# Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles

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## Abstract

**:**

## 1. Introduction

## 2. Mobile Energy Storage Capacity of EVs

_{k}. Electric vehicles can park or move freely from one zone to another, and their movement behavior is characterized by the PGR of each zone. Considering that vehicles are moving in and out of the area all the time, the total number of electric vehicles in the area varies at any given time [11]. The process of calculating the parking production rate is depicted in Figure 1.

#### 2.1. Parking Generation Rate

_{P}) between the regional parking demand obtained by PGR model and the actual number of electric vehicles parked in the current area is calculated. If D

_{P}> 0, vehicles on the move are randomly selected to park in the area; if D

_{P}< 0, vehicles whose state of charge (SOC) meet the departure requirements will be randomly selected to leave. Then, the status signs of each EV are updated to make them conform to the travel characteristics of each region. The driving record of an EV can be obtained from its status mark. The process of updating the moving state of EVs on the basis of PGR is shown in Figure 2.

#### 2.2. Mobile Energy Storage Model of EV

- (1)
- ${F}_{\mathrm{area}}\left(n\right)$ is the area status of the n-th EV, and its calculation formula is

- (2)
- ${S}_{\mathrm{OC}}$ is the charge state of the battery.

- (3)
- Q is the battery capacity, in units of kWh.

- (4)
- $\overline{v}$ is the average speed of the EV, in units of km/h.

- (5)
- ${F}_{\mathrm{P}}$ is the parking sign of the electric vehicle, and its calculation formula is

- (6)
- ${W}_{100}$ is the power consumption per 100 km, in units of kWh.

- (7)
- ${S}_{\mathrm{max}}$ and ${S}_{\mathrm{min}}$ are the upper and lower limits of ${S}_{\mathrm{OC}}$ when the EV is connected to the power grid.

## 3. Load Transfer Path Search

#### 3.1. Power Supply Path Search Algorithm inside a System

_{n}

_{,1}, ST

_{n}

_{,2}, …, ST

_{n,m}, and the tie lines between each station on layer N − 1 are W

_{n}

_{,1}, W

_{n}

_{,2}, …, W

_{n,k}. At this point, inter-station traversing is stopped, the tie lines are taken as starting points, and the BFS is initiated inside the station. The searched outgoing lines of layer N in W

_{n + 1}are saved. If all stations in layer N complete intra-station traversal and S outgoing lines are found, the contact relationship between layer N and layer N + 1 is determined, and the starting points of the next round of the inter-station search can be determined accordingly. The intra-station traversal is continued for layer N + 1, before being transferred to the inter-station traversal to layer N + 2. The iteration continues until all nodes advance to the ultimate layer.

#### 3.2. Power Supply Path Search Strategy between Systems

_{i,j}values is taken as the final similarity between node i and node j.

_{i}is the word number of the i-th line, A

_{j}is the word number with the same description for the j-th outgoing line, $\alpha $ is the minimum, and R is the set of all lines in another system.

_{PS}. If the fault occurs in the main network, the affected node set in the main network N

_{ZS}is searched from the faulty equipment, and then the corresponding node set N

_{ZP}in the corresponding distribution network is obtained via the edge matching method. Starting from N

_{ZP}, the influence range of N

_{PS}in the distribution network is sought in the direction of the active power stream.

_{PS}

_{,}and taking the transformer as the end point, the power supply path in the distribution network is searched against the active power flow to obtain the set of possible power supply nodes, and then the set of nodes in the main network N

_{PZ}is obtained via the edge matching method. Starting with N

_{PZ}and taking transformers as the end point, the power supply paths in the main network are obtained by searching against the active power flow. The search strategy is illustrated in Figure 5.

## 4. Path Evaluation Algorithm

#### 4.1. Index Set

- Load rate change of main transformer:

_{NL}is the apparent power of the main transformer with extra load, and S

_{L}is the apparent power of the main transformer before the fault [15].

- Remote node voltage deviation:

- Current load rate change:

- Switch operation times:

- Additional network loss rate:

_{l}is the rated transmission capacity.

- EV participation rate:

#### 4.2. Optimal Combination Weighting Method

#### 4.2.1. Subjective Weight

#### 4.2.2. Objective Weight

_{ij}is the normalized value of each index data point, n is the evaluation index scheme number, and k is the evaluation index number.

