# Collaborative Planning of Community Charging Facilities and Distribution Networks

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## Abstract

**:**

## 1. Introduction

## 2. New Community Collaborative Optimization Construction Method Framework

- (1)
- Calculate the basic electricity load of the new community in the target year by analyzing the occupancy rate, planned number of households and residential electricity characteristics, so as to plan the platform capacity of each load node;
- (2)
- Carrying out data analysis on the number of cars owned by 1000 people and the proportion of electric vehicles in the city where the newly built community is located, combined with the prediction results of the charging probability of electric vehicles, the charging load of electric vehicles in the newly built communities in the target year can be predicted;
- (3)
- Take residential buildings as base load nodes, calibrate base load locations in newly built communities, calculate load density, and determine the location of EV charging nodes and 10 kV outlet nodes;
- (4)
- Considering constraints such as line power, voltage amplitude, charging demand and platform capacity of distribution network in newly built communities, an economic model of collaborative optimization construction between electric vehicle charging facilities and distribution network in newly built communities is established. Combining the Prim algorithm and the single-parent genetic algorithm, the topology of the distribution network grid, the line type of the feeder, etc., are determined, and the construction of the charging facilities is planned.

## 3. Forecasting Base Load and Charging Load in New Communities

#### 3.1. Prediction of Basic Electricity Load

#### 3.2. EV Charging Load Prediction

#### 3.2.1. Prediction of Electric Vehicle Ownership in Communities

#### 3.2.2. EV Charging Probability Prediction in Communities

- (1)
- Starting time of electric vehicle charging

- (2)
- Charging start SOC

_{0}is the initial SOC of EV charging; d

_{k}is the mileage of the k trip of the electric vehicle; d

_{full}represents the maximum number of miles an electric car can travel on a full battery charge.

- (3)
- Charging time

_{c}is the charging time of the electric vehicle (unit: h); SOC

_{0}is the initial SOC of charging for electric vehicles; P

_{c}refers to the charging power of electric vehicle charging facilities (unit kW); E is the battery capacity of electric vehicles (kW·h).

- (4)
- Monte Carlo simulation charging probability prediction

## 4. Mathematical Model of Collaborative Optimization Construction of Community

#### 4.1. Objective Function

_{0}is the discount rate; y is the economic service life of the line, and the unit is: year; L is the cost of line loss in newly built communities, unit: ten thousand yuan/year.

_{k}is the length of line k, and the unit is km; f

_{Dk}is the construction cost when the cross-sectional area of line k is D

_{k}, and the unit is yuan/km; N

_{k}indicates a new line collection.

_{k}is the active power flowing on line k, and the unit is kW; ${Q}_{k}$ is the reactive power flowing on line k, and the unit is kVar; U is voltage, unit: kV; ${g}_{{D}_{k}}$ is the resistance per unit length when the cross-sectional area of line k is ${D}_{k}$. The unit is Ω/km. τ is the maximum number of hours of load utilization per year, expressed in hours.

#### 4.2. Constraints

- (1)
- Distribution network line capacity constraints

- (2)
- Distribution network node voltage constraints

- (3)
- Constraints on charging demand

_{char}(i) is the charging load of the i-th EV charging node in the distribution network, and the unit is kW; C

_{char}(i) is the variable capacity of the i-th EV charging node in the distribution network, and the unit is kVA.

- (4)
- Capacity constraints of distribution network station area

_{norm}(i) is the i-th resident base node load of the distribution network, and the unit is kW; C

_{norm}(i) is the distribution capacity of the i-th communities base node in the distribution network, and the unit is kVA.

## 5. Prim and Single-Parent Genetic Algorithm

#### 5.1. Prime Algorithm

#### 5.2. Single-Parent Genetic Algorithm

## 6. Analysis of Examples

^{2}and 1298 households and adopts 1:1 parking space allocation of underground parking lots. Two types of cables, YJV22-3*150 and YJV22-3*300, are used to construct the lines in the community area. The related parameters and construction costs are shown in Table A2 of Appendix A.

#### 6.1. Forecast of Electric Vehicle Ownership in Newly Built Communities

#### 6.2. Prediction of Base Load and Electric Vehicle Charging Load in Newly Built Communities

^{2}, the coefficient of area demand was 0.17, and the annual growth rate of the base load was 1%. The characteristics of residential conventional power load were shown in Figure 10. It is easier for this urban community to reach the saturation value of accepting the capacity of electric vehicles on summer working days. Therefore, summer working days in Figure 10 are used to calculate the peak base load of each node in this residential area, and the required capacity of each node is planned once. The results are shown in Table A3 of Appendix A. Taking 2022, 2025, 2030, 2035 and 2040 as the years to be planned, the charging power of electric vehicles is set as 7 kW, and the charging load of electric vehicles in the newly built communities is predicted. The results are shown in Figure 11.

