVs in fully controllable mode.

**Figure 5.**Coordinated charging load curves of 400 EVs in fast charging, fast & slow charging and slow charging mode.

Period of Time | Charging Station Purchase Price (USD/kWh) | Charging Station Selling Price When EV Owner Obeys the Dispatch (USD/kWh) | Charging Station Selling Price When EV Owner Disobeys the Dispatch (USD/kWh) |
---|---|---|---|

The peak time 8:00–12:00 17:00–21:00 | 0.126 | 0.189 | 0.251 |

The normal times 12:00–17:00 21:00–24:00 | 0.099 | 0.149 | 0.199 |

The valley time 0:00–8:00 | 0.053 | 0.079 | 0.106 |

Mode | Fast Charge Scheduling Status | Slow Charge Scheduling Status |
---|---|---|

${t}_{par}\le {t}_{k}$ | Fast charging but not dispatchable | Slow charging but not dispatchable |

${t}_{k}<{t}_{par}\le {t}_{m}$ | Fast charging and dispatchable | Slow charging but not dispatchable |

${t}_{par}>{t}_{m}$ | Fast charging and dispatchable | Slow charging and dispatchable |

**Table 3.**Charging cost and peak-to-valley difference comparison of different charging modes under fully controllable conditions of electric vehicles (100% charge response).

Optimization Method | System Load Peak-To-Valley Difference (kW) | Charging Rates (USD) | Charging Station Revenue (USD) |
---|---|---|---|

Disordered charging | 4045 | 1227 | 409 |

Coordinated charging (fast charge & slow charge) | 2842 | 770 | 257 |

Coordinated charging (fast charge) | 2908 | 752 | 247 |

Coordinated charging (slow charge) | 2828 | 754 | 251 |

**Table 4.**Analysis of the effects of charging load and income of 400 EVs with different controllable proportions.

EV Charging Controllable Ration (%) | System Load Peak-To-Valley Difference (kW) | EV Charging Rates Regardless of Scheduling Fee (USD) | Charging Station Revenue Regardless of Scheduling Fee (USD) | EV Charging Rates Considering Scheduling Fee (USD) | Charging Station Revenue Considering Scheduling Fee (USD) | Proportion of Revenue Increase Considering the Scheduling Fee (%) |
---|---|---|---|---|---|---|

30% | 3483 | 1139 | 370 | 1387 | 647 | 74.7 |

60% | 3190 | 1025 | 342 | 1185 | 502 | 46.8 |

90% | 2980 | 927 | 309 | 961 | 343 | 10.9 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhao, Y.; Huang, H.; Chen, X.; Zhang, B.; Zhang, Y.; Jin, Y.; Zhang, Q.; Cheng, L.; Chen, Y.
Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode. *World Electr. Veh. J.* **2019**, *10*, 47.
https://doi.org/10.3390/wevj10020047

**AMA Style**

Zhao Y, Huang H, Chen X, Zhang B, Zhang Y, Jin Y, Zhang Q, Cheng L, Chen Y.
Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode. *World Electric Vehicle Journal*. 2019; 10(2):47.
https://doi.org/10.3390/wevj10020047

**Chicago/Turabian Style**

Zhao, Yutong, Hong Huang, Xi Chen, Baoqun Zhang, Yiguo Zhang, Yuan Jin, Qian Zhang, Lin Cheng, and Yanxia Chen.
2019. "Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode" *World Electric Vehicle Journal* 10, no. 2: 47.
https://doi.org/10.3390/wevj10020047