#### 4.2.3. Optimal Combination Weighting

## 5. Case Studies

#### 5.1. Mobility Characteristics of EV

- Non-working days

- 2.
- Weekdays

#### 5.2. Path Search and Evaluation

## 6. Conclusions

- During the working day, the load in the industrial area is high, and the EV discharge capacity in this area is also high. Therefore, during load transfer, part of the load can be transferred to the EV cluster node. The calculation results show that this scheme has a higher score and can be recommended to the dispatchers first.
- In the daytime of non-working days, the load in the commercial area is high, and the discharge capacity of EVs in this area is low; thus, part of the load can be transferred to other transformers or EV cluster nodes in other areas.
- In the evening, the load in residential areas and the discharge capacity of EVs are high; therefore, part of the load can be transferred to the EV cluster node locally to achieve the purpose of peak shaving and avoid the transfer of load to other areas, resulting in network congestion.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Example diagram and load data: (

**a**) diagram of calculation example; (

**b**) typical load curve of each area.

**Figure 8.**Electric vehicle discharge capacity: (

**a**) non-working day EV capacity; (

**b**) weekday EV capacity.

Region | Node |
---|---|

Industry | 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 44 45 46 47 48 49 50 |

Business | 9 10 11 39 40 41 42 43 51 55 56 57 |

Resident | 7 8 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 52 53 54 |

No. | Path | N1 | N2 | N3 | N4 | N5 | N6 | Evaluation Results |
---|---|---|---|---|---|---|---|---|

1 | [50, 49, 13, 15, 1] | 1.00 | 0 | 0 | 0 | 0 | 0 | 0.33 |

2 | [50, 49, 13, 15, 3] | 0.97 | 0.35 | 0.79 | 0.00 | 0.95 | 0 | 0.5 |

No. | Path | N1 | N2 | N3 | N4 | N5 | N6 | Evaluation Results |
---|---|---|---|---|---|---|---|---|

1 | [50, 49, 13, 9] | 0.96 | 1 | 0.9 | 1 | 0.98 | 0.05 | 0.81 |

2 | [50, 49, 13, 15, 1] | 1 | 0 | 0 | 0 | 0 | 0 | 0.16 |

3 | [50, 49, 13, 15, 3] | 0.97 | 0.35 | 0.79 | 0 | 0.95 | 0 | 0.47 |

4 | [50, 51, 10, 9] | 0.96 | 0.55 | 1 | 1 | 0.99 | 0.05 | 0.74 |

5 | [50, 51, 10, 12, 9] | 0 | 0.07 | 0.94 | 0 | 1 | 1 | 0.46 |

No. | Node | Working Day | Weekday Night | Holiday | Holiday Night | Average |
---|---|---|---|---|---|---|

1 | [50, 49, 13, 9] | 0.75 | 0.73 | 0.81 | 0.79 | 0.77 |

2 | [50, 49, 13, 15, 1] | 0.33 | 0.33 | 0.28 | 0.41 | 0.34 |

3 | [50, 49, 13, 15, 3] | 0.3 | 0.24 | 0.42 | 0.49 | 0.36 |

4 | [50, 51, 10, 9] | 0.72 | 0.7 | 0.77 | 0.83 | 0.76 |

5 | [50, 51, 10, 12, 9] | 0.48 | 0.36 | 0.33 | 0.31 | 0.37 |

No. | Node | N1 | N2 | N3 | N4 | N5 | N6 | Evaluation Results |
---|---|---|---|---|---|---|---|---|

1 | [26, 25, 27] | 1 | 0.02 | 0.22 | 0 | 0.21 | 0 | 0.3 |

2 | [24, 23] | 0 | 1 | 1 | 1 | 1 | 0 | 0.86 |

5 | [24, 25, 27] | 0.58 | 0 | 0 | 0 | 0 | 0 | 0.12 |

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**MDPI and ACS Style**

Mao, D.; Huang, X.; Chen, Z.; Lv, Y.; Tian, J.; Yang, Z.
Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles. *World Electr. Veh. J.* **2021**, *12*, 210.
https://doi.org/10.3390/wevj12040210

**AMA Style**

Mao D, Huang X, Chen Z, Lv Y, Tian J, Yang Z.
Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles. *World Electric Vehicle Journal*. 2021; 12(4):210.
https://doi.org/10.3390/wevj12040210

**Chicago/Turabian Style**

Mao, Dongyu, Xueliang Huang, Zhong Chen, Yang Lv, Jiang Tian, and Zexin Yang.
2021. "Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles" *World Electric Vehicle Journal* 12, no. 4: 210.
https://doi.org/10.3390/wevj12040210