#### 6.3. Planning Scheme of Charging Facilities in Community

#### 6.4. Summary

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Year | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|

Private car ownership/thousand | 1033 | 1277 | 1572 | 1933 | 2241 | 2564 | 2853 | 3158 | 3499 |

Private car growth rate/% | 32.80 | 23.60 | 23.10 | 23.00 | 15.90 | 14.4 | 15.00 | 10.70 | 10.80 |

Line Model | Cross-Sectional Area/mm^{2} | Resistivity /Ω/km | Reactance /Ω/km | Ampacity /A | Line Construction Costs/Thousand/km |
---|---|---|---|---|---|

YJV22-3*150 | 150 | 0.12 | 0.103 | 358 | 900 |

YJV22-3*300 | 300 | 0.305 | 0.089 | 540 | 1500 |

Number | Number of Households | Building Area/m^{2} | Load Density W/m^{2} | Area Requirement Factor | Annual Growth Rate | Average Daily Load/kW | Peak Load/kW | Capacity Configuration/kVA |
---|---|---|---|---|---|---|---|---|

1# | 120 | 15,600 | 40 | 0.17 | 1% | 126.89 | 192.25 | 315 |

2# | 66 | 8580 | 40 | 0.17 | 1% | 69.79 | 105.74 | 160 |

3# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |

4# | 144 | 18,720 | 40 | 0.17 | 1% | 152.26 | 230.70 | 400 |

5# | 44 | 6600 | 40 | 0.17 | 1% | 53.68 | 81.34 | 125 |

6# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |

7# | 72 | 7920 | 40 | 0.17 | 1% | 64.42 | 97.61 | 160 |

8# | 72 | 7920 | 40 | 0.17 | 1% | 64.42 | 97.61 | 160 |

9# | 44 | 5720 | 40 | 0.17 | 1% | 46.53 | 70.49 | 125 |

10# | 120 | 15,600 | 40 | 0.17 | 1% | 126.89 | 192.25 | 315 |

11# | 180 | 27,000 | 40 | 0.17 | 1% | 219.61 | 332.75 | 500 |

12# | 174 | 26,100 | 40 | 0.17 | 1% | 212.29 | 321.65 | 500 |

13# | 174 | 26,100 | 40 | 0.17 | 1% | 212.29 | 321.65 | 500 |

Total | 1442.12 | 2185.04 | 3510 |

Electrovalency/kWh | YJV22-3*150 Construction Costs/Thousand/km | YJV22-3*300 Construction Costs/Thousand/km | Operating Life/Years | Discount Rate | Maximum Load Operating Hours per Year/Hour |
---|---|---|---|---|---|

0.5 | 900 | 1500 | 40 | 0.1 | 3000 |

Average Annual Cost of Network Loss/Thousand | YJV22-3*150 Line Construction Length/m | YJV22-3*300 Line Construction Length/m | Average Annual Cost of Line Construction/Thousand |
---|---|---|---|

104 | 3041.23 | 249.71 | 318.2 |

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Number | Number of Households | Building Area/m ^{2} | Number | Number of Households | Building Area/m^{2} |
---|---|---|---|---|---|

1# | 120 | 15,600 | 8# | 72 | 7920 |

2# | 66 | 8580 | 9# | 44 | 5720 |

3# | 44 | 5720 | 10# | 120 | 15,600 |

4# | 144 | 18,720 | 11# | 180 | 27,000 |

5# | 44 | 6600 | 12# | 174 | 26,100 |

6# | 44 | 5720 | 13# | 174 | 26,100 |

7# | 72 | 7920 |

Year | Number of Charging Facilities to Build/Unit | Peak Load (Weekdays)/kW | Node Capacity Configuration/kVA |
---|---|---|---|

2022 | 2 | 14 | 315 |

2023 | 5 | 21 | 315 |

2024 | 8 | 35 | 315 |

2025 | 12 | 49 | 315 |

2026 | 18 | 70 | 315 |

2027 | 26 | 98 | 315 |

2028 | 35 | 133 | 315 |

2029 | 48 | 182 | 315 |

2030 | 62 | 238 | 315 |

2031 | 75 | 287 | 630 |

2032 | 86 | 322 | 630 |

2033 | 95 | 357 | 630 |

2034 | 102 | 385 | 630 |

2035 | 107 | 406 | 630 |

2036 | 111 | 420 | 630 |

2037 | 114 | 427 | 630 |

2038 | 117 | 441 | 630 |

2039 | 119 | 448 | 630 |

2040 | 119 | 448 | 630 |

**Table 3.**Peak load density of the power supply grid in the saturation year (2040) for community areas.

Grid Number | Load Density (kW/km^{2}) | Grid Number | Load Density (kW/km^{2}) |
---|---|---|---|

1 | 10,419.44 | 6 | 2563.33 |

2 | 1687.00 | 7 | 4781.78 |

3 | 783.22 | 8 | 0.00 |

4 | 3573.89 | 9 | 7113.89 |

5 | 2563.33 | 10 | 2136.11 |

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## Share and Cite

**MDPI and ACS Style**

Diao, X.-H.; Zhang, J.; Wang, R.-Y.; Jia, J.-W.; Chang, Z.-L.; Li, B.; Zhao, X.
Collaborative Planning of Community Charging Facilities and Distribution Networks. *World Electr. Veh. J.* **2023**, *14*, 143.
https://doi.org/10.3390/wevj14060143

**AMA Style**

Diao X-H, Zhang J, Wang R-Y, Jia J-W, Chang Z-L, Li B, Zhao X.
Collaborative Planning of Community Charging Facilities and Distribution Networks. *World Electric Vehicle Journal*. 2023; 14(6):143.
https://doi.org/10.3390/wevj14060143

**Chicago/Turabian Style**

Diao, Xiao-Hong, Jing Zhang, Rui-Yu Wang, Jiang-Wei Jia, Zhi-Liang Chang, Bin Li, and Xuan Zhao.
2023. "Collaborative Planning of Community Charging Facilities and Distribution Networks" *World Electric Vehicle Journal* 14, no. 6: 143.
https://doi.org/10.3390/wevj14060143